MDS(ID:7698/)KR systemfor Multi-level Diagnosis System or Meta Description System Knowledge representation and problem solving system Related languages
References: in Proceedings of the Third International Joint Conference on Artificial Intelligence IJCAI-73, Stanford, CA: Stanford University 1973 view details in Proceedings of the Third International Joint Conference on Artificial Intelligence IJCAI-73, Stanford, CA: Stanford University 1973 view details Changing Worlds Most representation systems have shied away from dealing with updating of information and have concentrated on reasoning within collections of facts and general knowledge. The strength of the MDS and AIMDS systems is in having a systematic way of updating information. This allows one to adopt a "Hypothesize and Revise" paradigm in processing information in place of the more corrimon search, methods that involve backtracking. Extract: Premises Premises 1. User suplies general knowledge about classes of objects ' (concrete objects, abstract objects, and spatial temporal logical relations among them). Knowledge about changes (actions, plans, hypothesis revision eic.) is supplied in the same formalism as the knowledge about the objects of change. Exceptions to the general rule are represented in a natural manner. 2. User supplies many knowledge sources as though they were independent; but the system integrates the knowledge as it needs them and uses them through a compiling process. 3. User defined processes are (a) defined on the logical structure of information and not on their syntactic structure; (b) defined so that they receive and utilize feedback from the knowledge sources. 4. Uniform system-defined procedures for asserting, querying, matching descriptions are available that permit user processes to have properties mentioned above. 5. Facts and general knowledge are clustered so that convenient pathways of control flow are established. Three levels of such chunking are available - called Frames, Dependency Networks and Contexts. Extract: English Dialog English Dialog The forte of the system developed primarily for BELIEVER, is in its ability to perform constructive information processing tasks (contrasted to deductive ones) using independently specified sets of constraints. The constraints in BELIEVER are in the form of internal consistency of the Person Model, of the plan structure, of the World Model and coherence between the plan structure and the two models. Presently there is no English input or output. In carrying out typed English dialog it is conceivable that a dialog model could be addilively specified ("added on") or knowledge about the domain of discourse be introduced to augment the general world knowledge. The hypothesize and revise paradigm may assist the dialog program in generating and maintaining only a limited number of alternative hypotheses and to avoid a backtracking structure of search. Extract: AIMDS Mechanisms Mechanisms The important mechanisms in AIMDS are 1. Definition of logical structure is made using a convention for introducing relation names along with typing of domain objects. 2. Semantic definitions of relations are given in terms of (a) properties of relations such as irreflexive. transitive. (b) .expressions in a first order logical language whose vocabulary consists of user introduced relations their inverses, typed variables and constants. 3. Uniform procedures are available that use the semantic definitions for (a) forward inferencing (antecedent reasoning) (b) backward inferencing (consequent reasoning) (c) finding and automatically filling in information (d) providing a focus for updating information, and (e) checking semantic constraints and providing feedback to user processes. 4. User has some control over when and how these semantic definitions are used. 5. The system acquires through user interaction necessary information for hypothesis revision or updating. The collection of these acquired rules form the core of the feedback-driven user processes. in [Bobrow, Daniel G.] A Panel On Knowledge Representation chaired by Daniel G. Bobrow view details in [Bobrow, Daniel G.] A Panel On Knowledge Representation chaired by Daniel G. Bobrow view details in Proceedings of the 5th International Joint Conference on Artificial Intelligence IJCAI-77, MIT, Cambridge, Mass., August, 1977 view details in Proceedings of the 5th International Joint Conference on Artificial Intelligence IJCAI-77, MIT, Cambridge, Mass., August, 1977 view details |