Results


And-or graphs, theorem-proving graphs, and bi-directional search

Classics

And-or graphs and theorem-proving graphs determine the same kind of search space and differ only in the direction of search: from axioms to goals, in the case of theorem-proving graphs, and in the opposite direction, from goals to axioms, in the case of and-or graphs. We investigate the construction of a single general algorithm which covers unidirectional search both for and-or graphs and for theorem-proving graphs, bidirectional search for path-finding problems and search for a simplest solution as well as search for any solution. Indeed, many different search spaces can represent the same original problem, as in the case of resolution systems where different refinements of the resolution rule determine different search spaces for a single problem of demonstrating the unsatisfiability of a given set of clauses. In the tree representation of theorem-proving graphs (used in Machine Intelligence 5 (Kowalski 1969)), identical problems Tare generated at different times, working forwards from the initial set of axioms {D, E, F, G}.


Human problem solving

Classics

The aim of the book is to advance the understanding of how humans think. It seeks to do so by putting forth a theory of human problem solving, along with a body of empirical evidence that permits assessment of the theory.Englewood Cliffs, N.J.: Prentice-Hall


Learning and executing generalized robot plans

Classics

"In this paper we describe some major new additions to the STRIPS robot problem-solving system. The first addition is a process for generalizing a plan produced by STRIPS so that problem-specific constants appearing in the plan are replaced by problem-independent parameters.The generalized plan, stored in a convenient format called a triangle table, has two important functions. The more obvious function is as a single macro action that can be used by STRIPS—either in whole or in part—during the solution of a subsequent problem. Perhaps less obviously, the generalized plan also plays a central part in the process that monitors the real-world execution of a plan, and allows the robot to react "intelligently" to unexpected consequences of actions.We conclude with a discussion of experiments with the system on several example problems."Artificial Intelligence 3:251-288


Some new directions in robot problem solving

Classics

For the past several years research on robot problem-solving methods has centered on what may one day be called'simple' plans: linear sequences of actions to be performed by single robots to achieve single goals in static environments. This process of forming new subgoals and new states continues until a state is produced in which the original goal is provable; the sequence of operators producing that state is the desired solution. In the case of a single goal wff, the objective is quite simple: achieve the goal (possibly while minimizing some combination of planning and execution cost). The objective of the system is to achieve the single positive goal (perhaps while minimizing search and execution costs) while avoiding absolutely any state satisfying the negative goal.


QA4: A procedural calculus for intuitive reasoning

Classics

Abstract: This report presents a language, called QA4, designed to facilitate the construction of problem-solving systems used for robot planning, theorem proving, and automatic program synthesis and verification. Thus it provides many useful programming aids. More importantly, however, it provides a semantic framework for common sense reasoning about these problem domains. The interpreter for the language is extraordinarily general, and is therefore an adaptable tool for developing the specialized techniques of intuitive, symbolic reasoning used by the intelligent systems.


An approach to the frame problem, and its implementation

Classics

This paper proposes a method for handling the frame problem in representing conceptual, or natural-language-type information. The method is part of a larger calculus for expressing conceptual information, called P c F-2, which is described in Sandewall (1972), and which is a modification and extension of Sandewall (1971a). When the STRIPS schema adds a fact, PLANNER would add the corresponding fact to the data base using the primitive thassert. In this context, by epistemological information we mean a notation together with a set of rules (for example, logical axioms) which describe permissible deductions.


A Survey of the Literature on Problem-solving methods in artificial intelligence

Classics

"Problem-solving methods using some sort of heurstically guided search process have been the subject of much research in Artificial Intelligence. This paper groups these problem-solving methods under three major headings: the State-Space Approach, the Problem-Reduction Approach and the Formal-Logic Approach." New York: McGraw-Hill.



On generality and problem solving: a case study using the DENDRAL program

Classics

"Heuristic DENDRAL is a computer program written to solve problems of inductive inference in organic chemistry. This paper will use the design of Heuristic DENDRAL and its performance on different problems for a discussion of the following topics: 1. the design for generality; 2. the performance problems attendant upon too much generality; 3. the coupling of expertise to the general problem solving processes; 4. the symbiotic relationship between generality and expertness, and the implications of this symbiosis for the study and design of problem solving systems. We conclude the paper with a view of the design for a general problem solver that is a variant of the "big switch" theory of generality."See also: Stanford University Report (ACM Citation)In Meltzer, B. and Michie, D. (Eds.), Machine Intelligence 6, pp. 165–190. Edinburgh University Press


Bi-Directional Search

Classics

Ph.D. dissertation "Bi-directional and heuristic search in path problems" (Stanford, Computer Science, 1970) summarized in this article in Machine Intelligence 6 (1971).In the uni-directional algorithms, the search proceeds from an initial nodeforward until the goal node is encountered. Problems for which the goal nodeis explicitly known can be searched backward from the goal node. Analgorithm combining both search directions is bi-directional.This method has not seen much use because book-keeping problems werethought to outweigh the possible search reduction. The use of hashingfunctions to partition the search space provides a solution to some of theseimplementation problems. However, a more serious difficulty is involved.To realize significant savings in bi-directional search, the forward andbackward search trees must meet in the 'middle' of the space. The potentialbenefits from this technique motivates this paper's examination of thetheoretical and practical problems in using bi-directional search.