Hisashi Kondo

Efficient BDD encodings for partial order constraints with application to expert systems in software verification

One of the difficulties of using Artificial Neural Networks (ANNs) to estimate atmospheric temperature is the large number of potential input variables available. In this study, four different feature extraction methods were used to reduce the input vector to train four networks to estimate temperature at different atmospheric levels. The four techniques used were: genetic algorithms (GA), coefficient of determination (CoD), mutual information (MI) and simple neural analysis (SNA). The results demonstrate that of the four methods used for this data set, mutual information and simple neural analysis can generate networks that have a smaller input parameter set, while still maintaining a high degree of accuracy.

BDD encoding for partial order constraints and its application to expert systems in software verification domains

We introduce a class of computational problems called partial-order constraint satisfaction problems (POCSPs) for formulating the general problem of determining partial orders that satisfy the given constraints. Then we present some heuristic methods for encoding and solving these problems efficiently by using the binary decision diagram (BDD) data structure. Finally, we show that these methods can be successfully applied to expert systems in the subdomain of software verification.

Dynamic control of curve-constrained hyper-redundant manipulators

Hyper-redundant manipulators have large number of kinematic degrees of freedom, thus possessing some unconventional features such as the ability to enter a narrow space while avoiding obstacles. To control these hyper-redundant manipulators accurately, the manipulator dynamics must be included into consideration. This is however time-expensive and makes implementation of the real-time control difficult. In this paper, we propose a dynamic control scheme for hyper-redundant manipulators, which is based on analysis in the defined posture space where three parameters were used to determine the manipulator posture. The manipulator dynamics is modeled in the parameterized form with the parameter of the posture space path and the posture space path-tracking feedforward controller is formulated on the basis of the parameterized dynamic equation. Computer simulation was executed to show the validity of the proposed technique, where a hyper-redundant manipulator traces the posture space path well by the proposed feedforward controller. As a result, it is proven that the hyper-redundant manipulator can track the workspace path accurately.

USING ATMS TO EFFICIENTLY VERIFY THE TERMINATION OF REWRITE RULE PROGRAMS

Assumption-based truth maintenance systems (ATMS) have become powerful and widely used tools in artificial intelligence problem solvers. In this paper, we apply ATMS to verification of termination of computer programs written as a set of rewrite rules. Compared with the traditional methods based on the ordinary backtracking, our method can greatly improve the overall efficiency by virtue of the ATMSs ability to avoid futile backtracking, rediscovering inferences, and rediscovering contradictions. The originality of our work lies in the practical use of the ATMS in a software engineering problem and in the communication protocol between the termination verifier and the ATMS.

Design and heuristics for BDD-based automated termination verification system for rule-based programs

We present the design and heuristics for TERMINATOR/R, an expert system for automatically verifying the termination of rewrite-rule-based programs by using binary decision diagrams (BDDs) for efficient representation of provability. First, we give a recursive definition of the boolean function that computes the provability based on a partial ordering >(precedence) on the set of function symbols. Then the construction of the BDDs for this function, in which a primitive expression f>g consisting of two operation symbols f and g is associated with the logical variable x/sub fg/, is incorporated into an incremental termination verification procedure. We conduct some experiments to see how the performance of this procedure is affected by heuristic selection of variable orderings, and show that our method and heuristics are useful.

Heuristics and Experiments on BDD Representation of Boolean Functions for Expert Systems in Software Verification Domains

Binary decision diagrams (BDDs) are graph-theoretical, compact representation of Boolean functions, successfully applied in the domain of expert systems for practiced VLSI design. The authors have been developing the methods of using BDDs for expert systems that mechanically try to prove the termination of rule-based computer programs. To make BDD representation reeJly practiced, however, we need good heuristics for ordering Booleem variables and operations. In this paper, we will present some heuristic methods that could affect the performance and evaluate them through the comprehensive experiments on sample rule-based programs taken from practical domains such as hatrdwaxe diagnosis, software specification, and mathematics. The results show the big difference among the heuristics and provide us useful information for optimizing the overall systems.

Completion of term-rewriting systems with multiple reduction orderings

The Knuth-Bendix completion procedure results in one of the following, when the reduction ordering and the set of equations are given: (1) the procedure succeeds by generating a complete term-rewriting system; (2) the procedure fails by not giving the orientation to an equation by the reduction ordering; and (3) the procedure diverges without being terminated. The success or failure of the completion procedure depends greatly on the reduction ordering. This paper proposes completion procedure with multiple reduction orderings to reduce the burden of the user in determining the reduction ordering and to prevent the procedure from diverging due to the inadequate reduction ordering. To improve the efficiency, the data called node are used, based on the data structure of ATMS, which is an architecture proposed in the artificial intelligence. The parallel execution of the ordinary completion procedure is simulated under each reduction ordering. The property of ATMS to handle the multiple context is utilized, and the duplication of the inference is avoided by sharing the result of inference among the reduction orderings.

Completion for Multiple Reduction Orderings

We present a completion procedure (called MKB) which works with multiple reduction orderings. Given equations and a set of reduction orderings, the procedure simulates a computation performed by the parallel processes each of which executes the standard Kuuth-Bendix completion procedure (KB) with one of the given orderings. To gain efficiency, however, we develop new inference rules working on objects called nodes, which are data structure consisting of a pair s: t of terms associated with the information to show which processes contain the rule s → t (or t → s) and which processes contain the equation s ↔ t. The idea is based on the observation that some of the inferences made in the processes are closely related, so we can design inference rules that simulate multiple KB inferences in several processes all in a single operation. Our experiments show that MKB is significantly more efficient than the naive simulation of parallel execution of KB procedures, when the number of reduction orderings is large enough.