Software composition using graph types, graph, and agents6976020Abstract An environment for composing software permits the separation of control functions from information about the context in which the control functions operate. The software composition environment is used to make a system which will translate XML documents into models and vice-versa. The translation system is used to translate an XML document having one DTD into an XML document having another DTD by translating the first XML document into a model representing the semantics of the XML document and translating the model into the second XML document (2005). The system for translating XML documents into models employs a general technique for translating any XML documents into a mirror model (2107) that reflects the structure of the XML document and a general technique of using tag pattern models (2109) to obtain information from one model and using it to make or modify another model. In the system for translating XML document, the tag pattern models are used to translate mirror models into semantic models and vice-versa. Claims 1. Apparatus for controlling a processor that has access to a storage device, Description BACKGROUND OF THE INVENTION
The model type for the taxonomy model thus has a subclass facet specifier for the subclass facet and an is a facet specifier for the is a facet. The relation specifier for the subclass facet specifies that the subclass relationship is transitive, non-reflexive, and non-symmetric. The fact that the relationship is transitive means that if entity A is a subclass of entity B and entity C is a subclass of entity B, then entity C is a subclass of entity A, or in terms of FIG. 1, that parkas 117 is a subclass of clothing 111. The fact that the subclass relationship is non-reflexive means that an entity cannot be a subclass of itself (which is why there are no edges of subclass graph 107 connecting an entity to itself). The fact that the relationship is non-symmetric means that if entity B is a subclass of entity A, entity A cannot be a subclass of entity B or in terms of FIG. 1, if parkas 117 is a subclass of outerwear 113, outerwear 113 cannot be a subclass of parkas 117. The relation specifier for the is a facet specifies that the is a relationship is transitive, reflexive, and non-symmetric. Thus, as shown in FIG. 1, parkas 117 is itself as well as outerwear and clothing, but if parkas are outerwear, then outerwear cannot be (just) parkas. The relation specifiers are used to define procedures for adding concepts to models belonging to the class. For instance, if new concepts, say swimwear, bathing suits, and wetsuits are added to the model of FIG. 1, with swimwear being a subclass of clothing and bathing suits and wetsuits being subclasses of swimwear, the relation specifiers will ensure that there are edges in the subclass facet connecting clothing to swimwear and swimwear to bathing suits and wetsuits, but no edges in the subclass facet connecting clothing to wetsuits or bathing suits to wetsuits, and will similarly ensure that there are edges in the is a facet connecting each of the new concepts to itself and wetsuits and bathing suits to swimwear and swimwear to clothing, but no edges connecting wetsuits and bathing suits to clothing and none connecting wetsuits and bathing suits to each other. One example of a propagator for a taxonomy is a subclass display propagator that displays all of the subclasses belonging to a class. The subclass display propagator works by simply following the subclass facet beginning at the specified class. Thus, if the class is clothing, the display propagator will display outerwear 113, parkas 117, raingear 119, footwear 115, sandals 121, and insulated boots 123. Another example is an is a display propagator that displays the concepts that the specified concept belongs to. This propagator simply follows the is a facet beginning at the specified concept. Thus, for sandals 121, it will display sandals 121, footwear 115, and clothing 111. Relating Concepts to the World: FIG. 3 In order to be useful, the cards in a library card catalog relate the concepts used in the catalog to books in the library. The same is true with concepts organized by models. In order for the concepts to be useful, they must be related to entities that are examples of the concepts. In the invention, an entity that is or may be an example of a concept is termed an instance, and an instance that is an example of a concept is termed an instance of the concept. It should be pointed out here that one of the things which may be an example of a concept is a model, and thus, an instance may be a model. Using models as instances in other models is one way of making complex models. All of the instances available to a system in which the invention is implemented is termed the world of the system. In general, one makes a model to deal with a given area from several aspects, and this area is termed the model's subject. For example, the subject of model 209 is clothing and all of the instances of its concepts represent items of clothing. One thus makes a model for a subject and then relates the model to instances in the world that are relevant to the model's subject. The instances in the world that are relevant to a given subject are termed the subject's collection. FIG. 3 shows how concepts are related to instances in a preferred embodiment. FIG. 3 shows a set 301 of instances representing objects accessible to the system upon which model 209 is being used. This set 301 is termed herein the world of the model. The subject of model 209 is clothing; in FIG. 3, instances belonging to clothing's collection are surrounded by a curve, as shown at 306. Thus, in FIG. 3, model 209 is being applied to world 301, but the instances with which it is actually concerned belong to clothing collection 306. Item instances in clothing collection 306 are consequently termed clothing instances 307. The instances in clothing collection 306 with which model 209 is concerned all represent items of clothing or agents, as will be explained below; however, other instances in clothing collection 306 may represent models. Of course, more than one set of concepts may apply to a subject or a world and a given set of concepts may be applied to different subjects or worlds. There are two kinds of instances in world 301: item instances 303, which represent items, including other models, that may be related to concepts, and agent instances 304, which represent programs that are executed by models in response to the occurrence of events such as the addition of a concept to the model or a request by a user to view items belonging to a given concept. While the program represented by an agent may be any program at all, the program executes in the context of the model and can thus take advantage of the model's facets and propagators. In effect, the operations defined for the model are available to agents in the same fashion that programs belonging to run-time libraries are available to application programs. The mechanism by which an item instance 303 or an agent instance 304 is related to a concept is an instance facet 309. There is an instance facet 309 for each instance that is related to a given concept. Thus, instance facets relate clothing instances 307(b and c) to concept 121. Of course, an instance may have instance facets connecting it to more than one concept and even to concepts belonging to different models. Generally, the item represented by an instance has another representation, termed an object, in the computer system. What kind of object an instance represents will depend on the application for which the invention is being used. For example, the clothing instances might represent database identifiers of rows describing products in a database table describing a clothing company's products or they might be URLs of WEB pages describing the products. Propagators may work on instances as well as concepts. For example, a propagator may be defined for the taxonomy model type which retrieves all of the instances associated with a concept and its subclasses. It does so by first following the instance facets for the concept and retrieving all of the concept's instances. Then it follows subclass facet 107 from the concept to its subclasses, their subclasses, and so on down to concepts which have no subclasses. At each concept, the propagator retrieves the instances associated with the concept. Thus, in FIG. 3, when the propagator is applied to concept 115, it will retrieve the clothing instances 307 labeled a,b,c,d in collection 306. One agent instance is shown in collection 306: the instance for refinement agent 308. Refinement agent 308 is executed when a concept representing a new subclass is added to model 209. For example, in model 209 as shown in FIG. 1, the concept footwear 115 has two subclasses: sandals 121 and insulated boots 123. Instances which belong to neither of those subclasses belong to footwear. One such instance, 307(a), is shown in FIG. 3. The instance represents gardening clogs. Now, the user of the model is planning to sell more kinds of clogs and consequently decides to add the concept clogs as a subclass of footwear. When that is done, instance 307(a) should become an instance of clogs rather than an instance of footwear. This process of moving an instance into the proper subclass concept is termed refinement, and refinement agent instance 308 automatically does refinement whenever a subclass concept is added to model 209. In FIG. 3, refinement agent instance 308 is shown attached to clothing concept 111 and to footwear concept 115. Clothing concept 111 is the broadest concept in the model and is termed the root concept of the model. Of course, every model of type taxonomy has a root concept. In models of the taxonomy type, an agent attached to a concept propagates along subclass facet 107; thus, any concept which is a subclass inherits the agent. Consequently, each concept in model 209 has its own copy of refinement agent instance 308. In FIG. 3, only the copies for clothing 111 and footwear 115 are shown. Since each concept has its own copy of refinement agent instance 308, execution of the agents can be done in parallel. When the user adds the new subclass clogs to footwear 115, that event causes refinement agent instance 308(k) to execute. The program follows the subclass facet to the new subclass concept clogs and examines it to determine whether any of the item instances that are related to it are also related to footwear 115. One such item instance, garden clogs, is, and the program rearranges the instance facets 309 so that there is now an instance facet relating clogs to garden clogs, but no longer an instance facet relating footwear to garden clogs. As can be seen from the foregoing, an agent, while user-defined, operates within the context of the environment provided by the model and takes advantages of the operations defined for the model's type. Representing Models, Concepts and Instances: FIG. 4 FIG. 4 shows at 401 how the representations of model types, models, concepts, and instances are structured in a preferred embodiment. In overview, as shown by the arrows in FIG. 4, each model definition 413 refers to a model type definition for its model type and to a set of node structures. Some of the node structures represent concepts belonging to the model and others represent instances of the concepts. Each concept node 425 refers to its model and each instance node 437 refers to the concepts the node is instances of. There may be many models of a given model type, a given model may have many concepts, a given concept may have many instances and a given instance may be an instance of many concepts. A model type definition may thus be located from any model definition of its type, a model definition may be located from any of its concepts, and a concept may be located from any of its instances. Continuing in more detail, model type definition 403 includes the model type's name 405, a description 407 of the model type, a facet specifier list 409 that specifies the kinds of facets that models of the type have, and a propagator list 411 that specifies the propagators for models of the type. Model definition 413 includes the model's name and description at 415 and 417, a list 419 of the concept and instance nodes in the model, a facet list 421 showing how the model's nodes are related by each facet of the model, and a model type name 423, which refers back to the model type definition 403 for the model. Concept node 425 includes the concept's name and description at 427 and 429, a property list 431, which is a list of user-defined properties of the concept, and attribute list 433, which is a list of attributes for the concept. Each attribute specifies the name of a facet to which the concept node belongs and the name of the node which is the next neighbor of the concept node in the facet. The facets, and correspondingly, the attributes may be subdivided into model facets, which specify facets whose vertices are made up only of concepts of the model, and instance facets, which specify facets connecting concepts and instances. What kinds of model facets a model has is determined by its model type; in a preferred embodiment, there are three kinds of instance facets that run from the concept to an instance:
Exhibitor facets are used to deal with concepts like color. A blue clog, for example, exhibits the property of being blue and would therefore be connected to a concept representing the color blue by an exhibitor facet. Owning model 435, finally, refers to model definition 413 for the model the concept belongs to. Instance node 439, finally, has an instance name 439, an instance description 441, and a property list 443 for the instance. Included in property list 443 is referent 445, which specifies how to locate the object represented by instance node 439. What the referent is depends on what kind of object the instance node represents. For example, if the instance node represents a Web page, the referent will be the page's URL; if it represents an agent, it may be a pathname for the agent's code; if it represents another model, the referent will be the model's name. Attribute list 447, finally, specifies the instance facets that run from the instance to the concepts it belongs to. There is one such facet corresponding to each of the instance facets running from the concept to the instance. Each of these facets is termed the dual of the corresponding facet. Thus, the item of facet is the dual of the item facet; exhibitor of is the dual of the exhibitor facet; and action of is the dual of the action facet. Applying all of the foregoing to concept 115 of model 209, we see that concept node 425 for that concept has model attributes for the subclass facet for concepts 121 and 123 and for the is a facet for itself and for concept 111, an item instance attribute for clothing instance 307(a), and an action instance attribute for refinement agent instance 308(k). Instance node 437 for clothing instance 307(a) has an item of instance attribute for concept 115 and the instance node for refinement agent instance 308(k) has an action of attribute for concept 115. In a preferred embodiment, the structures that make up the components of a model are all linked by name, and hash functions and hash tables are used to relate names in the structures to the locations of the structures in memory. For example, to find a concept instance, the preferred embodiment takes the name and presents it to a hash function, which hashes the name to obtain an index of an entry in a hash table and uses the index to find the entry for the name in the hash table; that entry contains a pointer to the location of the concept instance. In other embodiments, other techniques such as pointers might be used to link the components of the structures 401 that represent a model. A System that Uses Models to Organize Information: FIG. 5 FIG. 5 is an overview of a system 501 that uses models to organize information. The system, called Ariadne, has three major components:
Server 509 may be implemented on any kind of computer system, and viewers 507 may be monitors, Web browsers, PC's or other systems that have either local or remote access to the computer system upon which server 509 is implemented. As shown in FIG. 5, the outside systems accessed via ERIS 505 may include relational database systems, with the objects being records or queries, Web servers, with the objects being Web pages, email systems, with the objects being email messages, and systems that use XML as their interface to other systems. The viewers 507 and the components of ERIS 505 interact with the model types, models, agents, views, and instances by way of interfaces 511 defined using Interface Definition Language (IDL). An example of how system 501 functions is the following: A user of a viewer 507(i) is interacting with clothing model 209 via a graphical user interface and wishes to see all of the instances of footwear that are currently available in collection 306 of clothing model 209. The user specifies footwear concept 115 and a "display instances" operation. This operation specification arrives via IDL 511 in server 509, and the propagator for the taxonomy model type which retrieves instances retrieves the instances that are related to concepts footwear 115, sandals 121, and insulated boots 103. Ariadne server 509 then typically makes a list of the instances represented by the objects for display in viewer 507(i). If the user of the viewer selects one or more of the instances from the list, Ariadne server 509 provides the referents 445 for the objects represented by the selected instances to ERIS 505, which retrieves the objects referred to by the referents and returns them to Ariadne, which then makes a display using the retrieved objects and sends the display to viewer 507(i). For example, if the clothing instances represent Web pages containing catalog descriptions of the items, when the user of viewer 501 selects an item from the list, Ariadne server 509 will provide the URL for the item's web page to ERIS 505, ERIS 505 will fetch the Web pages, and Ariadne 509 will provide them to viewer 507(i). Ariadne server 509 also provides views 513 which permit a user at viewer 507(i) to define, examine, and modify models. The user interfaces for doing so will be explained in detail later on. Details of Views 513: FIG. 6 FIG. 6 shows details of the implementation of views 513 in a preferred embodiment. Models may have multiple views and views may have multiple presentations. The implementation supports different presentations of the same model concurrently, collaborative modeling and real time knowledge sharing, and independent yet sharable knowledge explorations. In Ariadne, views are implemented in a subsystem known as Calyx. Calyx 601 is a CORBA server which exports via IDL specifications an abstract interface for views. Calyx 601 could also be any other distributed middleware server (for example, proprietary RPCs or DCE or possibly DCOM). A view 603 is a collection of bins 605 of information about the target source: A model or a world. Bins hold information such as the current objects being shown, whether the attributes of an object along any given facet are expanded, what facet a bin is looking at, etc. The typical representation 601 of a view is a structure containing (among other things) a container of bins 605. All views and bins (as well as any other externally accessible resource) are referenced by opaque IDs which are presented to any viewer 607 logging into Ariadne. A viewer 607 is a active object through which the abstract information is displayed. Each viewer takes the abstract information maintained by Calyx in a view 601 and presents it in a manner which is consistent with the interface requirements and look and feel of a given GUI. For example, a taxonomy might be represented by a graph, an outline, or simply as an indented list of text and the viewer will use whatever resources are provided by its GUI to make the representation. For example, an outline might be presented by a Java Swing tree widget or an MFC tree widget. As may be seen from the dashed lines in FIG. 6, a view 601 may be shared by a number of viewers 607. Calyx ensures that all viewers 607 that use a given view 6021(i) are synchronized to the most recent changes in view 602(i). When a viewer 607(j) requests Calyx to update or otherwise change part of the view (say, expand a node in a bin), Calyx performs this operation for viewer 607(i) and then asynchronously sends the update information to all other viewers actively using the view in question. These requests by Calyx to such viewers are client requests to server portions in those viewers. Hence, Calyx is a client and the viewers must implement a server interface for these asynchronous updates. Calyx also supports (via the model and world infrastructure) various operations on the contents of bins. Specifically, various set operations (union, set difference, intersection, etc.) may be applied to arbitrary sets of bins. Additional operations may be defined by the user. The effect of the set operations is to apply the operation on the sets of information represented in the bin to produce a new bin (called a composition bin) with the computed resulting information. This is then propagated to all connected viewers. Further, bins may be combined in this way to create constraint networks of composition bins. If any bin in the network is changed (manually or via automated updates) the effect is propagated throughout the entire affected subnetwork in which the bin is connected. These propagated results are sent to all viewiers via the asynchronous operations described above. Separation of Levels of Information in the Implementation: FIGS. 3-6 An important characteristic of Ariadne is the manner in which complexity is reduced and flexibility increased by separating various levels of information from each other. One of these is the separation of model types from models, as seen in the separation of model type definition 403 from model definition 413 in FIG. 4. Another is the separation of models from instances, as seen in FIGS. 3 and 4; this permits multiple models to be built independently of each other and yet work over the same world. It also permits models to be reused in different worlds. Yet another is the separation of an instance from the object that it represents, so that the instance serves as a proxy for the object, as seen in with regard to referent property 445 in FIG. 4 and the use of ERIS interface 505 to retrieve objects represented by referents from a number of different information sources 503. Then there is the agent/model separation: agents run in the context of models, but they are defined in terms of model types, not the individual models. For example, the refine agent will work with any model that has the taxonomy type. Finally, as seen in FIGS. 5 and 6, views 601 are separated from models and worlds and viewers 607 are separated from views 601. The User Interface for Building, Modifying, and Displaying Models: FIGS. 7-12 A particular advantage of model types is that they greatly simplify the construction and modification of models. They do so because the part of Ariadne which constructs models can use the information in the model type to automatically place concepts in the proper facets and in the proper locations in those facets and to propagate information provided by the user to the concepts that require it. One example of such propagation is the propagation of the refinement agent from the root of a model of the taxonomy type via the subclass facet to all of the concepts in the model. FIG. 7 shows the dialog box 701 used in a preferred embodiment to create a new model. At 703 there appears a list of the presently-available model types; the user has selected simple taxonomy, indicating that the new model is to have the simple taxonomy model type; in the name box, the user has input "usr:Clothing", indicating that that is to be the name of the new model; at 709, the user may input the description. The result of these inputs is of course the construction of a model definition 413 for the new model, with model name 415 being "usr:Clothing" and model type name 423 being "Simple Taxonomy". List 705 gives an example of what can be done with models. In Ariadne, models themselves are instances in a model whose concepts are model types; one can thus simply select an already-made model from that model. In instance node 437 for an instance representing a model, referent 445 simply specifies the location of the model's model definition 413. The action model similarly treats agents as instances of a model whose concepts are the model types the agents are written for. FIG. 8 shows the dialog box 801 used to add a root concept to the subclasses facet of the new model "Clothing". At 803 would normally appear the concepts that are presently in the model; the field is empty, as the model as yet has no concepts. At 805, the user writes the name of the root concept, and as before, the user may also add a description. The result of these inputs is the creation of a concept node 425 with the name "Clothing" in field 427 and the model name "usr:Clothing" in field 435. Since "Clothing" is a root concept and there are no other nodes, the taxonomy type requires that there be as yet no subclass attributes in attribute list 433, but a single is a attribute for "Clothing" itself, and Ariadne automatically adds these to "Clothing"'s concept node 425. FIG. 9 shows the dialog box 901 used to add subclasses to an existing taxonomy model. Here, the model already has as subclasses of the root concept clothing the concepts accessories, apparel, swimmwear, and footwear, and further subclasses are being added to to the apparel subclass. At 903, the name apparel of the concept to which subclasses is being added appears; at 904, names of aready existing concepts appear; since only the first level of concepts have as yet been defined, the names are those of concepts at the same level as apparel; at 905, finally, is a field for adding a newly-made concept. A user may add a subclass either by selecting from among concepts listed in 904 or by using field 905 to add a newly-made subclass. For each newly-made subclass concept that is added, Ariadne creates a concept node 425 with the name of the concept at 427 and the name of the model at 435; for each concept being added as a subclass, Ariadne adds attributes in attribute list 433 for the is a facet specifying the new concept node itself and the concept node for the apparel concept. Ariadne further creates an attribute in attribute list 433 in the concept node for the apparel concept for the subclass facet which specifies the new concept node. Thus, when all of the subclasses have been added, they all belong to the subclass and is a facets in the manner required for the taxonomy model type. It should be pointed out here that if the user attempts to select one of the concepts listed in 904 to be added to apparel, Ariadne will determine from the model type that this is not possible in the taxonomy model type (in a taxonomy, a concept at one level of the taxonomy may not be a subclass of another concept at the same level) and will not add the concept but will indicate an error. In other embodiments, Ariadne may simply not display concepts that cannot be added to the concept selected at 903. FIG. 10 shows dialog box 1001 used to relate instances to a concept. Dialog box 1001 has the same form as dialog box 901, with area 903 containing the name of the concept to which the instances are being related, area 905 containing the names of instances that are available to be added to the concept, and field 1007, which can be used to add a newly-made instance. When a newly-made instance is added, an instance node 437 is created for the instance, with the instance's name at 439 and any description provided by the user at 441. For a newly-made or prevously-existing instance, an attribute for the item of facet that indicates the concept sweaters is added to the instance node's attribute list 447, and one for the item facet that indicates the instance is added to the concept node's attribute list 433. Similar dialog boxes are used to add agents and items that are exhibitors, with corresponding modifications in the attribute lists of the concept and instance nodes. Ariadne also has a copying interface that can be used to select instances belonging to a concept in one one model to become instances of a concept in another. The attribute lists 433 off the instance nodes for the copied instances are modified to add attributes for the instance of facet specifying the concept, and the other concept's attribute list 433 is modified to include attributes for the instance facet for the newly added instances. FIG. 11 shows how referent fields 445 are set in instance nodes 437. Window 111 has three subwindows: two show models that apply to the clothing world: "clothing categories" and "fabrics". Both models belong to the taxonomy type, and thus both can be displayed as outlines, as shown at 1103. The user wishes to add referents, in this case the URLs of Web pages that show the items represented by the instances, to the instances that belong to the concept "apparel". In terms of facets, that is all of the instances which have an is a relationship to "apparel", that is, the instances that are related to "apparel" and all of its subclasses. To perform this operation the user selects "apparel" in outline 1103; Ariadne then uses a propagator for the taxonomy model type to generate the list seen at 1107, which is the list of all of the instances that belong to "apparel" and its subclasses. To assign an URL to an instance, the user writes the URL opposite the instance in field 1109. The URL for a given instance goes into referent 445 in node 437 for the instance. FIG. 12 shows how Ariadne displays a model. Model 1201 is a taxonomy of the events handled by Ariadne. The boxes are the model's concepts and the arcs 1203 are the arcs of one of the facets, in this case, the is a facet. Selection of facets to be viewed is controlled by check box 1205; as seen there, model 1201 is to be displayed showing its concepts and its is a facets. More than one facet may be selected, in which case, the arcs for each selected facet are displayed simultaneously. Architecture of Model Types Facets and Facet Specifiers As could be seen from the taxonomy models explored in the foregoing, all models of a given type have the same kinds of facets. To define a model type, therefore, one defines its facets. Each facet of a model is defined by its corresponding facet specifier. All the facets available to a model are determined by the set of facet specifiers given in the model's corresponding model type definition (see below). Each facet specifier defines the set theoretic relational properties of the base relation captured by the facet and provides an interpretation of what the relation is intended to convey. This interpretation provides the meaning of the facet through semantic constraints on what concepts may be related by the facet and how the facet is mapped to facet descriptions in other model types. Hence, the set of facet specifiers defines the complete semantics of the model type at any given concept in an instance of that model type. Def: Facet-specifier. A facet specifier F is defined by a tuple: where
We will often refer to a facet specifier as simply a facet and let context ensure the sense of use. A facet name is a simple string (actually an interned symbol). Def: Facet Interpretation. A facet interpretation I is defined by a tuple: where
While a propagator may be null an interpretation can never be null, since a relation specifier can never be null as it must at least provide the basic set theoretic properties of the relation. Def: Relation Specifier. A relation specifier R is a tuple which describes the relation of the facet in terms of its set theoretic character and the local semantic constraints imposed on concepts connected to each other through the facet. where
Def: A semantic constraint specifier: A semantic constraint specifier for a relation R of a facet F in a model type MT is a set of sentences Γ which determines when two concepts in a model M of type MT can be connected along F and how that relationship is mapped relative to possibly connected models of other model types and to instances in the world. That is, Γ supply necessary conditions on R (and thus F): c1Rc2 Each sentence φ∈Γ is a statement with free variables over the concepts in the model M, possibly free variables over the concepts in a related model FM of some model type FMT, and possibly free variables over the instances in the world. These variables are implicitly bound to the specific values of their corresponding sets provided by the context of each specific constraint action. In addition any global predicates and operators defined for all model types can be used as can R and any RFM associated with the related FMT. There may be several such related model types involved in a semantic constraint. Such related models and their model types are often referred to as "feature models" and "feature model types" and the concepts in them as "features", though this terminology is a bit misleading (they do not have to be related via a "feature" facet—any facet may have such relationships, but for historical reasons we often use this terminology). Both universal and existential quantification are available for binding variables ranging over explicitly specified sets. Quantifiers can be mixed and nested to any level. Deeper sentences may refer to the quantifier variables of outer sentences with the expectation that any binding is properly maintained. Additionally a constraint may assert a condition to hold provided another condition holds. This supports actions which must be atomic with respect to the overall constraint. For example, if a concept C1 is added to the concept C2 in the subclasses facet of a model with a taxonomic model type having facets subclasses and superclasses, then the constraint for the facet can assert the dual relationship: C2 added to C1 in the superclasses facet. Language for Semantic Constraints Letting M be a model of model type MT and FM be a model of model type FMT and F be a facet defined in MT, then the following lexical elements are available for use in semantic constraint specifiers:
The syntax for sentences is standard s-expression forms, where any quantifier, operator, relation, and connector may define a clause. Additionally, since all of Γ are implied by a constraint, Γ can be represented as a single conjunctive expression (there is no need for an explicit set of sentences). Ex-1 Constraint: Suppose MT is a model type with a facet specifier F containing the following constraint:
Then for any two concepts c1 and c2 of a model M of type MT if c1Rc2 then either there is a related model of type FM with facet (and relation) Rfm for which there are features f1 and f2. in the features of c1 and c2 respectively, for which f1Rfmf2 in the related model or the cardinality of the feature set of c2 is larger than the cardinality of the feature set of c1 The second disjunct of the or-clause in this example illustrates a particularly interesting constraint between models of two model types. It induces a homomorphism between two such models with respect to the graphs of the two facets involved. Hence, this sort of constraint ensures that sets of models are constructed to ensure such homomorphisms and this can be relied upon by agents or other processing of the models involved. One obvious use of this is the standard technique of exploring and investigating questions concerning one structure by looking at one or more of its homomorphic images. In such a technique, the issues would typically already have been resolved for the images or the images would be significantly simpler to explore. This can be particularly useful in agent autoclassifying and configuration scenarios. Ex-2, Facet Specifier: If MT is a model type for a simple graph without edge constraints (a so called "weak" semantic model type) then the following simple facet specifier could capture the edge set of models of type MT:
The facet's name is adjacent-vertices, its interpretation specifies no propagators and inside the relation specifier of the interpretation, no semantic constraints are given and the relation's properties are the singleton symmetric (standard character for simple graphs). Ex-3, Facet Specifier: If MT is a simple taxonomic model type then the following facet specifiers could capture simple notions of subclasses and features (given in s-expr clause form):
The facets are subclasses and features. The subclasses facet has a simple constraint requiring the addition of some new feature(s) for a subclass to be legal and a null propagator. The features facet has a null constraint but designates a propagator. The purpose of the propagator on features is to ensure that features of concepts of models of type MT obey the expected standard class based inheritance behavior for concept features (or characteristics). Propagators and Propagation Specifiers As noted earlier, a propagator provides a degree of expected behavior for all models of the model type containing the propagator's specification. Propagation specifiers define what and how values of attributes of models are moved, i.e., propagated, between concepts—both within the model and between concepts in related models. Def: Propagation Specifier. A propagation specifier PS is a tuple which describes an expected intrinsic piece of behavior for information movement between selected attributes along a path in a given facet graph for any model whose model type contains the specifier. where
While Ai and Aj may be different attributes, the most typical case is where they are the same attribute. The along facet F controls whether propagation is one step or continues until all concepts along the facet from the starting concept have been visited. If the relation of F is transitive, then propagation continues for all concepts in the potential path, otherwise propagation stops after the first step. In many cases, the global propagation semantics provided by propagation specifiers may need to be supplemented with context specific aspects. This is accommodated by providing two predefined properties for concepts in models of any type. These are,
On a propagation event, if the propagator involved has a prepropagation action, then the corresponding function is called on the value of the from attribute before propagation; if the propagator has a postpropagation action, then the corresponding function is called on the updated value of the to attribute. Ex-4: Suppose MT is a typical taxonomic model type including the facets superclasses and features, with the facet interpretation for features designating the following propagation specifier (also included in MT's definition): We also presume the typical case that the relation of superclasses is transitive.
Then any access designating features on a concept c in a model of type MT will obtain all the features directly attributed to c and any features in any superclass of c, i.e., the result is standard class based inheritance. Ex-5, Propagation Specifier: Suppose MT is as above in example 1. All model types have the predefined attribute instances (and a simple predefined facet specifier for this) and so MT has this. Assume the typical further condition that MT includes a facet specifier subclasses whose relation is also transitive and that instances' interpretation designates the following propagation specifier (also included in MT):
Then any access designating instances on a concept c in a model of type MT will obtain all the instances directly connected to c and any instances of any subclass of c, i.e., the result is standard class based instance set covering. Ex-6, Propagation Specifier: Suppose MT is some example of a causal network model type. Let MT have facets causes, effects, and happened and assume that the facet interpretation of happened designates the following propagation specifier which is also defined in MT:
If effects are transitive (each effect is a cause for something else), then any change to happened at a concept c in a model of type MT, where the value is greater than zero, will "fire" all the causes along the causal chains connected to c whose values are not the same as the value supplied. Model Type Definition We are now in a position to give the definition of a model type. A model type definition requires the following basic set of information:
The last point can be made implicit. Any currently extent model types are available for use in semantic constraint specifications of facet specifiers of the model type. We assume this scenario. Def: Model Type: A model type MT is defined by a tuple of sets which collectively describe the complete semantics of the model type: where
The Pj and Fk are specified per the definitions and descriptions covered in the relevant sections given earlier. This completes the definition of model type and this definition allows the description of the various styles of models mentioned earlier. We give some examples here to illustrate the technique for capturing a style with the machinery. All the examples are given in s-expression clausal form. Ex-7, Model Type:One of the simplest model types possible is that of the basic simple graph from graph theory. The following model type definition provides this most basic structure:
While not explicitly specified, there are also attributes and facets for instances and exhibitors (as required by the definition of model type). Note that there is also no need to specify attributes which have explicit facet specifiers, as these imply the corresponding attributes. Ex-8, Model Type: A somewhat more interesting example is that of a very simple taxonomy. This style of model is the one where there are no "features" and subclassing proceeds essentially by fiat.
Note that if we had decided to use a "superclasses" facet instead (or perhaps in addition to) the is-a facet the relational character would be slightly different: it would specify nonreflexive: a rose is a rose is a rose, but a rose is not a superclass of itself. As pointed out in the example introduction, there are no semantic constraints on any of the facets so what is or isn't a legal subclass or instance is left completely up to the modeler when building a specific instance of simple-taxonomy. Lastly note the propagator. It is the same as that given in Ex-5 and it is designated by facet instances. This means that whenever the facet instances is accessed at a concept c, the propagator instances-of will run in order to obtain the correct value for instances at c. When the propagator runs it will first get the instances directly attached to c, then it will move along the subclasses facet to all the immediate subclasses of c and get the instances of each of these. Since subclasses's relation is defined to be transitive, the propagator will then recurse. We could augment simple-taxonomy a little to get automatic "what am I" kind of semantics (the standard "is-a" game). We would do this by adding a propagator:
Since is-a is transitive, on access to the is-a facet of a concept c, the result will be c and all concepts along the entire chains of is-a rooted at c. Global Structural Semantics It is worth noting that our definition of model type is silent concerning certain global semantic properties of possible models of the types that may be constructed. It is, however, not always silent as there are cases where it may be deduced that a given model type provides sufficient conditions for that property and thus all models of the type will have the property. However, this is the unusual case. The canonical example is that of all model to world (and vice-versa) facets which are bipartite. A more specific example comes from our definition of simple-taxonomy, which states that subclasses is transitive and nonreflexive and thus we know that (the graph of) subclasses is acyclic. Typically, however, the above machinery is silent for individual models with regard to the following important global properties of facets:
In large measure, these issues must be answered by looking at the particular graphs in question and running an analysis on them. In many cases such analyses will be unable to answer the question. This can be due to the fact that the graph does not exhibit known necessary or sufficient conditions for the property in question or that the required computational complexity to determine such conditions exceeds "allowable limits". In any event, various intrinsic predicates and path finders are provided for such analyses for all model types Notice that this is possible since the scope of the styles of models captured by model types is constrained by the requirement that they are all a graph in their most fundamental character. Intrinsics for Model Walks The set of supplied intrinsic model walkers and predicates should behave in a functional manner. This is not an issue with respect to predicates as they simply take a model and proceed to (attempt to) determine whether the graph in question is of the sort specified by the predicate. There are two possible sorts of output from such predicates:
The set of model intrinsics is subject to continual update, but includes at least the following set of capabilities:
Ariadne agents reflect the typical set of required properties for agents such as autonomy, mobility, reactiveness (sometimes called "responsive"), proactiveness, and social ability. These all have explicit constructs in the agent language to allow for direct and simple descriptions involving such characters. However, constructs for such more controversial agent aspects as the "Belief, Desire, and Intent" (BDI) model have been deemed too vague and problematic. In addition these latter are more concerned with large scale agents with internalized "symbolic models" of their world. Ariadne explicitly parts company with such traditional AI techniques. Agents are specified by means of constructs arising out of a family of interrelated languages that all "play together". Model types in Ariadne provide various specialized "formats" to organize and structure information. As such, agent descriptions for moving within and among the models built upon these should have access to the more specific and higher level semantics that the model types present. Additionally models in an Ariadne application present semantic interpretations (perspectives) of the various subjects on which the application is focused. Collections of such interrelated models provide the contextual, or "domain level" semantics of the application. Again, descriptions of agents for processing these structures should have constructs which more directly reflect this level of semantic for the subject. Hence users should have access to corresponding families of agent (mini) languages. In order to satisfy these highly desirable qualities and keep the results consistent and manageable, these "mini" languages are in turn part of a family of extensible languages layered on top of a more general base language. The base language has a fuller but "lower level semantic" capacity for agent descriptions and at any point a (power) user can dip down into it from a higher level child language to access this capacity. Such layered languages creating families of inter-related languages can be built by various means, but the most straightforward method would be to define a very simple consistent syntax with a macro style compile time constructor. All new constructions are defined by means of this constructor and each construction itself becomes a new construct in a language layer. Hence, the constructor always has access to all previous constructions when defining a new construct. At compile time of a set of constructions for an agent's definition, each construct is first expanded according to its definition into the lower level constructs upon which it is based. The process recurses. The recursion stops when the base level constructs are all that is left. The resulting base level version of the original code is then compiled into machine code. The design and implementation of such an extensible base language for agent descriptions needs to take into account these notable points:
Agent construction and definition takes place within the context of the overall Ariadne system and makes use of three basic models:
In addition, as we have seen, all models that have agents have the standard actions facet that relates agents to concepts. All of these standard models are user extensible and manipulatable like any other model. The ability of users to access and change the models provides a very high degree of flexibility to users in changing and contexualizing the processing model of Ariadne to their specific needs. Details of Event Model 1301 Continuing in more detail with event model 1301, that model is used to indicate the event classes with which an agent may be registered. If an agent has been registered with an event class, the agent will be invoked and run whenever an event of the class occurs in a context where the agent is available. Each agent invocation creates an activation copy of the agent which runs as a separate thread. There can be any number of agents (modulo system resources) running at any given time. The event class for an agent indicates what sorts of events that the agent should respond to when it is configured for use. An agent is available in a given context if either: Continuing in more detail with the above three options, FIG. 16 shows a fabric model 1601 which belongs to the taxonomy class. The actions facet of model 1601 has two agents attached to the root concept fabric, as shown at 1605. The two agents respond to the events of adding a concept to the model and adding an instance to the model. Since they are attached to the root of model 1601 and by the rules of the taxonomy class are inherited by all of the concepts of the model, one or the other of the agents runs whenever a concept is added to the model and whenever an instance is added to the model. The predefined actions facet has several constraints on it which prevent various possible misconfigurations. Again all of this setup, configuration and enforcement is done via standard Ariadne model and model type definitions and manipulations. The actions facet (like any other) may also have a variety of propagation behaviors for any given model type. For example, in a typical taxonomy it may well be the case that the Actions facet will be inherited down the subclasses facet, as described above for the agents 1605. This basically gives all the capabilities of standard object oriented method inheritance, but is far more flexible and is also end user configurable. Many other scenarios are possible. FIG. 17 shows how the user interface permits a user to manually invoke an agent. In interface 1701, the instances representing the agents are listed at 1705. The user has selected one of them, named FIND-BLANKS. When invoked with two taxonomy models, this agent finds concepts of the one model that have no instances which belong to a given concept of the other model. The instances representing the models are listed at 1703; the user has selected two models, gender and clothing. Ariadne will respond to this input by invoking the selected agent on the two models. The effect of invoking the agent is the following: for any combination of a clothing and a gender concept for which there are no instances, Ariadne will display both the clothing concept and the gender concept. In addition to the models, there is a subsystem for event handling which fully supports asynchronous event processing, including posting, dispatching and handler threads for each top level event class (concept in the events model). Part of this subsystem is an event activation layer for the core level capabilities. This layer supports various core level actions (adding and deleting objects, adding and deleting neighbors along facets, agent invocation, etc.) with transparent posting of associated events. Each event consists of the event's event class in event model 1301, a universal identifier for the particular event, and an argument list. The latter, together with the event class, serve as a "signature" to determine what code is executed for the event. A component which generates the event posts it to a main event queue. Each of the GUI, Agent, Core, and IR classes of events has its own event queue and a main event dispatcher reads the events in the main queue and places each event in the proper queue for its class. The queues are read by an event dispatcher for each class. Each of the class event dispatchers runs in its own thread and dispatches each event in turn as it reads it from the class queue. The event dispatcher for the core class further runs in its own separate task. This split between the core event dispatcher and the other class event dispatchers supports clustering of event actions and increases flexibility and performance of core actions. An extensive palette of out-of-the-box agent "prototypes" is provided for intermediate level users—those not expected to write agent level code. These prototypes can be completed by configuring various properties and registering them with a set of events. Both of these actions are performed by the standard model manipulation capabilities of Ariadne and the GUI: most typically selection, bin element addition, and copy a set of objects and paste along a facet of other objects. This will result in an agent definition instance (in the world) which can then be attached (along the actions facet) to any concept in any model of a model type, where the type is one that is included in the agent's model type list. Typically the actions facet for a model type will have a propagator (though none of these are implicitly provided—the model type definer must decide to make one for the specific case). For example FIG. 16 shows a fabric model 1601 used in an Ecatalog application dealing with clothing. The model 1601 belongs to the model type Simple Taxonomy which is a kind of taxonomy. It has facets is-a (not displayed), subclasses 1611, actions 1607, and a propagator for facet actions:
This propagator causes actions to be inherited by subclass concepts from their parents (in the direct analogue to OO class based inheritance of "methods"). For example, agent refine-content-on-clas-add 1605 is connected in facet actions 1607 to root concept Fabric 1613 and thus it will be available to all concepts throughout the underlying tree. This would be equally true if it were attached to any concept C in the subclasses facet: 1605 would be available to the subclasses under C. Continuing further, let agent 1605 be registered with the event class "Neighbor addition" 1207 of FIG. 12. This indicates that the events that 1605 should watch for in any model where it is attached along the actions facet, are those where some concept (or instance) is being added to one of the existing concepts in the model. For example, if the new concept Chamois is added to the Cotton concept 1609 along subclasses facet 1611, this will generate a "Neighbor addition" event. Agent 1605 would then become active (in its own thread) and perform its actions based on the context of the event: the model where the event happened (model 1601), the concept being added to (Cotton concept 1609), the neighbors being added (new concept Chamois), and the facet involved (subclasses facet 1611). Agent 1605 would then reclassify any chamois fabric instances attached to the originating concept (Cotton concept 1609) down into the more specific new Chamois concept. Note how this uses the context specific information of working with only the instances that are known to be only cotton (not some other existing specialization of cotton or all instances in the world). As an example of an agents code we present here the definition for agent 1605:
Innovations and Benefits of the Ariadne System Further experience with the Ariadne system disclosed in the parent of the present patent application has led to an improved understanding of the notions that underlie the system and of the benefits of basing a software composition system on those notions. Underlying Notions The notions underlying the Ariadne system can be summarized as follows:
The benefits of a system that implements the above notions include the following:
The effect of all of this is that in the Ariadne programming environment, "programs=models+agents+adapters", instead of "programs=algorithms+data structures", as in the traditional programming environments. Introduction to Software Composition Using Model Types, Models, and Agents Development of Software Composition Techniques From the very beginning of software writing up to the present, one of the main problems has been bridging the gap between the functional design of a program and its implementation in the environment provided by a particular combination of a hardware architecture and an operating system. In the beginning, programmers actually wrote code in the binary machine language that the hardware interpreted. This approach was both slow and error prone, so the programmers began using the computer to bridge the gap between the program design and its implementation. The first step in this process was assembly language, which used symbolic representations of areas in memory and of machine instructions. A program called an assembler translated the program written in assembly language into machine language. Each hardware architecture had its own assembly language and the programmer thus still had to have an intimate knowledge of the hardware the code was being written for. The next step in the process of bridging the gap between a program's functional design and its implementation was the development of high-level languages such as Cobol, FORTRAN, and C. These languages were hardware-independent and further permitted direct expression in the program of complex data types and of programming constructs such as conditional branches, loops, functions and procedures, and invocations of functions and procedures. Then came object-oriented programming languages such as C++ or Java. In these languages, programs manipulated objects belonging to programmer-defined classes. The definition for an object's class included a definition of the representation of an object of the class in memory and definitions of operations that could be performed on the object. The implementations of the operations were hidden within the object. For example, a programmer could define a list class and define a representation of the list, for example as a linked list data structure, and operations such as adding an entry to the list, deleting an entry from the list, and reading an entry in the list. The only operations that could be performed on an object of the list class were those defined for the class. A programmer using the object could not directly manipulate the data structure that represented the list. Object-oriented programming languages have many advantages. The entities being manipulated by the program could be defined at as high a level as the programmer desired and as long as the operations on an object and the interfaces to those operations did not change, an object could be reimplemented without disturbing the rest of the program. Further, once a set of object classes had been defined, the class definitions could be reused in many different programs. In terms of the entities manipulated by a program, object oriented programming represented an enormous improvement. A fundamental difficulty remained, however: Though any kind of object could be defined, how the objects manipulated by a program related to each other could be determined only by reading the program code. In other words, there was no way of defining semantic relationships between objects apart from the code that manipulated the objects. The lack of a way of defining the semantic relationships apart from the code makes programs harder to understand and decreases the reusability of their components. The programs are harder to understand because two kinds of information are intertwined in the program:
A good example of the problem of intertwined information is provided by the use of tables in relational database systems (RDBs) to implement complex structures such as trees of Web pages. When a tree is implemented in an RDB, the semantic information and the control information are necessarily intertwined. The reason for this is that the only kind of semantics that can be directly expressed in an RDB is that of a table. In Ariadne terms, an RDB has only one model type, an enhanced two-dimensional matrix, and all of the information in the RDB must be stored in such matrices. Information having any other semantics must be implemented in specific cases by means of this tabular structure. When RDB tables are used to implement a tree of Web pages, what makes the tabular structure behave like a tree is the code that manipulates the information in the tabular structure. What's more, such a representation's implementation must be encoded in the data involved; there is no separation of tables from their data. These limitations make it difficult for applications that are implemented using RDBs and deal with information-rich problems and spaces to handle problems that arise in areas such as flexibility, dynamism, consistency, change, update, etc. For example, schema migration is generally an issue when an RDB is used to represent information-rich problems and spaces. Schema migration becomes necessary when there is a need to change the structure (schema) of the data in the RDB, e.g., adding a column, deleting a column, or changing the type of a column. Since the data and structure are mixed in the tables, all of these changes require making changes to the previous existing versions of the schema to bring them (migrate them) into compliance with the new format. The development of software composition techniques sketched above can be described in broad terms as a growth of expressiveness of the techniques used to compose software. Expressiveness is measured by how simply, explicitly, and directly one can express something in a software composition technique. Anything can be in fact implemented in a program written in machine code; it is however, much harder to write a program of any complexity in machine code and to understand the program once it is written than, for example, to do the same with a program that uses an object-oriented programming language. The reason it is harder is that the machine code is less expressive than the object-oriented programming language. Similarly, because an RDB cannot directly represent a tree, writing and understanding programs that represent trees using RDBs is hard. Using software composition techniques with a low degree of expressiveness for the problem at hand has several severe disadvantages, the most prominent being:
An important difference between Ariadne and other software composition techniques is Ariadne's expressiveness. Ariadne's model types supports the direct expression of novel contextually sensitive formats for the descriptions of the intricate issues involved. Ariadne's models permit the programmer to interweave models of various types in a free and open-ended fashion. Ariadne's agents permit the programmer to simply and explicitly augment the models, and Ariadne's instances permit transparent interaction with a wide variety of external resources. Software Composition Techniques Permitting Direct Expression of Relationships Between Objects Manipulated by a Program The model types, models, and agents disclosed in the parent of the present patent application provide a way of separating semantic information from control information. As is apparent from the foregoing discussion, the semantic information about a group of objects will remain unchanged as long as the kinds of objects remain unchanged, while the control information will vary with every program involving the group of objects. It has long been known that graphs can be used to represent relationships between entities. Each entity is a vertex in the graph, while relationships between the entities are represented by edges connecting the vertices. The graph thus provides a representation of the semantic information concerning the entities represented by the vertices. A simple example of such a graph is a family tree, where each vertex represents an individual and the edges represent relationships between the individuals. It has also long been known that graphs may be organized into classes of graphs. For example, the relationships among the members of any family may be expressed using a family tree graph, and one may speak of a family tree type of graph. The relationships between the vertices of all graphs belonging to the family tree type are the same. The model types of the parent of the present patent application are of course types of graphs, and the models are graphs. Semantics are represented in the Ariadne system at two levels: the model type and the model. A model type represents semantic relationships between the concepts and instances represented by the vertices of any model having the type. A model represents semantic relationships between the particular items represented by the vertices of the model. Control is provided in the systems disclosed in the parent of the present application by defining agents and making the agents available to the models. Agents are programs that respond to events and operate in the context provided by one or more models. As disclosed in the parent of the present patent application, an agent is defined for one or more types of models and one or more classes of events and may be made available to models belonging to the types for which the agent is defined. When an agent is made available to a model, it will respond to an event of a class for which agent is defined by executing in the context provided by the model. Among the events to which an agent may respond is an invocation of the agent by a user of the Ariadne system or another agent. The programming system of the parent of the present patent application thus separates the semantic information about the concepts, represented in the model types and models, from the control information, incorporated in agents. It should be noted here that the use of agents with the models and model types of the parent of the present patent application is in no way dependent on the fact that certain of the vertices of the models of the parent patent application represent concepts. Agents can in fact be used with graphs whose vertices represent any kind of entity whatever, and the model types, models, and agents of the parent patent application thus represent a particular example of a general software composition paradigm. Model Types, Models, Events, and Agents in a Preferred Embodiment: FIG. 18 FIG. 18 sh | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||