Relay-Version: version B 2.10 5/3/83; site utzoo.UUCP Path: utzoo!utgpu!water!watmath!clyde!rutgers!ames!ucbcad!ucbvax!nmsu.CSNET!yorick From: yorick@nmsu.CSNET.UUCP Newsgroups: comp.ai.digest Subject: Computer and Cognitive Science Abstracts (1 of 2) Message-ID: <8705130513.AA19887@ucbvax.Berkeley.EDU> Date: Sun, 10-May-87 18:07:45 EDT Article-I.D.: ucbvax.8705130513.AA19887 Posted: Sun May 10 18:07:45 1987 Date-Received: Fri, 15-May-87 07:15:04 EDT Sender: daemon@ucbvax.BERKELEY.EDU Distribution: world Organization: The ARPA Internet Lines: 336 Approved: ailist@stripe.sri.com ABSTRACTS OF MEMORANDA IN COMPUTER AND COGNITIVE SCIENCE Computing Research Laboratory New Mexico State University Box 30001 Las, Cruces, NM 88003. Kamat, S.J. (1985), Value Function Approach to Multiple Sensor Integration, MCCS-85-16. A value function approach is being tried for integrating multiple sensors in a robot environment with known objects. The state of the environment is characterized by some key parameters which affect the performance of the sensors. Initially, only a handful of discrete environmental states will be used. The value of a sensor or a group of sensors is defined as a function of the number of possible object contenders under consideration and the number of contenders that can be rejected after using the sensor information. Each possible environmental state will have its effect on the function, and the function could be redefined to indicate changes in the sampling frequency and/or resolution for the sensors. A theorem prover will be applied to the sensor information available to reject any contenders. The rules used by the theorem prover may be different for each sensors, and the integration is provided by the common decision domain. The values for the different sensor groups will be stored in a database. The order of use of the sensor groups will be according to the values, and can be stored as the best search path. The information in the database can be adaptively updated to provide a training methodology for this approach. Cohen, M. (1985), Design of a New Medium for Volume Holographic Information Processing, MCCS-85-17. An optical analog of the neural networks involved in sensory processing consists of a dispersive medium with gain in a narrow band of wavenumbers, cubic saturation, and a memory nonlinearity that may imprint multiplexed volume holographic gratings. Coupled mode equations are derived for the time evolution of a wave scattered off these gratings; eigenmodes of the coupling matrix $$kappa$$ saturate preferentially, implementing stable reconstruction of a stored memory from partial input and associative reconstruction of a set of stored memories. Multiple scattering in the volume reconstructs cycles of associations that compete for saturation. Input of a new pattern switches all the energy into the cycle containing a representative of that pattern; the system thus acts as an abstract categorizer with multiple basins of stability. The advantages that an imprintable medium with gain biased near the critical point has over either the holographic or the adaptive matrix associative paradigms are (1) images may be input as non-coherent distributions which nucleate long range critical modes within the medium, and (2) the interaction matrix $$kappa$$ of critical modes is full, thus implementing the sort of `full connectivity' needed for associative reconstruction in a physical medium that is only locally connected, such as a nonlinear crystal. Uhr, L. (1985), Massively Parallel Multi-Computer Hardware = Software Structures for Learning, MCCS-85-19. Suggestions are made concerning the building and use of appropriately structured hardware/software multi-computers for exploring ways that intelligent systems can evolve, learn and grow. Several issues are addressed such as: what computers are, the great variety of topologies that can be used to join large numbers of computers together into massively parallel multi-computer networks, and the great sizes that the micro-electronic VLSI (``very large scale integration'') technologies of today and tomorrow make feasible. Finally, several multi-computer structures that appear especially appropriate as the substrate for systems that evolve, learn and grow are described, and a sketch of a system of this sort is begun. Partridge, D. (1985), Input-Expectation Discrepancy Reduction: A Ubiquitous Mechanism, MCCS-85-24. The various manifestations of input-expectation discrepancy that occurs in a broad spectrum of research on intelligent behavior is examined. The point is made that each of the different research activities highlights different aspects of an input-expectation reduction mechanism and neglects others. A comprehensive view of this mechanism has been constructed and applied in the design of a cognitive industrial robot. The mechanism is explained as both a key for machine learning strategies, and a guide for the selection of appropriate memory structures to support intelligent behavior. Ortony, A., Clore, G. & Foss, M. A. (1985), Conditions of Mind, MCCS-85-27. A set of approximately 500 words taken from the literature on emotion was examined. The overall goal was to develop a comprehensive taxonomy of the affective lexicon, with special attention being devoted to the isolation of terms that refer to emotions. Within the taxonomy we propose, the best examples of emotion terms appear to be those that (a) refer to [i]internal, mental[xi] conditions as opposed to physical or external ones, (b) are clear cases of [i]states[xi], and (c) have [i]affect[xi] as opposed to behavior or cognition as their predominant referential focus. Relaxing one or another of these constraints yields poorer examples or nonexamples of emotions; however, this gradedness is not taken as evidence that emotions necessarily defy classical definition. Wilks, Y. (1985), Machine Translation and Artificial Intelligence: Issues and their Histories, MCCS-85-29. The paper reviews the historical relations, and future prospects for relationships, between artificial intelligence and machine translation. The argument of the paper is that machine translation is much more tightly bound into the history of artificial intelligence than many realize (the MT origin of Prolog is only the most striking example of that), and that it remains, not a peripheral, but a crucial task on the AI agenda. Coombs, M.J. (1986), Artificial Intelligence Foundations for a Cognitive Technology: Towards The Co-operative Control of Machines, MCCS-85-45. The value of knowledge-based expert systems for aiding the control of physical and mechanical processes is not firmly established. However, with experience, serious weaknesses have become evident which, for solution, require a new approach to system architecture. The approach proposed in this paper is based on the direct manipulation of models in the control domain. This contrasts with the formal syntactic reasoning methods more conventionally employed. Following from work on the simulation of qualitative human reasoning, this method has potential for implementing truly co-operative human/computer interaction. Coombs, M.J., Hartley, R. & Stell J.F. (1986), Debugging User Conceptions of Interpretation Processes, MCCS-85-46. The use of high level declarative languages has been advocated since they allow problems to be expressed in terms of their domain facts, leaving details of execution to the language interpreter. While this is a significant advantage, it is frequently difficult to learn the procedural constraints imposed by the interpreter. Thus, declarative failures may arise from misunderstanding the implicit procedural content of a program. This paper argues for a \fIconstructive\fR approach to identifying poor understanding of procedural interpretation, and presents a prototype diagnostic system for Prolog. Error modelling is based on the notion of a modular interpreter, misconceptions being seen as modifications of correct procedures. A trace language, based on conceptual analysis of a novice view of Prolog, is used by both the user to describe his conception of execution, and the system to display the actual execution process. A comparison between traces enables the the correct interpreter to be modified in a manner which progressively corresponds to the user's mental interpreter. Dorfman, S.B. & Wilks, Y. (1986), SHAGRIN: A Natural Language Graphics Package Interface, MCCS-85-48. It is a standard problem in applied AI to construct a front-end to some formal data base with the user's input as near English as possible. SHAGRIN is a natural language interface to a computer graphics package. In constructing SHAGRIN, we have chosen some non-standard goals: (1) SHAGRIN is just one of a range of front-ends that we are fitting to the same formal back-end. (2) We have chosen not a data base in the standard sense, but a graphics package language, a command language for controlling the production of graphs on a screen. Parser output is used to generate graphics world commands which then produce graphics PACKAGE commands. A four-component context mechanism incorporates pragmatics into the graphics system as well as actively aids in the maintenance of the state of the graph world. Manthey, M.J. (1986), Hierarchy in Sequential and Concurrent Systems or What's in a Reply, MCCS-85-51. The notion of hierarchy as a tool for controlling conceptual complexity is justifiably well entrenched in computing in general, but our collective experience is almost entirely in the realm of sequential programs. In this paper we focus on exactly what the hierarchy-defining relation should be to be useful in the realm of concurrent programming. We find traditional functional dependency hierarchies to be wanting in this context, and propose an alternative based on shared resources. Finally we discuss some historical and philosophical parallels which seem to have gone largely unnoticed in the computing literature. Huang, X-M (1986), A Bidirectional Chinese Grammar in A Machine Translation System, MCCS-85-52. The paper describes a Chinese grammar which can be run bidirectionally, ie., both as a parser and as a generator of Chinese sentences. When used as a parser, the input to the grammar is single Chinese sentences, and the output would be tree structures for the sentences; when used as a generator, tree structures are the input, and Chinese sentences, the output. The main body of the grammar, the way bidirectionality is achieved, and the performance of the system with some example sentences are given in the paper. Partridge, D. & Wilks, Y. (1986), Does AI have a methodology different from Software Engineering?, MCCS-85-53. The paper argues that the conventional methodology of software engineering is inappropriate to AI, but that the failure of many in AI to see this is producing a Kuhnian paradigm ``crisis''. The key point is that classic software engineering methodology (which we call SPIV: Specify-Prove-Implement-Verify) requires that the problem be circumscribable or surveyable in a way that it is not for areas of AI like natural language processing. In addition, it also requires that a program be open to formal proof of correctness. We contrast this methodology with a weaker form SAT ( complete Specification And Testability - where the last term is used in a strong sense: every execution of the program gives decidably correct/incorrect results) which captures both the essence of SPIV and the key assumptions in practical software engineering. We argue that failure to recognize the inapplicability of the SAT methodology to areas of AI has prevented development of a disciplined methodology (unique to AI, which we call RUDE: Run-Understand-Debug-Edit) that will accommodate the peculiarities of AI and also yield robust, reliable, comprehensible, and hence maintainable AI software. Slator, B.M., Conley, W. & Anderson, M.P (1986), Towards an Adaptive Front-end, MCCS-85-54. An adaptive natual language interface to a graphics package has been implemented. A mechanism for modelling user behavior operating over a script-like decision matrix capturing co-occurrence of commands is used to direct the interface, which uses a semantic parser, when ambiguous utterances are encountered. This is an adaptive mechanism that forms a model of a user's tendencies by observing the user in action. This mechanism provides a method for operating under conditions of uncertainty, and it adds power to the interface - but, being a probabilistic control scheme, it also adds a corresponding element of nondeterminism. A hidden operator experiment was conducted to collect utterance files for a user-derived interface development process. These empirical data were used to design the interface; and a second set, collected later, was used as test data. Lopez, P., Johnston, V. & Partridge, D. (1986), Automatic Calibration of the Geometric Workspace of an Intelligent Robot, MCCS-85-55. An intelligent robot consisting of an arm, a single camera, and a computer, functioning in an industrial environment, is described. A variety of software algorithms that compute and maintain, at task-execution time, the mappings between robot arm, work environment (the robot's world), and camera coordinate systems, are presented. These mappings are derived through a sequence of arm movements and subsequent image ``snapshots'', from which arm motion is detected. With the aid of world self-knowledge (i.e., knowledge of the length of the robot arm and the height of the arm to the base pivot), the robot then uses its ``eye'' to calculate a pixel-to-millimeter ratio in two known planes. By ``looking'' at its arm at two different heights, it geometrically computes the distance of the camera from the arm, hence deriving the mapping from the camera to the work environment. Similarly, the calculation of the intersection of two arm positions (where wrist location and hypothetical base location form a line) gives a base pivot position. With the aid of a perspective projection, now possible since the camera position is known, the position of the base and its planar angle of rotation in the work environment (hence the world to arm mapping) is determined. Once the mappings are known, the robot may begin its task, updating the approximate camera and base pivot positions with appropriate data obtained from task-object manipulations. These world model parameters are likely to remain static throughout the execution of a task, and as time passes, the old information receives more weight than new information when updating is performed. In this manner, the robot first calibrates the geometry of its workspace with sufficient accuracy to allow operation using perspective projection, with performance ``fine-tuned'' to the nuances of a particular work environment through adaptive control algorithms. Fass, D. (1986), Collative Semantics: An Approach to Coherence, MCCS-85-56. Collative Semantics (CS) is a domain-independent semantics for natural language processing that focusses on the problem of coherence. Coherence is the synergism of knowledge (synergism is the interaction of two or more discrete agencies to achieve an effect of which none is individually capable) and plays a substantial role in cognition. The representation of coherence is distinguished from the representation of knowledge and some theoretical connections are established between them. A type of coherence representation has been developed in CS called the semantic vector. Semantic vectors represent the synergistic interaction of knowledge from diverse sources (including the context) that comprise semantic relations. Six types of semantic relation are discriminated and represented: literal, metaphorical, anomalous, novel, inconsistent and redundant. The knowledge description scheme in CS is the senseframe, which represents lexical ambiguity. The semantic primitives in senseframes are word-senses which are a subset of the word-senses in natural language. Because these primitives are from natural language, the semantic markerese problem is avoided and large numbers of primitives are provided for the differentiated description of concepts required by semantic vectors. A natural language program called meta5 uses CS; detailed examples of its operation are given. McDonald, D.R. & Bourne, L.E. Jr. (1986), Conditional Rule Testing in the Wason Card Selection Task, MCCS-85-57. We used the Wason card selection task, with variations, to study conditional reasoning. Disagreement exists in the literature, as to whether performance on this task improves when the problem is expressed concretely and when instructions are properly phrased. In order to resolve some inconsistencies in previous studies, we examined the following variables, (1) task intructions, (2) problem format, and (3) the thematic compatibility of solution choices with formal logic and with pre-existing schemas. In Experiment 1, performance was best in an 8-card, rather than a 4-card or a hierarchical decision-tree format. It was found in Experiment 2 that instructions directing subjects to make selections based on ``violation'' of the rule, rather than assessing its truth or falsity, resulted in more correct responses. Response patterns were predictable in part from formal logical considerations, but primarily from mental models, or schemas, based on (assumed) common prior experience and knowledge. Several explanations for the findings were considered. Partridge, D, McDonald, J., Johnston, V. & Paap, K. (1986) AI Programs and Cognitive Models: Models of Perceptual Processes, MCCS-85-60. We examine and compare two independently developed computer models of human perceptual processes: the recognition of objects in a scene and of words. The first model was developed to support intelligent reasoning in a cognitive industrial robot - an AI system. The second model was developed to account for a collection of empirical data and known problems with earlier models - a cognitive science model. We use these two models, together with the results of empirical studies of human behaviour, to generate a generalised model of human visual processing, and to further our claim that AI modelers should be more cognizant of empirical data. A study of the associated human phenomena provides an essential basis for understanding complex models as well as valuable constraints in complex and otherwise largely unconstrained domains.