E1AR0002@SMUVM1.BITNET (11/15/86)
TECHNICAL NOTE: 309\hfill PRICE: \$10.00\\[0.01in] \noindent TITLE: AN ABSTRACT PROLOG INSTRUCTION SET\\ AUTHOR: DAVID H.D. WARREN\\ DATE: OCTOBER 1983\\[0.01in] ABSTRACT: This report describes an abstract Prolog instruction set suitable for software, firmware, or hardware implementation. The instruction set is abstract in that certain details of its encoding and implementation are left open, so that it may be realized in a number of different forms. The forms that are contemplated are: \begin{itemize} \item Translation into a compact bytecode, with emulators written in C (for maximum portability), Progol (a macrolanguage generating machine code, for efficient software implementations as an alternative to direct compilation on machines such as the VAX), and VAX-730 microcode. \item Compilation into the standard instructions of machines such as the VAX or DECsystem-10/20. \item Hardware (or firmware) emulation of the instruction set on a specially designed Prolog processor. \end{itemize} The abstract machine described herein (new Prolog Engine) is a major revision of the old Prolog Engine described in a previous document. The new model overcomes certain difficulties in the old model, which are discussed in a later section. The new model can be considered to be a modification of the old model, where the stack contains compiler- defined goals called environments instead of user-defined goals. The environments correspond to some number of goals forming the tail of a clause. The old model was developed having primarily in mind a VAX-730 microcode implementation. The new model has, in addition, been influenced by hardware implementation considerations, but should remain equally amenable to software or firmware implementation on machines such as the VAX.\\ -------------------------------------------------------------------------------- -------------------------------------------------\\ TECHNICAL NOTE: 310\hfill PRICE: \$10.00\\[0.01in] \noindent TITLE: OVERVIEW OF THE IMAGE UNDERSTANDING TESTBED\\ AUTHOR: ANDREW J. HANSON\\ DATE: OCTOBER 1983\\[0.01in] ABSTRACT: The Image Understanding Testbed is a system of hardware and software that is designed to facilitate the integration, testing, and evaluation of implemented research concepts in machine vision. The system was developed by the Artificial Intelligence Center of SRI International under the joint sponsorship of the Defense Advanced Research Projects Agency (DARPA) and the Defense Mapping Agency (DMA). The primary purpose of the Image Understanding (IU) Testbed is to provide a means for transferring technology from the DARPA-sponsored IU research program to DMA and other organizations in the defense community. The approach taken to achieve this purpose has two components: \begin{itemize} \item The establishment of a uniform environment that will be as compatible as possible with the environments of research centers at universities participating in the IU program. Thus, organizations obtaining copies of the testbed can receive new results of ongoing research as they become available. \item The acquisition, integration, testing, and evaluation of selected scene analysis techniques that represent mature examples of generic areas of research activity. These contributions from IU program participants will allow organizations with testbed copies to immediately begin investigating potential applications of IU technology to problems in automated cartography and other areas of scene analysis. \end{itemize} An important component of the DARPA IU research program is the development of image-understanding techniques that could be applied to automated cartography and military image interpretation tasks; this work forms the principal focus of the testbed project. A number of computer modules developed by participants in the IU program have been transported to the uniform testbed environment as a first step in the technology transfer process. These include systems written in UNIX C, MAINSAIL, and FRANZ LISP. Capabilities of the computer programs include segmentation, linear feature delineation, shape detection, stereo reconstruction, and rule-based recognition of classes of three-dimensional objects.\\ -------------------------------------------------------------------------------- -------------------------------------------------\\ TECHNICAL NOTE: 312\hfill PRICE: \$15.00\\[0.01in] \noindent TITLE: PLANNING ENGLISH REFERRING EXPRESSIONS\\ AUTHOR: DOUGLAS APPELT\\ DATE: OCTOBER 1983\\[0.01in] ABSTRACT: This paper describes a theory of language generation based on planning. To illustrate the theory, the problem of planning referring expressions is examined in detail. A theory based on planning makes it possible for one to account for noun phrases that refer, that inform the hearer of additional information, and that are coordinated with the speaker's physical actions to clarify his communicative intent. The theory is embodied in a computer system called KAMP, which plans both physical and linguistic actions, given a high-level description of the speaker's goals.\\ -------------------------------------------------------------------------------- -------------------------------------------------\\ TECHNICAL NOTE: 313\hfill PRICE: \$10.00\\[0.01in] \noindent TITLE: COMMUNICATION AND INTERACTION IN MULTI-AGENT PLANNING\\ AUTHOR: MICHAEL GEORGEFF\\ DATE: DECEMBER 9, 1983\\[0.01in] ABSTRACT: A method for synthesizing multi-agent plans from simpler single-agent plans is described. The idea is to insert communication acts into the single-agent plans so that agents can synchronize activities and avoid harmful interactions. Unlike most previous planning systems, actions are represented by \underline{sequences} of states, rather than as simple state change operators. This allows the expression of more complex kinds of interaction than would otherwise be possible. An efficient method of interaction and safety analysis is then developed and used to identify critical regions in the plans. An essential feature of the method is that the analysis is performed without generating all possible interleavings of the plans, thus avoiding a combinatorial explosion. Finally, communication primitives are inserted into the plans and a supervisor process created to handle synchronization.\\ -------------------------------------------------------------------------------- -------------------------------------------------\\ TECHNICAL NOTE: 314\hfill PRICE: \$10.00\\[0.01in] \noindent TITLE: PROCEDURAL EXPERT SYSTEMS\\ AUTHORS: MICHAEL GEORGEFF and UMBERTO BONOLLO (MONASH U., AUSTRALIA)\\ DATE: DECEMBER 9, 1983\\[0.01in] ABSTRACT: A scheme for explicitly representing and using expert knowledge of a procedural kind is described. The scheme allows the \underline{explicit} representation of both declarative and procedural knowledge within a unified framework, yet retains all the desirable properties of expert systems such as modularity, explanatory capability, and extendability. It thus bridges the gap between the procedural and declarative languages, and allows formal algorithmic knowledge to be uniformly integrated with heuristic declarative knowledge. A version of the scheme has been fully implemented and applied to the domain of automobile engine fault diagnosis.\\ -------------------------------------------------------------------------------- -------------------------------------------------\\ TECHNICAL NOTE: 315\hfill PRICE: \$16.00\\[0.01in] \noindent TITLE: CHOOSING A BASIS FOR PERCEPTUAL SPACE\\ AUTHOR: STEPHEN T. BARNARD\\ DATE: JANUARY 3, 1984\\[0.01in] ABSTRACT: If it is possible to interpret an image as a projection of rectangular forms, there is a strong tendency for people to do so. In effect, a mathematical basis for a vector space appropriate to the world, rather than to the image, is selected. A computational solution to this problem is presented. It works by backprojecting image features into three-dimensional space, thereby generating (potentially) all possible interpretations, and by selecting those which are maximally orthogonal. In general, two solutions that correspond to perceptual reversals are found. The problem of choosing one of these is related to the knowledge of verticality. A measure of consistency of image features with a hypothetical solution is defined. In conclusion, the model supports an information-theoretic interpretation of the Gestalt view of perception.\\ -------------------------------------------------------------------------------- -------------------------------------------------\\ TECHNICAL NOTE: 316\hfill PRICE: \$12.00\\[0.01in] \noindent TITLE: GENERATING EXPERT ANSWERS THROUGH GOAL INFERENCE\\ AUTHOR: MARTHA E. POLLACK\\ DATE: OCTOBER 1983\\[0.01in] ABSTRACT: Automated expert systems have adopted a restricted view in which the advice-seeker is assumed always to know what advice he needs, and always to express in his query an accurate, literal request for that advice. In fact, people often need to consult with an expert precisely because they don't know what it is they need to know. It is a significant feature of human expertise to be able to deduce, from an incomplete or inappropriate query, what advice is actually needed. This paper develops a framework for enabling automated experts to perform similar deductions, and thereby generate appropriate answers to queries made to them.\\ -------------------------------------------------------------------------------- -------------------------------------------------\\ TECHNICAL NOTE: 317\hfill PRICE: \$10.00\\[0.01in] \noindent TITLE: THE SRI ARTIFICIAL INTELLIGENCE CENTER--A BRIEF HISTORY\\ AUTHOR: NILS J. NILSSON\\ DATE: JANUARY 24, 1984\\[0.01in] ABSTRACT: Charles A. Rosen came to SRI in 1957. I arrived in 1961. Between these dates, Charlie organized an Applied Physics Laboratory and became interested in learning machines and self-organizing systems. That interest launched a group that ultimately grew into a major world center of artificial intelligence research--a center that has endured twenty-five years of boom and bust in fashion, has graduated over a hundred AI research professionals, and has generated ideas and programs resulting in new products and companies as well as scientific articles, books, and this particular collection itself. The SRI Artificial Intelligence Center has always been an extremely cohesive group, even though it is associated with many contrasting themes. Perhaps these very contrasts are responsible for its vitality. It is a group of professional researchers, but visiting Ph.D. candidates (mainly from Stanford University) have figured prominently in its intellectual achievements. It is not part of a university, yet its approach to AI has often been more academic and basic than those used in some of the prominent university laboratories. For many years a vocal group among its professionals has strongly emphasized the role of logic and the centrality of reasoning and declarative representation in AI, but it is also home to many researchers who pursue other aspects of the discipline. Far more people have left it (to pursue careers in industry) than are now part of it, yet it is still about as large as it has ever been and retains a more or less consistent character. It is an American research group, supported largely by the Defense Department, but, from the beginning, it has been a melting pot of nationalities.\\ -------------------------------------------------------------------------------- -------------------------------------------------\\ TECHNICAL NOTE: 318\hfill PRICE: \$15.00\\[0.01in] \noindent TITLE: AN AI APPROACH TO INFORMATION FUSION\\ AUTHORS: THOMAS D. GARVEY and JOHN D. LOWRANCE\\ DATE: DECEMBER 1983\\[0.01in] ABSTRACT: This paper discusses the use of selected artificial intelligence (AI) techniques for integrating multisource information in order to develop an understanding of an ongoing situation. The approach takes an active, top-down view of the task, projecting a situation description forward in time, determining gaps in the current model, and tasking sensors to acquire data to fill the gaps. Information derived from tasked sensors and other sources is combined using new, non-Bayesian inference techniques. This active approach seems critical to solve the problems posed by the low emission signatures anticipated for near-future threats. Simula- tion experiments lead to the conclusion that the utility of ESM system operation in future conflicts will depend on how effectively onboard sensing resources are managed by the system. The view of AI that will underly the discussion is that of a tech nology attempting to extend automation capabilities from the current replace the human's hands approach to that of replacing or augmenting the human's cognitive and perceptual capabilities. Technology transfer issues discussed in the presentation are the primary motivation for highlighting this view. The paper will conclude with a discussion of unresolved problems associated with the introduction of AI technology into real world military systems.\\ -------------------------------------------------------------------------------- -------------------------------------------------\\ TECHNICAL NOTE: 319\hfill PRICE: \$15.00\\[0.01in] \noindent TITLE: BELIEF AND INCOMPLETENESS\\ AUTHOR: KURT KONOLIGE\\ DATE: JANUARY 11, 1984\\[0.01in] ABSTRACT: Two artificially intelligent (AI) computer agents begin to play a game of chess, and the following conversation ensues: \begin{itemize} \item S1: Do you know the rules of chess? \item S2: Yes. \item S1: Then you know whether White has a forced initial win or not. \item S2: Upon reflection, I realize that I must. \item S1: Then there is no reason to play. \item S2: No. \end{itemize} Both agents are state-of-the-art constructions, incorporating the latest AI research in chess playing, natural-language understanding, planning, etc. But because of the overwhelming combinatorics of chess, neither they nor the fastest foreseeable computers would be able to search the entire game tree to find out whether White has a forced win. Why then do they come to such an odd conclusion about their own knowledge of the game? The chess scenario is an anecdotal example of the way inaccurate cognitive models can lead to behavior that is less than intelligent in artificial agents. In this case, the agents' model of belief is not correct. They make the assumption that an agent actually knows all the consequences of his beliefs. S1 knows that chess is a finite game, and thus reasons that, in principle, knowing the rules of chess is all that is required to figure out whether White has a forced initial win. After learning that S2 does indeed know the rules of chess, he comes to the erroneous conclusion that S2 also knows this particular consequence of the rules. And S2 himself, reflecting on his own knowledge in the same manner, arrives at the same conclusion, even though in actual fact he could never carry out the computations necessary to demonstrate it.\\ -------------------------------------------------------------------------------- -------------------------------------------------\\ TECHNICAL NOTE: 320\hfill PRICE: \$15.00\\[0.01in] \noindent TITLE: A FORMAL THEORY OF KNOWLEDGE AND ACTION\\ AUTHOR: ROBERT C. MOORE\\ DATE: FEBRUARY 1984\\[0.01in] ABSTRACT: Most work on planning and problem solving within the field of artificial intelligence assumes that the agent has complete knowledge of all relevant aspects of the problem domain and problem situation. In the real world, however, planning and acting must frequently be performed without complete knowledge. This imposes two additional burdens on an intelligent agent trying to act effectively. First, when the agent entertains a plan for achieving some goal, he must consider not only whether the physical prerequisites of the plan have been satisfied, but also whether he has all the information necessary to carry out the plan. Second, he must be able to reason about what he can do to obtain necessary information that he lacks. In this paper, we present a theory of action in which these problems are taken into account, showing how to formalize both the knowledge prerequisites of action and the effects of action on knowledge.\\ -------------------------------------------------------------------------------- -------------------------------------------------\\ TECHNICAL NOTE: 321\hfill PRICE: \$10.00\\[0.01in] \noindent TITLE: PROBABILISTIC LOGIC\\ AUTHOR: NILS J. NILSSON\\ DATE: FEBRUARY 6, 1984\\[0.01in] ABSTRACT: Because many artificial intelligence applications require the ability to deal with uncertain knowledge, it is important to seek appropriate generalizations of logic for that case. We present here a semantical generalization of logic in which the truth-values of sentences are probability values (between 0 and 1). Our generaliza- tion applies to any logical system for which the consistency of a finite set of sentences can be established. (Although we cannot always establish the consistency of a finite set of sentences of first-order logic, our method is usable in those cases in which we can.) The method described in the present paper combines logic with probability theory in such a way that probabilistic logical entailment reduces to ordinary logical entailment when the probabilities of all sentences are either 0 or 1.\\ -------------------------------------------------------------------------------- -------------------------------------------------\\ -------