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) …
A program called "AM" is described which carries on simple mathematics research, defining and studying new concepts under the guidance of a large body of heuristic rules. The 250 heuristics communicate via an agenda mechanism, a global priority queue of small tasks for the program to perform, and reasons why each task is plausible (for example, "Find generalizations of'primes', because'primes' turned out to be so useful a concept"). Each concept is represented as an active, structured knowledge module. One hundred very incomplete modules are initially supplied, each one corresponding to an elementary set-theoretic concept (for example, union). This provides a definite but immense space which AM begins to explore.
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'.
In this paper we compare three models of transformational grammar: the mathematical model of Ginsburg and Partee (1969) as applied by Salomaa (1971), the mathematical model of Peters and Ritchie (1971 and forthcoming), and the computer model of Friedman et al. (1971). All of these are, of course, based on the work of Chomsky as presented in Aspects of the Theory of Syntax (1965). We were led to this comparison by the observation that the computer model is weaker in three important ways: search depth is not unbounded, structures matching variables cannot be compared, and structures matching variables cannot be moved. All of these are important to the explanatory adequacy of transformational grammar. Both mathematical models allow the first, they each allow some form of the second, one of them allows the third.
Young animals play games in order to prepare themselves for the business of serious living, without getting hurt in the training period. Game-playing on computers serves a similar function. It can teach us something about the structure of thought processes and the theory of struggle and has the advantage over economic modelling that the rules and objectives are clear-cut. If the machine wins tournaments it must be a good player. The complexity and originality of a master chess player is perhaps greater than that of a professional economist.
Report 83 36 What's New A Semantic Definition of Stanford KSL Novelty. Despite the vast interest in learning and the abundance of related papers (cf.[Dietterich 81a], [Buchanan 78], [Michalski 83], [Dietterich 81b], [Dietterich 82]), no one has rigorously defined what;i means to be "new", either in general or with respect to a single concept. This paper attempts to fill that gap. This preference stems from our belief that a semantic account (one based on the possible interpretations of the theory) provides important insight into the phenomenon of novelty. It also means we may be able to generalize these results to other logic._
This mei includes many implementation level details aboui the RLL-1 system, described in a companion paper, "RLL4: A Representation Language Language" (Heuristic Programming Project Working Paper HPF-80-9, October 1980, at Stanford University, by Russell Greiner). F.1 F.1 Top Level Functions F 1 F.2 Functions needed to Bootstrap RLL-1 F 3 F.3 Convenience Functions F 9 F.4 Advised Functions F 11 F.5 Global Variables F 11 G. Sample Session G.1 Anything Many RLL-1 units are directly used by one or more of the RLL-1 functions listed below. These special ones are enumerated below, following a depth first traversal of the RLL-1 Knowledge Base. Diagram #1 portrays a skeleton of this hierarchy, showing the subset relations joining these various classes. AnySlot refers to slots) - refers to the abstract object which typifies members of Any*** - refers to a format [e.g.
The language designer typically designs that language with one particular application domain in mind: as subsequent types of applications are tned, what had originally been useful features are found to be undesirable limitations, and the language is overhauled or scrapped. One remedy to this bleak cycle might be to construct a representation language whose domain is the field of representational languages itself. Toward this end, we designed and implemented RLL-11, a frame-based Representation 1.anguanr. The components of representation languages in general (such as slots and inheritance mechanisms) and of RLL-1 itself, in particular, are encoded declaratively as frames. By modifying these frames, the user can change the semantics of RLL-1's components, and significnntly alter the overall character of the RLL-1 environment.
NI Introduction Section 1 1 Introduction The MOLGEN project has focused on research Into the applications of symbolic computation and Inference -to the field of molecular biology. This has taken the specific form of systems which provide assistance to the experimental scientist in various tasks, the most important of which have been the design of complex experiment plans and the analysis of nucleic acid sequences. During the period of further research proposed in this document, we plan to expand and improve these systems and build new ones to meet the rapidly growing needs of the domain of recombinant DNA technology. We do this with the view of including. The advent of rapid DNA cloning and sequencing methods has had an explosive effect on the amount of data that can be most readily represented and analyzed by computer. Moreover we have already reached a point where progress In the analysis of the information in DNA sequences is being limited by the combinatorics of the various types ...
It is understood that you have a patient who may have an infection. Please answer the following questions, terminating each response with RETURN. To correct typing errors, use the DELETE key to delete single characters, ctrl W to delete a word, and ctrl Q to delete the whole line. If you are not certain of your answer, you may modify the response by inserting a certainty factor (a number from 1 to 10) in parentheses after your response. Absolute certainty (10) is assumed for every unmodified answer.