A fast hypothetical reasoning system is described which is named KICK-SHOTGAN, and which avoids inefficient backtracking by the forward synthesis of necessary hypothesis combination along this network. The formation of inference-path network is based on a linear-time algorithm for the satisfiability testing of propositional Horn clauses. This system differs from ATMS mainly in its total problem solving nature. That is, it works for the logical problem-solving framework which yields a solution for a given goal, whereas the ATMS calculates possible data supported by hypotheses incrementally in response to the input of a justification (rule) from a problem solver existing outside the ATMS. >

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Fast hypothetical reasoning system using inference-path network

Industrial plants tends to be immersed in a distressing environment and the decision making process can be hindered by some blocking conditions. The key idea of this work is to use risk analysis to support the user in identifying the cause of a problems as well as deciding what actions should be developed to eliminate it. The risk analysis is then used to calculate the risk of a specific situation affecting an industrial plant. The situation, characterized by the symptoms, has a risk value which is estimated from the information stored on the system concerning the probability of occurrence of a new problem when a specific action is implemented. For the sake of system robustness it is expected that this knowledge grows along the life-cycle of an industrial plant. Depending on the knowledge available and on the risk of the situation, the adequate procedure is suggested for promptly reaction and problem elimination.

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Risk based decision support system for life cycle management of industrial plants

The methods of fast hypothetical reasoning systems developed for propositional logic cannot be applicable in a straightforward manner to the predicate-logic case. A fast hypothetical reasoning mechanism that is effective for predicate-logic knowledge (actually for function-free predicate Horn-clause knowledge) is presented. A reasoning method developed in the deductive database area is effectively applied to this mechanism. >

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An efficient hypothetical reasoning system for predicate-logic knowledge-base

A hypothetical reasoning system is an important framework in the development of advanced knowledge-based systems. It can be effectively applied to many practical problems including model-based diagnosis, and designs. However, the inference speed of its prolog -based implementation is slow, and this is particularly because of inefficient backtracking. In order to overcome this problem, a fast hypothetical reasoning mechanism for propositional-logic knowlegde has been developed by combining the advantages of forward and backward reasoning styles. This fast mechanism, however, cannot be applied to hypothetical reasoning with predicate-logic knowledge where variables are included as arguments. The paper presents a fast hypothetical reasoning mechanism for predicate-logic knowledge as an extension of the above idea. A reasoning method developed in the deductive database area is effectively utilized to realize this fast mechanism, which can even manipulate recursive rules.

Efficient hypothetical reasoning system for predicate-logic knowledge base

Hypothetical reasoning, which is one type of abductive reasoning, is an important framework in the development of advanced knowledge-based systems. One problem with hypothetical reasoning is its slow inference speed, which is due to its nonmonotonic inference nature. A fast hypothetical reasoning system with predicate Horn clause expressions has been developed to overcome this problem. However, when the constraints for hypotheses are not strong, the number of hypotheses to be synthetized becomes too large to calculate. The paper presents an efficient hypothetical reasoning method combining best-first search, beam search and branch-and-bound search strategies for computing the optimal solution, which is the most desirable solution in many cases. The effectiveness of this method is shown experimentally using fault-diagnosis problems in logic circuits.

Efficient inference method for computing an optimal solution in predicate-logic hypothetical reasoning

A hypothesis-based reasoning system handles a knowledge base including complete (fact) and incomplete (hypothesis) knowledge. The handling of the incomplete knowledge plays an important role in realizing advanced AI functions such as commonsense, flexible matching, learning, etc. From both theoretical and practical viewpoints, the hypothesis-based logical reasoning system can be considered to be an important start point towards a next-generation knowledge base architecture. This paper describes an enhanced knowledge representation of the hypothetical reasoning system designed particularly for interactive diagnosis problems. The mechanisms of two knowledge acquisition modules developed for this knowledge base including hypotheses are then described. The first module, which is based on an inductive inference mechanism for the knowledge including hypotheses, enables multiple-concept formation from given examples. The key technology of this mechanism is a minimum generalization extended to the knowledge including hypotheses. The second module provides knowledge assimilation and management functions for the frame knowledge base constructed on the hypothetical reasoning system. That is, this module enables the following functions: (1) the system can assimilate new knowledge while maintaining the consistency and non-redundancy of the knowledge base; (2) the system rearranges the knowledge base when existing knowledge is deleted; and (3) the system can control the inheritance link of the frame knowledge base in response to an input indicating that the property inheritance from an upper frame is denied as an exceptional case. The nonmonotonicity of the hypothetical reasoning system is considered in these mechanisms. Shapiro's logical debugging algorithm is employed effectively to identify the knowledge which causes inconsistency. The whole system has been implemented using the metaprogramming of Prolog.

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Tetsusi Matsuda

Papers

Knowledge acquisition mechanisms for a logical knowledge base including hypotheses