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) …
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."
ABSTRACT TEntEsuis is a program designed to provide assistance on the task of building knowledge-based systems. It facilitates the interactive transfer of knowledge from a human expert to the system, in a high level dialog conducted in a restricted subset of natural language. TEIRESIAS in operation and demonstrates how it guides the acquisition of new inference rules. I. Introduction Where much early work in artificial intelligence was devoted to the search for a single, powerful, domain-independent problem solving methodology (e.g., This work was supported in part by the Advanced Research Projects Agency under ARPA Order 2494; by a Chaim Weizmann Postdoctoral Fellowship for Scientific Research, and by grant MCS 77-02712 from the National Science Foundation. It was carried out on the SUMEX Computer System, supported by the NIH Grant RR-00785. The program is named for the blind seer in Oedipus the King, since, as we will see, the program, like the prophet, has a form of "higher order" ...
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.
KEYNOTE: SOME NOTES ON THE TECHNOLOGY OF RECOGNITION Oliver G. Selfridge Lincoln Laboratory,* Massachusetts Institute of Technology Lexington, Massachusetts We are here today,I take it, to appraise what has been done, and to discern the future, if we may. I notice that a man's worth these times is in the words he speaks and writes. The understanding that may lead to a publishable paper is much to be preferred to the understanding that leads to a useful machine. "But I say unto you, that every idle word that men shall speak, they shall give account thereof in the day of judgment. For by thy words thou shalt be justified, and by thy words thou shalt be condemned."
Success of a knowledge-based program depends on both competence and acceptability. It must perform well for it to be worth using, but is must be acceptable to users for it to be used. There are many dimensions to developing competent and acceptable knowledge based systems which can serve as "intelligent assistants" for problem solvers in science (see Shortliffe and Davis, 1975). One of these is the old AI problem of representation of knowledge. Since most previous work on representation has stressed its importance for problem-solving (e.g.
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 . 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."
Further progress in the application of computers to many practical fields seems to depend heavily on the success in implementing learning and inductive processes within machines. For example, to develop a consultation system for medical or plant disease diagnosis, prognosis and decision making in general, it is very desirable, perhaps even necessary, to be able to'teach' the system through examples of correct and/or incorrect decisions, rather than by precisely describing the decision process in its full generality and then transforming this description into a computer program. A similar situation exists in computer chess. The development of computer programs playing at the master level (especially the end games) seems to be a formidable task if the programs are not eventually able to learn and improve on their decision making rules through the specific examples of games, rather than by being explicitly told all the rules. Due to easy access to human knowledge about chess and the relative simplicity of testing the results, chess is one of the most attractive testing domains for inductive inference programs.
Describing scientific theory formation as an information-processing problem suggests breaking the problem into subproblems and searching solution spaces for plausible items in the theory. Scientific theories are judged partly on how well they explain the observed data, how general their rules are, and how well they are able to predict new events. The meta-D END RA L program attempts to use these criteria, and more, as guides to formulating acceptable theories. The problem for the program is to discover conditional rules of the form S-421, where the S's are descriptions of situations and the A's are descriptions of actions. The rule is interpreted simply as'When the situation S occurs, action A occurs'.
Research in machine vision is an important activity in artificial intelligence laboratories for two major reasons. First, understanding vision is a worthy subject for its own sake. The point of view of artificial intelligence allows a fresh new look at old questions and exposes a great deal about vision in general, independent of whether man or machine is the seeing agent. Second, the same problems found in understanding vision are of central interest in the development of a broad theory of intelligence. Making a machine see brings one to grips with problems like that of knowledge interaction on many levels and of large system organization.
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. Recent speculation and preliminary work at several research centers has suggested a variety of ways in which these traditional constraints could be relaxed. In this paper we describe some of these possible extensions, illustrating the discussion where possible with examples taken from the current Stanford Research Institute robot system. A major theme in current artificial intelligence research is the design and construction of programs that perform robot problem solving. The usual formulation begins with the assumption of a physical device like a mechanical arm or a vehicle that can use any of a preprogrammed set of actions to manipulate objects in its environment.