If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
The purpose of this research is to investigate the extent to which knowledge can replace and support search in selecting a chess move and to delineate the issues involved. This has been carried out by constructing a program. PARADISE (PArtern Recognition Applied to Directing SEarch), which finds the best move in tactically sharp middle game positions from the games of chess masters. The actions of the rules post concepts in the data base while the conditions match patterns in the chess position and data base. The program uses the knowledge base to discover plans during static analysis and to guide a small tree search which confirms that a particular plan is best.
This is an informal description of my ideas about using formal logic as a tool for reasoning systems using computers. Introduction The title of this paper contains both the words'mechanized' and'theory'. I want to make the point that the ideas presented here are not only of interest to theoreticians. I believe that any theory of interest to artificial intelligence must be realizable on a computer. I will not present difficult examples.
In the synthesis of a plan or computer program, the problem of achieving several goals simultaneously presents special difficulties, since a plan to achieve one goal may interfere with attaining the others. This paper develops the following strategy: to achieve two goals simultaneously, develop a plan to achieve one of them and then modify that plan to achieve the second as well. A systematic program modification technique is presented to support this strategy. The technique requires the introduction of a special "skeleton model" to represent a changing world that can accommodate modifications in the plan. This skeleton model also provides a novel approach to the "frame problem."
The selection of what to do next is often the hardest part of resource-limited problem solving. In planning problems, there are typically many goals to be achieved in some order. The goals interact with each other in ways which depend both on the order in which they are achieved and on the particular operators which are used to achieve them. A planning program needs to keep its options open because decisions about one part of a plan are likely to have consequences for another part. This paper describes an approach to planning which integrates and extends two strategies termed the least-commitment and the heuristic strategies.
EPISTEMOLOGICAL PROBLEMS OF ARTIFICIAL INTELLIGENCE John McCarthy Computer Science Department Stanford University Stanford, California 94305 Introduction In (McCarthy and Hayes 1969), we proposed dividing the artificial intelligence problem into two parts - an epistemological part and a heuristic part. This lecture further explains this division, explains some of the epistemological problems, and presents some new results and approaches. The epistemological part of Al studies what kinds of facts about the world are available to an observer with given Opportunities to observe, how these facts can be represented in the memory of a computer, and what rules permit legitimate conclusions to be drawn from these facts. It leaves aside the heuristic problems of how to search spaces of possibilities and how to match patterns. Considering epistemological problems separately has the following advantages: I. The same problems of what information is available to an observer and what conclusions ...
The frame problem arises in attempts to formalise problem--solving processes involving interactions with a complex world. It concerns the difficulty of keeping track of the consequences of the performance of an action in, or more generally of the making of some alteration to, a representation of the world. The paper contains a survey of the problem, showing how it arises in several contexts and relating it to some traditional problems in philosophical logic. In the second part of the paper several suggested partial solutions to the problem are outlined and compared. This comparison necessitates an analysis of what is meant by a representation of a robot's environment.
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.
This paper explores the truism that people think about what they say. It proposes that, to satisfy their own goals, people often plan their speech acts to affect their listeners' beliefs, goals, and emotional states. Such language use can be modelled by viewing speech acts as operators in a planning system, thus allowing both physical and speech acts to be integrated into plans. Methodological issues of how speech acts should be defined in a planbased theory are illustrated by defining operators for requesting and informing. Plans containing those operators are presented and comparisons ore drawn with Searle's formulation.
Meta-DENDRAL programs are products of a large, interdisciplinary group of Stanford University scientists concerned with many and highly varied aspects of the mechanization of scientific reasoning and the formalization of scientific knowledge for this purpose. An early motivation for our work was to explore the power of existing Al methods, such as heuristic search, for reasoning in difficult scientific problems . DENDRAL project began in 1965. Then, as now, we were concerned with the conceptual problems of designing and writing symbol manipulation programs that used substantial bodies of domain-specific scientific knowledge. In contrast, this was a time in the history of AI in which most laboratories were working on general problem solving methods, e.g., in 1965 work on resolution theorem proving was in its prime.
Human programmers seem to know a lot about programming. This suggests a way to try to build automatic programming systems: encode this knowledge in some machine-usable form. In order to test the viability of this approach, knowledge about elementary symbolic programming has been codified into a set of about four hundred detailed rules, and a system, called PECOS, has been built for applying these rules to the task of implementing abstract algorithms. The implementation techniques covered by the rules include the representation of mappings as tables, sets of pairs, property list markings, and inverted mappings, as well as several techniques for enumerating the elements of a collection. The generality of the rules is suggested by the variety of domains in which PECOS has successfully implemented abstract algorithms, including simple symbolic programming, sorting, graph theory, and even simple number theory.