Showing papers on "Knowledge representation and reasoning published in 1977"

Reasoning about knowledge and action

Abstract: This paper discusses the problems of representing and reasoning with information about knowledge and action. The first section discusses the importance of having systems that understand the concept of knowledge, and how knowledge is related to action. Section 2 points out some of the special problems that are involved in reasoning about knowledge, and section S presents a logic of knowledge based on the idea of possible worlds. Section 4 integrates this with a logic of actions and gives an example of reasoning in the combined system. Section 5 makes some concluding comments.

A network-based knowledge representation and its natural deduction system

Abstract: We describe a knowledge representation scheme called KNET and a problem solving system called SNIFFER designed to answer queries using a K-NET knowledge base. K-NET uses a partitioned semantic net to combine the expressive capabilities of the first-order predicate calculus with linkage to procedural knowledge and with full indexing of objects to the relationships in which they participate. Facilities are also included for representing taxonomies of sets and for maintaining hierarchies of contexts. SNIFFER is a manager and coordinator of deductive and problem-solving processes. The basic system includes a logically complete set of natural deduction facilities that do not require statements to be converted into clause or prenex normal form. Using SNIFFER's coroutine-based control structure, alternative proofs may be constructed in pseudo-parallel and results shared among them. In addition, SNIFFER can also manage the application of specialist procedures that have specific knowledge about a particular domain or about the topology of the K-NET structures, for example, specialist procedures are used to manipulate taxonomic information and to link the system to information in external data bases.

NUDGE: a knowledge-based scheduling program

Abstract: Traditional scheduling algorithms (using the techniques of PERT charts, decision analysis or operations research) require well-defined, quantitative, complete sets of constraints. They are insufficient for scheduling situations where the problem description is ill-defined, involving incomplete, possibly inconsistent and generally qualitative constraints. The NUDGE program uses an extensive knowledge base to debug scheduling requests by supplying typical values for qualitative constraints, supplying missing details and resolving minor inconsistencies. The result is that an informal request is converted to a complete description suitable for a traditional scheduler. To implement the NUDGE program, a knowledge representation language-FRL-0- based on a few powerful generalizations of the traditional property list representation has been developed. The NUDGE knowledge base defined in FRL-0 consists of a hierarchical set of concepts that provide generic deseriptions of the typical activities, agents, plans and purposes of the domain to be scheduled. Currently, this domain is the management and coordination of personnel engaged in a group project. NUDGE constitutes an experiment in knowledge-based, rather than power-based AI programs. It also provides an example of an intelligent support system, in which an AI program serves as an aid to a decision maker. Finally, NUDGE has served an experimental vehicle for testing advanced representation techniques.

The FRL Primer

Abstract: : The Frame Representation Language (FRL) is an experimental language written to explore the use of frames as a knowledge representation technique. The term 'frame' as used in FRL was inspired by Minsky's (75) development of frame theory. FLR extends the traditional Property List representation scheme by allowing properties to have comments, defaults and constraints, to inherit information from abstract forms of the same type, and to have attached procedures triggered by adding or deleting values, or if a value is needed. We introduce FRL with the aid of a simple example: WHOSIS, a database of Al person's names, addresses, interests, and publications. A second section contains an abridged manual describing FRL's most-used commands and conventions. (Author)

An Overview of OWL, a language for knowledge representation

Abstract: : The motivation and overall organization of the OWL language for knowledge representation is described. OWL consists of a memory of concepts in terms of which all English phrases and all knowledge of an application domain are represented, a theory of English grammar which tells how to map English phrases into concepts, a parser to perform that mapping for individual sentences, and an interpreter to carry out procedures which are written in the same representational formalism. The system has been applied to the study of interactive dialogs, explanations of its own reasoning, and question answering.

Error recovery in robot systems.

Abstract: This dissertation addresses itself to the problem faced by a robot in recovering from failures during execution of a task. Failures occur partly because sensory information is inaccurate, partly because effectors do not always perform as expected, and partly because the domain in which the robot operates cannot be characterized exactly. Robot systems with automated planners have traditionally dealt with the problem of error recovery by merely replanning to achieve the desired goal, without attempting to characterize the failure in any way whatsoever. The central idea in this thesis is that planning recovery from failures has its own special techniques, distinct from those used in conventional planning systems. Two viewpoints, looking at the past for an explanation of the failure, and looking at the current situation to attempt a characterization of the failure state, provide powerful heuristics for error recovery. This thesis suggests that these heuristics can be formalized as failure reason analysis and multiple outcome analysis, and that knowledge relevant for such analysis can be provided through a failure reason model and a multiple outcome model associated with each action. The failure reason model about why actions provides a means for representing fail, like bumping into an object to be grasped because of servoing errors or because of inaccurate information about the location of the object. The model also provides knowledge required for distinguishing between the different reasons for failure. Finally, it includes recommendations of corrective actions to be taken if failure is attributed to a specific reason. This model in used in failure reason analysis in building a failure tree representing possible explanations of the failure. The explanations represented in the tree are then used in planning recovery. The multiple outcome model provides a way of representing the possible outcomes of an action, like bumping onto the object or bumping onto the ground in the immediate vicinity of the object, ignoring the fact that these outcomes could be the result of several different reasons. Knowledge required to distinguish between different outcomes is provided as part of the model. In cases where the immediately available information is inadequate to identify the outcome of an action, the multiple outcome model provides a basis for executing actions to serve as information gathering steps. The novel feature here is that information gathering is directed by specific expectations about the state of the world. A computer implementation of a program called MEND has provided a medium for exploring the above idea. MEND has been designed to automate recovery from failures in simple manipulation tasks to be performed by the JPL robot, but the techniques used in MEND have greater generality. A first implementation of MEND established the basis of this investigation. A second version, which has been designed to correct some limitations of the first version, has not yet been fully implemented and integrated with the JPL robot system. The techniques of planning recovery from failures through failure reason analysis and multiple outcome analysis are contributions to the subject of robotics. More importantly, however, the problem of error recovery is recognized to be a member of a larger class of problems involving knowledge representation and common sense reasoning, both of which are core topics in the study of artificial intelligence. The solution presented in this thesis makes some new contributions to these core topics.

Experience with KRL-O: one cycle of a knowledge representation language

Abstract: The goal of the KRL research group is to develop a knowledge representation language with which to bui ld sophisticated systems and theories of language understanding. This is a d i f f i cu l t goal to reach, one that wi l l require a number of years. We are using an iterative strategy with repeated cycles of design, implementation and testing. An in i t ia l design is described in an overview of KRI (Bobrow & Winograd, 1977). The system created in the f i rst cycle is called KRL-o, and this paper describes its implementation, an analysis of what was learned f rom our experiments in using KRL-o, and a brief summary of plans for the second iteration of the cycle (the KRi.-i system). In wr i t ing this paper, we have emphasized our d i f f icul t ies and disappointments more than our successes, because the major lessons learned f rom the iterative cycle were in the form of problems. We mention only br ief ly in the summary of experiments those features of Krelo that we found most satisfactory and useful.

Aspects of a Theory for Automated Student Modelling.

Abstract: : This report documents some of our recent investigations into a theory for automatically inducing and using (structural) models of a student which explicate his reasoning strategies, his representation of procedural skills and his underlying misconceptions as manifested in his errors. The first chapter discusses a diagnostic model based on the concept of a procedural network - a network having many of the properties of the older style semantic networks but which captures both the intensional and extensional (or executable) aspects of procedural skills. These diagnostic models provide not only a technique for modelling the underlying or deep structure aspects of a procedural skill but they also suggest that an important criterion for modelling cognitive processes and their related knowledge representation is that of finding a natural way to account for all possible manifested errors in human performance of that skill. The second chapter describes a considerably more complex theory/technique for analyzing the problem solving trace or protocol of a student and then automatically synthesizing a model of his problem solving strategies as well as the motivations or plans that he used to guide him in his solution. This theory captures the subtle reasoning powers of a master tutor and as such acts as a powerful modelling technique of a learner which is needed for guiding our computer-based laboratory tutor as well as providing a new methodology for measuring how a student's problem solving performance is evolving. This theory also forms a cornerstone for building information processing models of master tutors.

Knowledge Representation in Automatic Planning Systems

Abstract: This paper is a tutorial on automatic planning systems, with particular emphasis given to knowledge representation issues. The effectiveness of a planner depends, to a large extent, on its ability to make use of descriptions and expertise associated with particular task domains. Examples of such domain knowledge include action models, state description models, scenarios, and special purpose plan composition methods. Our discussion focuses on how such knowledge is represented in planning systems, and on how various planning strategies make use of it.

Representation of Knowledge for Chess Endgames Towards a Self-Improving System

Abstract: This thesis describes an investigation of the problems involved in representing knowledge within the task area of elementary Chess endgames. Two major criteria are taken for the choice of a model of & the chessplayer's knowledge : firstly, that algorithms constructed using the model should be natural from the viewpoint of a chessplayer and commensurate with his, view of the complexity of the task, and secondly that the algorithms should be capable of refinement in the light of experience in a manner which preserves the previous property. Elementary chess endgames are studied as a field in which programs based on tree-searching and traditional evaluation functions have achieved poor results and where tree-searching seems to play little or no part for people. It is therefore possible to examine problems of knowledge representation and program refinement largely independently of the tree-searching paradigm. A long term aim of the research is to develop a representation suitable as the basis for a fully automatic system of algorithm refinement, whilst maintaining the criteria given above. A model is proposed and algorithms are given for two endgames, King and Rook against King (KRK) and King and Pawn against King (KPK) using this model. It is argued that both algorithms are reasonably natural and compact representations and experiments in refining these algorithms are described in detail. In both cases, the process of refinement is shown to be a reasonably straightforward one (for people) and one which maintains the properties of naturalness and compactness. The possibility of automating this process is considered.

Knowledge representation for automated understanding of natural language discourse

Abstract: levels, such as Pet6fi's text grammar and Rumelhart's schemata for stories By defining permissible interrelations between constituents of larger texts at all levels of abstraction, these create meaning structures organized on both hierarchical and associative principles, rather than on a sequential basis The automated "understanding" process consists of instantiating an appropriate set of event templates by associating a given text with the ERGO inventory of event templates and const ucting a network of instanti ted templates based on intertemplate relational rules and constraints The network of instantiated t mplates thus represents the information content of the unstructured original text, and provides the structured input for the event record data bas Details of the automated understanding process presented here in the abstract are given in the follo ing section 32 Processing Principles The process of data base generation involves two major functions: (1) co tent analysis of the incoming text (2) event record synthesis (or production) The first involves constructing a meaning representation of the text and the second the extraction of relevant information and its storage in a data base record The major focus of ERGO is on reports of a particular class of events which describe aircraft movements The unit of analysis is therefore the report, a textual unit consisting of one or more par graphs, each containing one or more sentences The first step in the analytical process involves a lexical lookup: a lexical entry contains morphological, syntactic and semantic information, on the lines of Sager (1973) and Stockwell et al (1973) Each sentence is then subjected to a syntactic analysis by means of an Augmented Transition Network (ATN) parser (Woods, 1970) Since event templates are based on propositional structures, the analytical

Design rationale for TELOS, a PASCAL-based AI language

Abstract: TELOS is a PASCAL-based AI language intended to facilitate efficient development of efficient, well-structured programs. The design emphasizes powerful data abstraction and control abstraction mechanisms rather than the provision of particular high-level constructs. Among the many capabilities of TELOS are those intended to make it especially suitable for systematic AI model building, for example, in the areas of knowledge representation, planning, and reasoning. An event facility is provided which unifies the handling of conditional interrupts (demons), process suspension, process communication and execution faults. The context-dependent TELOS data base is referenceable either associatively or directly.

Identification of Finite State Models of Human Operators.

Abstract: : The problems of modelling a class of manned systems in which the operator or operators have available only a finite number of decision alternatives which they can use to control the system configuration and mode of operation over time are the focus of this report. An abstract system theoretic structure suitable for representing such discrete control systems is developed and the structure is used to organize the analysis of data obtained from a man-in-the-loop simulation of an AAA system. The structure used to define discrete control is a hierarchical/heterarchical network of finite state systems. The nodes in this network represent system components, task and activities. Several levels of abstraction are used which means that both macroscopic and microscopic descriptions are possible. The structure captures the certain aspects of coordination and the flow of decision making activity through the system. The feasibility of discrete control modelling is demonstrated. The structural aspects, particularly knowledge representation and the identification of key decision points, seem quite powerful. The statistical and data analysis procedures work successfully but need further refinement if model construction is to maximally efficient.