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) …
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.
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.
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.
THE PERCEIVING ROBOT: WHAT DOES IT SEE? WHAT DOES IT DO? Oliver G. Selfridge Judy A. Franklin The Perceiving Robot: What Does It See? What Does It Do? by Oliver G. Selfridge and Judy A. Franklin - - The Perceiving Robot: What Does It See? What Does It Do? Oliver G. Selfridge & Judy A. Franklin GTE Laboratories We examine the nature of robots in the future, and propose that their role is fundamentally to be responsible agents for people, and not mere programmed artifacts. That means that besides extended powers of perception, they will need to deal with their own purposes--embedded in purpose structures--and the ways to modify and optimize their purposes in parallel. The primary purpose of robotic perception is to see how well the robot is performing on a current task (or subtask).
In May 1971 the Mark 1.5 Edinburgh robot system went online as a complete hand-eye system. Two years earlier the Mark 1 device had been connected to the ic L 4130 computer of the Department of Machine Intelligence and Perception. The present equipment thus represents a useable system, not yet up to full Mark 2 specification, but considerably more useful than the Mark 1. It is important that the complete system should be as self-reliant as possible. If it depends much upon human assistance to pre-process information or to put things right when they go astray, it is all too easy in one's research to avoid the central issues of a problem, and produce a'solution' which does not survive when confronted by real situations.
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.