Date: Fri 16 Sep 1988 01:22-EDT From: AIList Moderator Nick Papadakis Reply-To: AIList@AI.AI.MIT.EDU Us-Mail: MIT LCS, 545 Tech Square, Rm# NE43-504, Cambridge MA 02139 Phone: (617) 253-6524 Subject: AIList Digest V8 #85 To: AIList@AI.AI.MIT.EDU Status: R AIList Digest Friday, 16 Sep 1988 Volume 8 : Issue 85 Seminars: Expert Systems for Agriculture Workshop Parallel Symbolic Computing Using Multilisp The Representation of Pronouns and Definite Noun Phrases ---------------------------------------------------------------------- Date: Tue, 6 Sep 88 10:43:34 CDT From: dale@topaz.tamu.edu (A. Dale Whittaker) Subject: Expert Systems for Agriculture Workshop A first-of-its-kind workshop on the integration of expert systems with conventional problem solving techniques for agricultural problems was held in San Antonio, Texas on August 10 through 12, 1988. This workshop was supported by the American Association for Artificial Intelligence (AAAI) and by the Knowledge Systems Area of the American Society of Agricultural Engineering (ASAE). The meeting was part of the AAAI workshop series on applied topics and was focused toward agriculture. Agriculture is an area of enormous potential for appli- cations of integrated knowledge-based/conventional technolo- gies. For example, excellent databases are available for information ranging from historical weather data to indivi- dual dairy cow records. Complex simulations have been developed to describe phenomena ranging from plant growth to economic systems. These investments are a valuable asset as knowledge sources for knowledge-based decision making. The primary goals of this meeting were to: - assess the state-of-the-art of integrated systems for agriculture. - determine what factors are necessary to advance the state-of-the-art. - expose research needs and opportunities for the future. - form an interdisciplinary core of researchers for future communication and collaboration. A wide variety of research organizations were represented at the meeting including: Department of Entomology, Texas A&M University Department of Entomology, University of Massachusetts School of Computer Science, Rochester Institute of Technology Department of Statistics, North Carolina State Univer- sity Agricultural Engineering Department, Texas A&M Univer- sity Honeywell-Bull, Knowledge Engineering Services Texas Agricultural Experiment Station United States Dept. of Agri., Agricultural Research Service (Texas, Arizona, Nebraska) International Maize and Wheat Improvement Center, Cali, Columbia Animal Science Department, Oklahoma State University Department of Agricultural and Applied Economics, Univ. of Minn. Department of Agricultural Economics, Univ. of Arkansas Agricultural Engineering Department, Purdue University Institute of Food and Agricultural Sciences, Univ. of Fla. Topics presented included: The state of the Art and Future of Symbolic and Numeric Computation: Hardware Industry Viewpoint EASY-MACS: A Knowledge-based System Supporting IPM Decision Making in Apples Integrating a Knowledge-based Meat Grading System with a Voice-input Device Expert System and Conventional Programming Methods for Small Farm Planning A Blackboard Approach for Integrating Expert Systems with Conventional Problem Solving Techniques The State of the Art and Future of Symbolic and Numeric Computation: Software Industry Viewpoint The Use of Expert System Techniques and Database Files to Produce Customized Decision Aid Software COTFLEX: An Integrated Expert and Database System for Decision Support in Texas Cotton Production Use of an Expert System to Derive Pesticide Groundwater Contamination Recommendations An Expert System to Elicit Risk Preferences: The Futility of Utility Revisited Developing Integrated Decision Support Systems Using Prolog Decision Analysis as a Tool for Integrating Simulation with Expert Systems When Risk and Uncertainty are Important Farm Application of GOSSYM/COMAX Integrated Expert System for Culling Management of Beef Cows **************************************************************************** For more information concerning the workshop, contact: A. Dale Whittaker Agricultural Engineering Dept. Texas A&M University College Station, TX 77843-2117 dale@topaz.tamu.edu (409)845-8379 ------------------------------ Date: Tue, 06 Sep 88 16:35:08 EDT From: "Peter Mager" Subject: Parallel Symbolic Computing Using Multilisp The following seminar may be of interest to AI list subscribers: ACM GREATER BOSTON CHAPTER SICPLAN Thursday, September 8, 1988 8 P.M. Bolt Beranek and Newman, Newman auditorium 70 Fawcett St., Cambridge Parallel Symbolic Computing Using Multilisp Robert H. Halstead, Jr. Laboratory for Computer Science MIT Multilisp is an extension of the Lisp dialect Scheme with additional operators and additional semantics for parallel execution. The principal parallelism construct in Multilisp is the "future," which exhibits some features of both eager and lazy evaluation. Multilisp has been implemented, and runs on the shared-memory Concert multiprocessor, using as many as 34 processors. The implementation uses interesting techniques for task scheduling and garbage collection. The task scheduler helps control excessive resource utilization by means of an unfair scheduling policy; the garbage collector uses a multiprocessor algorithm modeled after the incremental garbage collector of Baker. Current work focuses on making Multilisp a more humane programming environment, on expanding the power of Multilisp to express task scheduling policies, and on measuring the properties of Multilisp programs with the goal of designing a parallel architecture well tailored for efficient Multilisp execution. The talk will briefly describe Multilisp, discuss the areas of current activity, and outline the direction of the Multilisp project with special attention to the areas of task scheduling and architecture design. ------------------------------ Date: Tue 13 Sep 88 15:58:37-EDT From: Marc Vilain Subject: The Representation of Pronouns and Definite Noun Phrases BBN Science Development Program AI Seminar Series Lecture THE REPRESENTATION OF PRONOUNS AND DEFINITE NOUN PHRASES IN LOGICAL FORM Mary P. Harper Brown University Computer Science Dept. (MPH%cs.brown.edu@RELAY.CS.NET) BBN Labs 10 Moulton Street 2nd floor large conference room 10:30 am, Thursday September 15 Initially, I will discuss the representation of pronouns in logical form. Two factors influence the representation of pronouns. The first factor is computational. This factor imposes certain requirements on the logical form representation of a pronoun. For example, the initial representation of a pronoun in logical form should be derivable before its antecedent is known. The antecedent, when determined, should be specified in a way consistent with the initial representation of the pronoun. The second factor is linguistic. This factor requires that the representation for a pronoun should be capable of expressing the range of behaviors of a pronoun in English, especially in the domain of verb phrase ellipsis. I will review past models of verb phrase ellipsis. These models do not provide a representation of pronouns for computational purposes, and accordingly fail to meet our computational requirements. Additionally, I will show that these models fail to represent pronouns in a way which captures the full range of behaviors of pronouns. I will then propose a new representation for pronouns and show how this representation meets our computational requirements while providing a better model of pronouns in verb phrase ellipsis. The representation of definite noun phrases will also be discussed. As in the case of pronouns, there are two factors which influence this representation (i.e. modeling definite behavior and obeying our computational guidelines). I will discuss several examples which argue for representing definites as functions in logical form before pronoun resolution is carried out. I will discuss the actual representation I chose, and illustrate its use with an example. ------------------------------ End of AIList Digest ********************