The Temporal Logic of the Tower Chief System

The purpose is to describe the logic used in the reasoning scheme employed in the Tower Chief system, a runway configuration management system. First, a review of classical logic is given. Defensible logics, truth maintenance, default logic, temporally dependent propositions, and resource allocation and planning are discussed

A Neural Network Truth Maintenance System.

A novel approach using Neural Networks has been developed to generate consistent labelling of facts in relation to a given set of rules. In the proposed system, facts are represented by neurons and their interconnections form the knowledge base. The Neural Network Truth Maintenance System(TMS) arrives at a valid solution provided the solution exists. A valid solution is a consistent labelling of facts. If a valid solution does not exist the network does not converge. An experimental setup was built and tested using conventional integrated circuits. The hardware design is suitable for VLSI implementation for large, real-time problems. The TMS Neural Network blends the simplicity and speed of Neural Network architecture with the power of artificial intelligence techniques. A methodology has been developed to study the stability of logical networks in terms of Lyapunov Stability criteria

A generic ATMS

AbstractThe main aim of this paper is to create a general truth maintenance system based on the De Kleer algorithm. This system (the ATMS) is to be designed so that it can be used in different propositional monotonic logic models of reasoning systems. The knowledge base system that will interact with it is described. Furthermore, we study the efficiency that transferring the ATMS to a logic with several truth values presupposes. Definitions and properties of the generic ATMS are particularized to interact both with a reasoning system based on multivalued logic specifically for the case of [0, 1 ]-valued logic and with a reasoning system based on fuzzy logic. The latter will be designed to reason with fuzzy truth values, although a parallel project might be followed using linguistic labels directly

Correct Parallel Status Assignments for the Reason Maintenance System

This paper represents a beginning development of a parallel truth maintenance system to interact with a parallel inference engine. We present a solution which performs status assignments in parallel to belief nodes in the Reason Maintenance System (RMS) presented by [3],[4]. We examine a previously described algorithms by [7] which fails to correctly detect termination of the status assignments. Under Petrie\u27s algorithm, termination may go undetected an in certain circumstances (namely the existence of an unsatisfiable circularity) a false detection may occur. We present an algorithm that corrects these problems

Mixed-initiative control of intelligent systems

Mixed-initiative user interfaces provide a means by which a human operator and an intelligent system may collectively share the task of deciding what to do next. Such interfaces are important to the effective utilization of real-time expert systems as assistants in the execution of critical tasks. Presented here is the Incremental Inference algorithm, a symbolic reasoning mechanism based on propositional logic and suited to the construction of mixed-initiative interfaces. The algorithm is similar in some respects to the Truth Maintenance System, but replaces the notion of 'justifications' with a notion of recency, allowing newer values to override older values yet permitting various interested parties to refresh these values as they become older and thus more vulnerable to change. A simple example is given of the use of the Incremental Inference algorithm plus an overview of the integration of this mechanism within the SPECTRUM expert system for geological interpretation of imaging spectrometer data

EXPERIMENTS WITH AN INTEGER PROGRAMMING FORMULATION OF AN EXPERT SYSTEM

We present expert system (ES) and Integer Programming (IP) formulations of an NP-complete constraint satisfaction problem (CSP). The problem involves generating a plan for assigning faculty to courses given a variety of constraints and preferences and other tentative data. The expert system consists of a heuristic rule-based problem solver and a truth maintenance system. The IP model consists of about 700 zero-one decision variables and 300 constraints. We describe and contrast the expert system and IP models in terms of behavior, quality of results, and computational performance. We find that the expressiveness of the IP model is hampered by its single objective function, inability to encode various types of complex preferences, the lack of useful output when it fails to find a feasible solution, and a general lack of control over inference. It is also difficult to make incremental revisions to the plan produced by the IP model. In contrast, the truth maintenance system maintains justifications for assignments, which makes it possible to reason about incremental modifications to a plan. In terms of performance, we found that whenever the IP approach finds a solution, it does so quickly using the Pivot and Complement heuristic of Balas and Martin (1980). The branch and bound always failed to find a feasible integer solution when the heuristic failed to find one.Information Systems Working Papers Serie

A study of the methodologies currently available for the maintenance of the knowledge-base in an expert system

This research studies currently available maintenance methodologies for expert system knowledge bases and taxonomically classifies them according to the functions they perform. The classification falls into two broad categories. These are: (1) Methodologies for building a more maintainable expert system knowledge base. This section covers techniques applicable to the development phases. Software engineering approaches as well as other approaches are discussed. (2) Methodologies for maintaining an existing knowledge base. This section is concerned with the continued maintenance of an existing knowledge base. It is divided into three subsections. The first subsection discusses tools and techniques which aid the understanding of a knowledge base. The second looks at tools which facilitate the actual modification of the knowledge base, while the last secttion examines tools used for the verification or validation of the knowledge base. Every main methodology or tool selected for this study is analysed according to the function it was designed to perform (or its objective); the concept or principles behind the tool or methodology: and its implementation details. This is followed by a general comment at the end of the analysis. Although expert systems as a rule contain significant amount of information related to the user interface, database interface, integration with conventional software for numerical calculations, integration with other knowledge bases through black boarding systems or network interactions, this research is confined to the maintenance of the knowledge base only and does not address the maintenance of these interfaces. Also not included in this thesis are Truth Maintenance Systems. While a Truth Maintenance System (TMS) automatically updates a knowledge base during execution time, these update operations are not considered \u27maintenance\u27 in the sense as used in this thesis. Maintenance in the context of this thesis refers to perfective, adaptive, and corrective maintenance (see introduction to chapter 4). TMS on the other hand refers to a collection of techniques for doing belief revision (Martin, 1990) . That is, a TMS maintains a set of beliefs or facts in the knowledge base to ensure that they remain consistent during execution time. From this perspective, TMS is not regarded as a knowledge base maintenance tool for the purpose of this study