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) …
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.
ABSTRACT Computer systems for use by physicians have had limited impact on clinical medicine. When one examines the most common reasons for poor acceptance of medical computing systems, the potential relevance of artificial intelligence techniques becomes evident. This paper proposes design criteria for clinical computing systems and demonstrates their relationship to current research in knowledge engineering. The MYCIN System is used to illustrate the ways in which our research group has attempted to respond to the design criteria cited. My goal is to present design criteria which may encourage the use of computer programs by physicians, and to show that Al offers some particularly pertinent methods for responding to the design criteria outlined.
We learn (memorize) multiplication tables, learn (discover how) to walk, learn (build UP an understanding of, then an ability to synthesize) languages. Many subtasks and capabilities are involved in these various kinds of learning. One capability central to many kinds of learning is the ability to generalize: to take into account a large number of specific observations, then to extract and retain the important common features that characterize classes of these observations. This generalization problem has received considerable attention for two decades in the fields of Artificial Intelligence, Psychology, and Pattern Recognition (e.g., [Bruner, The results so far have been tantalizing: Partially successful generalization programs have been written for problems ranging from learning fragments of spoken English to learning rules of Chemical spectroscopy. But comparing alternative strategies, and developing a general understanding of techniques has been difficult because of differences in data representations, terminology, and problem characteristics.
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 ...
APPLICATION OF THEOREM PROVING TO PROBLEM SOLVING *t Cordell Green Stanford Research Institute Menlo Park, California Abstract This paper shows how an extension of the resolution proof procedure can be used to construct problem solutions. The extended proof procedure can solve problems involving state transformations. The paper explores several alternate problem representations and provides a discussion of solutions to sample problems including the "Monkey and Bananas" puzzle and the "Tower of Hanoi" puzzle. The paper exhibits solutions to these problems obtained by QA3, a computer program based on these theorem-proving methods. In addition, the paper shows how QA3 can write simple computer programs and can solve practical problems for a simple robot.
Knowledge about a particular type of ore deposit is encoded in a computational model representing observable geological features and the relative significance thereof. Following the initial design of a model, simple performance evaluation techniques are used to assess the extent to which the performance of the model reflects faithfully the intent of the model designer. These results identify specific portions of the model that might benefit from "fine tuning", and establish priorities for such revisions. This description of the Prospector system and the model design process serves to illustrate the process of transferring human expertise about a subjective domain into a mechanical realization. I. INTRODUCTION In an increasingly complex and specialized world, human expertise about diverse subjects spanning scientific, economic, social, and political issues plays an increasingly important role in the functioning of all kinds of organizations.
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.