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) …
Essentially, a connection graph is merely a data structure for a set of clauses indicating possible refutations. The graph itself is not an inference system. To use the graph, one has to introduce operations on the graph. In this paper, we shall describe a method to obtain rewriting rules from the graph, and then to show that these rewriting rules can be used to generate a refutation plan that may correspond to a large number of linear resolution refutations. Using this method, many redundant resolution steps can be avoided.
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).
It creates some plans and tries to execute them. It analyses the situations deeper in the tree only if the plan fails. In that case it generates new plans correcting what is wrong in the old one. So, the program considers only natural branches of the tree. It can find combinations for which it is necessary to look more than twenty ply ahead.
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.
The problem of reducing the linelike elements of a digitized picture to idealized thin lines is of general interest in pattern recognition. The problem with which we have been primarily concerned is the automatic analysis of chromosome spreads, a typical example of which is shown in figure 1. The aim of the algorithm described in this paper is to reduce such a picture to a'skeleton' of idealized thin lines which satisfy not only the obvious 403 PATTERN RECOGNITION Before describing how this has been done it is necessary to describe more fully the form which the picture takes. J be the set of all pairs of integers. Subsequent transformations of the picture will, however, alter the values of the points and hence of course their significance.
This paper shows how a question-answering system can be constructed using first-order logic as its language and a resolution-type theorem-prover as its deductive mechanism. A working computer-program, Q A3, based on these ideas is described. The performance of the program compares favorably with several other general question-answering systems. A question-answering system accepts information about some subject areas and answers questions by utilizing this information. The type of questionanswering system considered in this paper is ideally one having the following features: 1.
The ability of the higher animals to accept and interpret information from distant objects confers enormous advantages for creatures (or machines) which respond only to immediate stimulation and have no opportunity to anticipate the future. Distance receptors, especially the eyes, serve as early warning systems by giving information of distance events, making it possible to gauge the probable future. The classical biological notion of stimulusresponse applies to creatures limited to touch information. The development of distance-receptors evidently allowed brains to develop to give strategic behaviour. It is unfortunate that the early, now classical, studies of reflexes involving touch and the internal regulation of the body have been so largely taken over to describe brain function, for these concepts are inadequate for describing the central nervous system.
Loosely speaking, recursive inference is when an inference procedure generates an infinite sequence of similar subgoals. In general, the control of recursive inference involves demonstrating that recursive portions of a search space will not contribute any new answers to the problem beyond a certain level. We first review a well known syntactic method for controlling repeating inference (inference where the conjuncts processed are instances of their ruicestors), provide a proof that it is correct, and discuss the con- (Mims under which the strategy is optimal. We also derive more powerful pruning theorems for rases involving transitivity axioms arid cases involving subsumed subgoals. The treatment of repeating inference is followed by consideration of the More difficult prr)liIon of recursive inference Crat does not repeat.
The process of designing, developing, and using expert systems has associated with it a number of basic problems: (i) Rule location: As the number of rules in the:xpert system grows, it becomes less practical to test the conditions of every rule, each time the situation changes slightly. There must be some way to efficiently find the relevant rules. In the context of diagnosing an unknown oil spill seen on a stream, rules about bridge bidding should not even be examined for potential relevance. Which rule should be chosen? Should several be obeyed immediately, or should just one be chosen for execution?
Reprint of a paper appearing in: Proceedings AWL Conference, SIGART/SIC;PLAN Combined issue, August, 1977. We suggest that the concept of a strategy can profitably oe viewLd as know,,tie about /ow to st,'t ct porn aII1011g a set of plausibly useful knowledge sowces, and explore the framewoi k foi knowledge organization which this implies. Meta rules are also considered in the broader context of a tool for programming. We show that they can be conciciered a medium for expressing the criteria for.etri7val The utility of this as a prugrairiming mechanism is considered.