A Methodology For Partial Verification And Validation Of OPS5-based Al Applications
28 Aug 1991-Vol. 3, pp 284-288
TL;DR: This article presents a methodology for partial verification and validation of production system applications, in particular, OPS5-based applications, and discusses how the two main parts of the methodology contribute towards the partial verified and validated applications.
Abstract: Verification and validation of AI systems is an important problem. In this article, we present a methodology for partial verification and validation of production system applications, in particular, OPS5-based applications. This methodology has two main parts in it. The first part, termed Domain-Independent Verification, involves finding certain properties and relations of productions by compile-time analysis. The second part, termed Systematic Testing, consists of generating the data-dependency graph, finding the set of distinct data-paths, and generating test data for each distinct data-path. We discuss how these two parts of the methodology contribute towards the partial verification and validation of OPS5-based applications.
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01 Jan 1985
TL;DR: This book presents techniques for rule-based programming of expert systems in OPS5, a widely-used language designed to simplify rule- based programming, and compares OPS5 with other tools for programming expert systems.
Abstract: This book presents techniques for rule-based programming of expert systems in OPS5. OPS5 is a widely-used language designed to simplify rule-based programming. This book is aimed at experienced programmers in industry and universities, and contains examples and exercises with answers. The first section of the book is a tutorial. The development of a small, self-contained OPS5 program is followed from problem definition to testing. The second section considers the nature of production-system architectures, and compares OPS5 with other tools for programming expert systems.
393 citations
TL;DR: This paper provides a solution to the frame problem, and to the related problem that it is not always reasonable to explicitly specify all of the consequences of actions, by keeping a single model of the world that is updated when actions are performed.
318 citations
TL;DR: An algorithm to detect errors in decision tables when the constraints are linear in variables that take only integer values is described, which exploits the abundance of very simple constraints that occur in typical decision table contexts.
Abstract: To detect errors in decision tables one needs to decide whether a given set of constraints is feasible or not. This paper describes an algorithm to do so when the constraints are linear in variables that take only integer values. Decision tables with such constraints occur frequently in business data processing and in nonnumeric applications. The aim of the algorithm is to exploit. the abundance of very simple constraints that occur in typical decision table contexts. Essentially, the algorithm is a backtrack procedure where the the solution space is pruned by using the set of simple constrains. After some simplications, the simple constraints are captured in an acyclic directed graph with weighted edges. Further, only those partial vectors are considered from extension which can be extended to assignments that will at least satisfy the simple constraints. This is how pruning of the solution space is achieved. For every partial assignment considered, the graph representation of the simple constraints provides a lower bound for each variable which is not yet assigned a value. These lower bounds play a vital role in the algorithm and they are obtained in an efficient manner by updating older lower bounds. Our present algorithm also incorporates an idea by which it can be checked whether or not an (m –2)-ary vector can be extended to a solution vector of m components, thereby backtracking is reduced by one component.
3 citations