The Use of Dependency Relationships in the Control of Reasoning

Research reported herein was conducted at the Artificial Intelligence Laboratory, a Massachusetts Institute of Technology research program supported in part by the Advanced Research Projects Agency of the Department of Defense and monitored by the Office of Naval Research under contract N00014-75-C-0643.Several recent problem-solving programs have indicated improved methods for controlling program actions. Some of these methods operate by analyzing the time-independent antecedent-consequent dependency relationships between the components of knowledge about the problem for solution. This paper is a revised version of a thesis proposal which indicates how a general system of automatically maintained dependency relationships can be used to effect many forms of control on reasoning in an antecedent reasoning framework.MIT Artificial Intelligence Laboratory Department of Defense Advanced Research Projects Agenc

Knowledge-Based Schematics Drafting: Aesthetic Configuration as a Design Task

Depicting an electrical circuit by a schematic is a tedious task that is a good candidate for automation. Programs that draft schematics with the usual algorithmic approach do not fully exploit knowledge of circuit function, relying mainly on the circuit topology. The extra-topological circuit characteristics are what an engineer uses to understand a schematic; human drafters take these characteristics into account when drawing a schematic. This document presents a knowledge base and an architecture for drafting arithmetic digital circuits having a single theme. The relevance and limitations of this architecture and knowledge base for other types of circuit are explored. It is argued that the task of schematics drafting is one of aesthetic design. The affect of aesthetic criteria on the program architecture is discussed. The circuit layout constraint language, the program's search regimen, and the backtracking scheme are highlighted and explained in detail.MIT Artificial Intelligence Laborator

Heuristic Backtracking Algorithms for SAT

In recent years backtrack search SAT solvers have been the subject of dramatic improvements. These improvements allowed SAT solvers to successfully replace BDDs in many areas of formal verification, and also motivated the development of many new challenging problem instances, many of which too hard for the current generation of SAT solvers. As a result, further improvements to SAT technology are expected to have key consequences in formal verification. The objective of this paper is to propose heuristic approaches to the backtrack step of backtrack search SAT solvers, with the goal of increasing the ability of the SAT solver to search different parts of the search space. The proposed heuristics to the backtrack step are inspired by the heuristics proposed in recent years for the branching step of SAT solvers, namely VSIDS and some of its improvements. The preliminary experimental results are promising, and motivate the integration of heuristic backtracking in state-of-the-art SAT solvers. 1

An Overview of Backtrack Search Satisfiability Algorithms

Propositional Satisfiability (SAT) is often used as the underlying model for a significan

Application of AI Principles to Constraint Managementin Intelligent User Interfaces

This paper describes the application of artificial intelligence principles to constraint management in intelligent user interfaces. Following a description of the problem area, I discuss reasoning models, design knowledge representation, and implementation. User acceptance issues and a current study that applies AI principles to constraints also are addresse

DEPENDENCY DIRECTED BACKTRACKING IN GENERALIZED SATISFICING ASSIGNMENT PROBLEMS

Many authors have described search techniques for the satisficing assignment problem: the problem of finding an interpretation for a set of discrete variables that satisfies a given set of constraints. In this paper we present a formal specification of dependency directed backtracking as applied to this problem. We also generalize the satisficing assignment problem to include limited resource constraints that arise in operations research and industrial engineering. We discuss several new search heuristics that can be applied to this generalized problem, and give some empirical results on the performance of these heuristics.Information Systems Working Papers Serie

AN APPROACH TO DEPENDENCY DIRECTED BACKTRACKING USING DOMAIN SPECIFIC KNOWLEDGE

The idea of dependency directed backtracking proposed by Stallman and Sussman (1977) offers significant advantages over heuristic starch schemes with chronological backtracking which waste much effort by discarding many "good" choices when backtracking situations arise. However, we have found that existing non-chronological backtracking machinery is not suitable for certain types of problems, namely, those where choices do not follow logically from previous choices, but are based on a heuristic evaluation of a constrained set of alternatives. This is because a choice is not justified by a âset of supportâ (of previous choices), but because its advantages outweigh its drawbacks in comparison to its competitors. What is needed for these types of problems is a scheme where the advantages and disadvantages of choices are explicitly recorded during problem solving. Then, if an unacceptable situation arises, information about the nature of the unacceptability and the tradeoffs can be used to determine the most appropriate backtracking point. Further, this requires the problem solver to use its hindsight to preserve those "good" intervening choices that were made chronologically after the "bad" choice, and to resume its subsequent reasoning in fight of the modified set of constraints. In this paper, we describe a problem solver for non-chronological backtracking in situations involving tradeoffs. By endowing the backtracker with access to domain-specific knowledge, a highly contextual approach to reasoning in dependency directed backtracking situations can be achieved.Information Systems Working Papers Serie

Experimental evaluation of preprocessing algorithms for constraint satisfaction problems

This paper presents an experimental evaluation of two orthogonal schemes for preprocessing constraint satisfaction problems (CSPs). The first of these schemes involves a class of local consistency techniques that includes directional arc consistency, directional path consistency, and adaptive consistency. The other scheme concerns the prearrangement of variables in a linear order to facilitate an efficient search. In the first series of experiments, we evaluated the effect of each of the local consistency techniques on backtracking and its common enhancement, backjumping. Surprizingly, although adaptive consistency has the best worst-case complexity bounds, we have found that it exhibits the worst performance, unless the constraint graph was very sparse. Directional arc consistency (followed by either backjumping or backtracking) and backjumping (without any pre-processing) outperformed all other techniques; moreover, the former dominated the latter in computationally intensive situations. The second series of experiments suggests that maximum cardinality and minimum width arc the best pre-ordering (i.e., static ordering) strategies, while dynamic search rearrangement is superior to all the preorderings studied