index int64 0 18.8k | text stringlengths 0 826k | year stringdate 1980-01-01 00:00:00 2024-01-01 00:00:00 | No stringlengths 1 4 |
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1,100 | tation of M ion Syste Steve Kuo and Dan Moldovan skuo@gringo.usc.edu and moldovan@gringo.usc.edu DRB-363, (213) 740-9134 Department of Electrical Engineering - Systems University of Southern California Los Angeles, California 90089- 1115 Abstract The performance of production p... | 1991 | 42 |
1,101 | IXM2: A Parallel Associative Processor for Knowledge Tetsuya Higuchil, Hiroaki Kitano2, Tatsurni Furuya’, Ken-ichi Handal, Naoto Takahashi3, Akio Kokubul Electrotechnical Laboratory’ Carnegie Mellon University2 University of Tsukuba3 l- 1-4 Umezono, Tsukuba, Center for... | 1991 | 43 |
1,102 | Ian Green Department of Engineering University of Cambridge Cambridge CB2 1PZ England img@eng.cam.ac.uk Abstract The problem of automatically improving func- tional programs using Darlington’s unfold/fold technique is addressed. Transformation tactics are formali... | 1991 | 44 |
1,103 | Conditional Existence of Variables in Generalized Constraint Networks* James Bowentand Dennis Bahlerz Dept. of Computer Science Box 8206, North Carolina State University Raleigh, NC 27695-8206 Abstract Classical constraint systems require that the set of variables which exist in a problem ... | 1991 | 45 |
1,104 | Laboratoire d’Informaticlue, de Robotique et de Micro&ctronique de 860, rue de Saint Priest 34090 Montpellier PRANCE Email: bessiere@xim.crim.fk Abstract Constraint satisfaction problems (CSPs) provide a model often used in Artificial Intelligence. Since the problem of the existence of a solu... | 1991 | 46 |
1,105 | Eugene 6. Freuder Department of Computer Science University of New Hampshire Durham, NH 03824 ecf@cs.unh.edu Abstract Constraint satisfaction problems (CSPs) involve finding values for variables subject to constraints on which combinations of values are permitted. This paper... | 1991 | 47 |
1,106 | Wrard Ligozat LIMSI, Universite Paris-Sud, B.P. 133 91403 Orsay Cedex, France ligozat@ limsifr Abstract The calculus of time intervals defined by Allen has been extended in various ways in order to accomodate the need for considering other time objects than convex intervals (eg.time points and i... | 1991 | 48 |
1,107 | Combinin ualitative and Quantitative strai s in Temporal Reasoning* Itay Meiri Cognitive Systems Laboratory Computer Science Department University of California, Los Angeles, CA 90024 itay@cs.ucla. edu Abstract This paper presents a general model for tempo- ral reasoning, ... | 1991 | 49 |
1,108 | Hinrichs and Janet slodner School of Information and Computer Science Georgia Institute of Technology Atlanta, Georgia 30332 Abstract Many design tasks have search spaces that are vague and evaluation criteria that are subjective. We present a model of design that can solve such prob... | 1991 | 5 |
1,109 | Integrating etrie a alitativ TernDora ‘4. easoning Henry A. Kautz AT&T Bell Laboratories Murray Hill, NJ 07974 kautz@research.att.com Abstract Research in Artificial Intelligence on constraint-based representations for temporal reasoning has largely con- centrated on two kinds ... | 1991 | 50 |
1,110 | ra ease Fei Song and Robin Cohen Dept. of Computer Science, Univ. of Waterloo Waterloo, Ontario, Canada N2L 3Gl {fsong,rcohen}@watdragon.uwaterloo.ca Abstract This paper presents a strengthened algorithm for tem- poral reasoning during plan recognition, which im- proves on a... | 1991 | 51 |
1,111 | ric ts Q 0 Massimo Poe&* Computer Science Department AT&T Bell Laboratories University of Rochester Rochester, NY 14627 poesio@cs.rochester.edu Abstract Reasoning about one’s personal schedule of appoint- ments is a common but surprisingly complex activ- ity. ... | 1991 | 52 |
1,112 | gie 0 Dept of Computer Sciences and Center for Cognitive Science University of Texas University of Texas Austin, TX 78712-1188 Austin, TX 78712 USA USA Abstract Know-how is an important concept in Artificial Intelligence. It has been argued previously that it cannot be succe... | 1991 | 53 |
1,113 | rova ngzhen Ein Yoav Shoham Computer Science Department Stanford University lin@cs.stanford.edu shoham@cs.stanford.edu Abstract Research on nonmonotonic temporal reason- ing in general, and the Yale Shooting Prob- lem in particular, has suffered from the ab- sence of a criterion against... | 1991 | 54 |
1,114 | of tasks that have in AI-notably planning and prediction, diagnosis and explanation. Recently it has become an ob- ject of study in its own right, drawing inspiration from the work of philosophers and logicians as well as more immediately AI-oriented concerns. In this paper I shall examine just one... | 1991 | 55 |
1,115 | A Logic and Time Nets for Probabilistic Inference Keiji Kanazawa* Department of Computer Science Brown University, Box 1910 Providence, RI 02912 kgk@cs.brown.edu Abstract In this paper, we show a new approach for reason- ing about time and probability that combines a f... | 1991 | 56 |
1,116 | efaul ositiona ic * Rachel Ben-Eliyahu < rachel@cs.ucla.edu > Cognitive Systems Laboratory Computer Science Department University of California Los-Angeles, California 90024 Abstract We present a mapping from a class of default theories to sentences in propositional ... | 1991 | 57 |
1,117 | Some Variations 0 efault Logic Institute of Computer Science Polish Academy of Sciences PKiN, 00-901 Warsaw, POLAND Abstract In the following paper, we view applying default rea- soning as a construction of an argument supporting agent’s beliefs. This yields a slight reformulation of th... | 1991 | 58 |
1,118 | Michael Gelfond Computer Science Department University of Texas at El Paso El Paso, Texas 79968 cvOO@utep.bitnet Abstract The purpose of this paper is to expand the syntax and semantics of logic programs and deductive databases to allow for the correct representatio... | 1991 | 59 |
1,119 | Prototype-Based Reasoning: An Integrated Approach to Solving Large Novel Shankar A. Rajamoney Computer Science Department University of Southern California Los Angeles, CA 90089 Abstract Two important computational approaches to problem solving are model... | 1991 | 6 |
1,120 | The P-Systems: A Systematic Classification of Logics of Nonmonotonicity Wolfgang Nejdl* Technical University of Vienna Paniglgasse 16, A-1040 Vienna, Austria e-mail: nejdl@vexpert.dbai.tuwien.ac.at Abstract In the last years many logics of nonmonotonicity have been developed using various ... | 1991 | 60 |
1,121 | Default Reasoning From Statistics Fahiem Bacchus* Department of Computer Science University of Waterloo Waterloo, Ontario, Canada N2L-3Gl fbacchus@logos. waterloo.ca Abstract There are two common but quite distinct interpreta- tions of probabilities: they can be interpreted as a mea- ... | 1991 | 61 |
1,122 | Incorporating Nonmonotonic easoning i lause Theories James P. Delgrande School of Computing Science Simon Fraser University Burnaby, B.C. Canada Abstract An approach for introducing default reasoning into first-order Horn clause theories is described. A default theory is exp... | 1991 | 62 |
1,123 | Step-logic and the Three-wise- Department of Computer Science and Engineering College of Engineering and Applied Sciences Arizona State University Tempe, AZ 85287-5406 drapkinQenws92.eas.asu.edu Abstract The kind of resource limitation that is most evident in commonsense reasoners is the passage... | 1991 | 63 |
1,124 | Mois& Goldszmidtt Judea Pearl < moises@cs.ucla.edu > < judea@cs.ucla.edu > Cognitive Systems Laboratory, Computer Science Department, University of California, Los Angeles, CA 90024 Abstract We develop a formalism for reasoning with de- faults that are e... | 1991 | 64 |
1,125 | king t e ecution Jeffrey A. Barnett Northrop Research and Technology Center One Research Park Palos Verdes Peninsula, CA 90274 jbarnett@nrtc.northrop.com Abstract How should opinions of control knowledge sources be represented and combined? These issues are addressed for the case whe... | 1991 | 65 |
1,126 | Abstract While every Shafer belief function corresponds to a set of interval beliefs on the atoms of the frame of discernment, an arbitrarily specified set of intervals of belief may not correspond to any belief function, even when it does correspond to bounds imposed by sets of probability functio... | 1991 | 66 |
1,127 | Explanation, rrelevanee and endence * Solomon E. Shimony Computer Science Department Box 1910, Brown University Providence, RI 02912 ses@cs.brown.edu Abstract We evaluate current explanation schemes. These are either insufficiently general, or suffer from other seri- ous dra... | 1991 | 67 |
1,128 | gic morphisms as a work for backward tr r ofle some modal and epistemic logics Ricardo cafema, Stiphme Denwi, Michel Herment LFIA-IMAG, 46, Av. Felix Wallet, d strategies in 38031 Grenoble Cedex, FRANCE {caferra 1 stephme 1 herment}@lifia.imag.fi (uucp) There exist methods in automated the... | 1991 | 68 |
1,129 | Mechanization of Analytic Reasoning about Sets Alan F. McMichael AT&T Bell Laboratories 480 Red Hill Rd., lA-214, Middletown, NJ 07748 Abstract Resolution reasoners, when applied to set theory problems, typically suffer from “lack of focus.” MARS is a program that attempts to rectify this ... | 1991 | 69 |
1,130 | Kalla ARES Laboratory Department of Computational Science University of Saskatchewan Saskatoon, CANADA S7N OWO Abstract Student modelling is not typically concerned with representing the deep mental models a student employs in dealing with the world around him/her. In ... | 1991 | 7 |
1,131 | ristie Eugene Charniak and Saadia Husain Department of Computer Science Brown University Box 1910, Providence RI 02912 Abstract Finding best explanations is often formalized in AI in terms of minimal-cost proofs. Finding such proofs is naturally characterized as a best-fi... | 1991 | 70 |
1,132 | Depth-First vs Nageshwara Rae Vempaty Vipin Kumar* ichard IL orft Dept. of Computer Sciences, Computer Science Dept., Dept. of Computer Science, Univ. of Central Florida, Univ. of Minnesota, Univ. of California, Orlando, FL - 32792. Minneapolis, MN - 554... | 1991 | 71 |
1,133 | Is there c Matthew L. Ginsberg* and Donald I?. Geddist Computer Science Department Stanford University Stanford, California 94305 ginsberg@cs.stanford.edu No. Abstract 1 Introduction The split between base-level and metalewel knowledge has long been recognized by the declarativ... | 1991 | 72 |
1,134 | ee Searches eiger and Jeffrey A. Barnett Northrop Research and Technology Center One Research Park P alos Verdes , CA 90274 Abstract We provide an algorithm that finds optimal search strategies for AND trees and OR trees. Our model includes three outcomes when a node is explore... | 1991 | 73 |
1,135 | Concept Languages as uery Languages Maurizio Lenzerini, Andrea Schaerf Dipartimento di Informatica e Sistemistica Universita di Roma “La Sapienza” via Salaria 113, 00198 Roma, Italia Abstract We study concept languages (also called terminological languages) as means for both defining a kno... | 1991 | 74 |
1,136 | Deduction as Parsing: in the KL-ONE Framework Marc Vilain The Mitre Corporation Burlington Road, Bedford, MA 01730 Internet: MBV@ATRE.ORG Abstract This paper presents some complexity results for deductive recognition in the framework of languages such as KL-ONE. In particular, it focuses on c... | 1991 | 75 |
1,137 | Knowledge Science Institute University of Calgary Calgary, Alberta, Canada T2N lN4 gaines@cpsc.ucalgary.ca This paper addresses the integration of services for rule- based reasoning in knowledge representation servers based on term subsumption languages. AS an alternative to ... | 1991 | 76 |
1,138 | C eason Maintenance and Inference Control for Constraint Propagation over Intervals Walter Hamscher Price Waterhouse Technology Centre 68 Willow Rd, Menlo Park, CA 94025 Abstract ACP is a fully implemented constraint propagation system that computes nume... | 1991 | 77 |
1,139 | Wwee Tou Ng Raymond J. Mooney Department of Computer Sciences University of Texas at Austin Austin, Texas 78712 htng@cs.utexas.edu, mooney@cs.utexas.edu Abstract This paper presents an algorithm for first-order Horn-clause abduction that uses an ATMS to avoid redundant computation. This... | 1991 | 78 |
1,140 | ity reasoning in a T -based analog diagnosis system David Jerald Goldstone* MIT A.I. La.l>ora.tory autl Xcros I’AHC’ 15.5 ulcst’u~lt~ St.. 3io11t clair. N.J O’iO-42 stoue(,lai.nlit,.etlu Abstract A system, called 5’kordo.s. has been implernent.etl for model-lmsed cliagilo... | 1991 | 79 |
1,141 | telli yacinth S. Nwana ri Department of Computer Science University of Liverpool P. 0. Box 147, Liverpool L69 3B.X, U.K. nwanahs@and.cs.liv.ac.uk Abstract This paper presents FITS - an Intelligent Tutoring System (ITS) for the domain of addition of fractions. It was developed with th... | 1991 | 8 |
1,142 | e er- of Versi ss 9 * (Extended Abstract) Carl A. Gunter Teow-Min Ngair University of Pennsylvania University of Pennsylvania 1. Introduction This paper arose out of the observation that the ver- sion space algorithm and the ATMS label-update algo- rithms operate on very simila... | 1991 | 80 |
1,143 | structive Induction on James I?. Calllan and nformation* Department of Computer and Information Science, University of Massachusetts 9 Amherst, Massachusetts 01003 callan@cs.umass.edu, utgoff@cs.umass.edu Abstract It is well-known that inductive learning algorithms ... | 1991 | 81 |
1,144 | Department of Computer and Information Science University of Massachusetts Amherst, MA 01003 U.S.A. Abstract This paper identifies two fundamentally different kinds of training information for learning search control in terms of an evaluation function. Each kind of traini... | 1991 | 82 |
1,145 | exity si Steven D. Whitehead Department of Computer Science University of Rochester Rochester, NY 14627 email: white@cs.rochester.edu Abstract Reinforcement learning algorithms, when used to solve multi-stage decision problems, perform a kind of online (inc... | 1991 | 83 |
1,146 | tiv obert Levinsoln and Department of Computer and Information Sciences University of California Santa Cruz Santa Cruz, CA 95064 levinsonQcis.ucsc.edu and snyder@cis.ucsc.edu Abstract Psychological evidence indicates that human chess players base their assessments of c... | 1991 | 84 |
1,147 | ANALYSIS OF THE INTERNAL NEURAL NETWORKS FOR MA &Wan CHAN Computer Science Department The Chinese University of Hong Kong Shatin, N.T., Hong Kong email : lwchan@cucsd.cuhk.hk (bitnet) Abstract The internal representation of the training pat- terns of multi-layer ... | 1991 | 85 |
1,148 | ir s Lorien U. Pratt and Computer Science Department Rutgers University New Brunswick, NJ 08903 Abstract A touted advantage of symbolic representations is the ease of transferring learned information from one intelligent agent to another. This paper investigates an analogous problem: ho... | 1991 | 86 |
1,149 | Error-Correct A General Metho Multiclass Inductive Thomas 6. ietterieh and Ghulum Bakiri Department of Computer Science Oregon State University Corvallis, OR 97331-3202 Abstract Multiclass learning problems involve finding a defini- tion for an unknown f... | 1991 | 87 |
1,150 | LE LEARNING Dept. of Computer and Information University of Florida Gainesville, Florida 32611 Sciences Abstract If the backpropagation network can produce an infer- ence structure with high and robust performance, then it is sensible to extract rules from it. The KT a... | 1991 | 88 |
1,151 | Craig noblock Carnegie Mellon University School of Computer Science Pittsburgh, PA 15213 cak&s.cmu.edu Steven Minton Sterling Federal Systems NASA Ames Research Center Mail Stop: 244-17 Moffett Field, CA 94035 minton@pluto.arc.nasa.gov rew Etaioni University ... | 1991 | 89 |
1,152 | Joi3 Courtois Institut Sup6rieu.r d’Electronique de Paris 21, rue d’Assas 75270 Paris cedex Q6 PRANCE and LAFORIA, Universite PARIS VI 4, place Jussieu 75252 Paris cedex 05 PRANCE Abstract This paper shows how a new approach in the use of AI techniques has been successfully used for the... | 1991 | 9 |
1,153 | STATIC ace Oren Etzioni Department of Computer Science and Engineering, FR-35 University of Washington Seattle, WA 98195 etzioni@cs.washington.edu Abstract Explanation-Based Learning (EBL) can be used to sig- nificantly speed up problem solving. Is there sufficie... | 1991 | 90 |
1,154 | pingStone: irid an nalyt ica David Ruby and Dennis Kibler Department of Information & Computer Science University of California, Irvine Irvine, CA 92717 U.S.A. druby@ics.uci.edu Abstract Decomposing a difficult problem into simpler subprob- lems is a classic p... | 1991 | 91 |
1,155 | Sanjay Bhansali and Mehdi T. Harandi Department of Computer Science University of Illinois at Urbana-Champaign 1304 W. Springfield Avenue, Urbana, IL 61801 bhansali@cs.uiuc. edu harandi@cs.uiuc. edu Abstract The feasibility of derivational analogy as a mechanism for ... | 1991 | 92 |
1,156 | Mark Derthick MCC 3500 West Balcones Center Drive Austin, TX 78759 derthick@mcc.com Abstract This paper discusses unsupervised learning of orthogo- nal concepts on relational data. Relational predicates, while formally equivalent to the features of the concept- ... | 1991 | 93 |
1,157 | avid -IV. Aha Department of Information and Computer Science The Turing Institute University of California, Irvine Irvine, CA 92717 U.S.A. albertQics.uci.cdu 36 North Flanover Street Glasgow Gl 2AD Scotland aha@Xuring.ac.uk Abstract This paper presents PAC-learning ... | 1991 | 94 |
1,158 | ussein Almuallim and Thomas G. ietterich 303 Dearborn Hall Department of Computer Science Oregon State University Corvallis, OR 97331-3202 almualhQcs.orst.edu tgd@cs.orst.edu Abstract In many domains, an appropriate inductive bias is the MIN-FEATURES bias, which prefers c... | 1991 | 95 |
1,159 | arity and Str Alexander Botta New York University - Courant Institute of Mathematical Sciences Author’s current address: I310 Dickerson Road, Teaneck, N.J. 07666 Abstract We present an approach to unsupervised concept formation, based on accumulation of partial regularities. Using an algo- rithm... | 1991 | 96 |
1,160 | An Algo acking John Woodfill and Ramin Zabih Computer Science Department St anford University Stanford, California 94305 Abstract We describe an algorithm for tracking an unknown ob- ject in natural scenes. We require that the object’s approximate ini... | 1991 | 97 |
1,161 | AUTOMATIC R. L. Cromwell and A. C. Kak Robot Vision Lab School of Electrical Engineering, Purdue University West Lafayette IN 47907 USA cromwell@ecn.purdue.edu, kak@ecn.purdue.edu Abstract Object recognition requires complicated domain- specific rules. For many problem domains,... | 1991 | 98 |
1,162 | The Geometry of Visual C at Jean-Yves Her& Rajeev Sharma eter Cucka Computer Vision Laboratory, Center for Automation Research and Department of Computer Science University of Maryland, College Park, MD 20742 herve@cvl.urnd.edu rajeev@cvl.urnd.edu cuc... | 1991 | 99 |
1,163 | Gary C. Borchardt Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge, MA 02139 Abstract This paper introduces the causaZ reconstructa’on task- the task of reading a causal description of a phys- ical system, forming an internal model ... | 1992 | 1 |
1,164 | Integrating Planning and Reacting Architecture for Controlling Erann Gat Jet Propulsion Lab, California Institute of Technology 4800 Oak Grove Drive Pasadena, California 9 1109 gat@robotics.jpl.nasa.gov ABSTRACT This paper presents a heterogeneous, asynchronous architecture for contr... | 1992 | 10 |
1,165 | James M. Crawford avid W. Etherington AI Principles Research Department AT&T Bell Laboratories 600 Mountain Ave. Murray Hill, NJ 07974-0636 {jc,ether}@research.att.com Abstract The development of a formal logic for reasoning about change has proven to be surprisingly dif- ficult. Furthe... | 1992 | 100 |
1,166 | 584 Representation and Reasoning: Action and Change Alvaro de1 Val Robotics Lab Stanford University Stanford, CA 94305 delval@scottie.stanford.edu Abstract Two areas that have attracted much interest in recent years, belief update and reasoning about ... | 1992 | 101 |
1,167 | Concurrent Actions in t Fang&en Lin and Uoav Shoharn Department of Computer Science Stanford University Stanford, CA 94305 Abstract We propose a representation of concurrent actions; rather than invent a new formalism, we model them within the standard situation calculus by in- trodu... | 1992 | 102 |
1,168 | Nonmonotonic orts for Feature Structures Mark A. Young Artificial Intelligence Laboratory The University of Michigan 1101 Beal Ave. Ann Arbor, MI 48109 marky@caen.engin.umich.edu Abstract There have been many recent attempts to incorpo- rate defaults into unification-based grammar... | 1992 | 103 |
1,169 | ilXt~OS~QCtiVQ bQliQf Kurt Konolige* Artificial Intelligence Center SRI International 333 Ravenswood Avenue Menlo Park, CA 94025 konolige@ai.sri.com Abstract Autoepistemic (AE) logic is a formal system character- izing agents that have complete introspective access to ... | 1992 | 104 |
1,170 | A Belief-Function Logic Alessandro Saffiotti* IRIDIA - Universite Libre de Bruxelles 50 av. F. Roosevelt - CP 19416 - 1050 Bruxelles - Belgium asaffio@ulb.ac.be Abstract We present BFL, a hybrid logic for representing uncertain knowledge. BFL attaches a quantified notion of belief - ... | 1992 | 105 |
1,171 | g Circ odal Logic Jacques Wainer Dept of Computer Science University of Colorado Boulder, CO 80309-0430 wainer@cs.colorado.edu Abstract This paper discusses the logic LKM which extends circumscription into an epistemic domain. This extension will allow us to define circumscr... | 1992 | 106 |
1,172 | From Statistics to Beliefs* Fahiem Bacchus Adam Grove Computer Science Dept. University of Waterloo Waterloo, Ontario Canada, N2L 3G1 fbacchus@logos.waterloo.edu Computer Science Dept. Stanford University Stanford, CA 943005 grove@cs.stanford.edu Abstract An intelligent agen... | 1992 | 107 |
1,173 | A Logic for evision a &iv eri Craig Boutilier Department of Computer Science University of British Columbia Vancouver, British Columbia CANADA, V6T 122 email: cebly@cs.ubc.ca Abstract We present a logic for belief revision in which revision of a theory b... | 1992 | 108 |
1,174 | Lexical Imprecision in zzy Constraint Networks James Bowen, Robert Lai and Dennis Bahler Department of Computer Science North Carolina State University Raleigh, NC 27695-8206 jabowen@adm.csc.ncsu.edu, lai@jim.csc.ncsu.edu, drb@adm.csc.ncsu.edu Abstract We define fuzzy constr... | 1992 | 109 |
1,175 | Landmark- obot Nav Anthony Lazanas Jean-Claude Latombe lazanas@flamingo.stanford.edu latombe@cs.stanford.edu Robotics Laboratory Department of Computer Science, Stanford University Stanford, CA 94305, USA Abstract To operate in the real world robots must deal with ... | 1992 | 11 |
1,176 | A Symbolic Generalization eory Adnan Y. Darwiche and Matthew E. Ginsberg Computer Science Department Stanford University Stanford, CA 94305 Abstract This paper demonstrates that it is possible to re- lax the commitment to numeric degrees of be- lief while retaining the... | 1992 | 110 |
1,177 | ogic of Knowle ge and Belief for * e Preliminary Report Piotr J. Grnytrasiewics and Edmund H. Durfee Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor, Michigan 48109 Abstract To make informed decisions in a multiagent en- ... | 1992 | 111 |
1,178 | Generating Kevin D. Ashley and Vincent Aleven University of Pittsburgh Intelligent Systems Program, School of L aw, and Learning Research and Development Center Pittsburgh, Pennsylvania 15260 Abstract We identify and illustrate five important kinds of Di- alectical Examples, standard co... | 1992 | 112 |
1,179 | Common Sense A. Julian Graddock University of B.C. Vancouver, B.C., V6T lW5 craddock@cs.ubc.ca Abstract An important and readily available source of knowledge for common sense reasoning is partial descriptions of specific experiences. Knowledge bases (KBs) containing such in- formati... | 1992 | 113 |
1,180 | aniel @. Edelson Institute for the Learning Sciences Northwestern University Evanston, IL 60208 stract Case-based teaching systems, like good human teachers, tell stories in order to help students learn. A case-based teaching system engages a student in a challenging task and monitors his ac-... | 1992 | 114 |
1,181 | ased Case A Victoria University of Wellington Wellington, New Zealand eric.jones@cornp.vuw.ac.nz Abstract In this paper, we demonstrate an important role for model-based reasoning in case adaptation. Model-based reasoning can allow a case-based rea- soner to apply cases to a wider range of... | 1992 | 115 |
1,182 | ualitative ase e* 23. Cui, A.G. Cohn and D.A. Randell Division of Artificial Intelligence School of Computer Studies University of Leeds, Leeds, LS2 9JT, England { cui,agc,dr}@dcs.leeds.ac.uk Abstract We describe an envisionment-based qualitative simulation program. ... | 1992 | 116 |
1,183 | Qualitative Reasoning Group The Institute for the Learning Sciences Northwestern University 1890 Maple Avenue, Evanston, IL, 60201 Abstract Qualitative reasoners have been hamstrung by the in- ability to analyze large models. This includes self- ex... | 1992 | 117 |
1,184 | Gordon Skorstad Beckman Institute, University of Illinois 405 North Mathews Street Urbana, Illinois 61801 g-skorstad@uiuc.edu bstraet Choosing between multiple ontological perspec- tives is crucial for reasoning about the physical world. Choosing the wrong perspective can make a reasoning tas... | 1992 | 118 |
1,185 | alitative cture of a sse Randall N. Wilson* Jean-Claude Latornbe rwilson@cs.stanford.edu latombe@cs.stanford.edu Robotics Laboratory Department of Computer Science, Stanford University Stanford, CA 94305, USA Abstract A mechanical assembly is usually described by th... | 1992 | 119 |
1,186 | eactive N igat io Experimenta David P. Miller, Rajiv S. Desai, Erann Ivlev and John Loch Jet Propulsion Laboratory / California Institute of Technology 4800 Oak Grove Drive Pasadena, CA 91109 Abstract This paper describes a series of experiments that were performed on the Rocky III r... | 1992 | 12 |
1,187 | Palo Alto, CA 94304. nayakBcs.stanford.edu Abstract Adequate problem representations require the identification of abstractions and approximations that are well suited to the task at hand. In this paper we introduce a new class of approximations, called cuusal approximations, that are comm... | 1992 | 120 |
1,188 | P. Pandurang Nayak* Leo Joskowicz Sanjaya Addanki Knowledge Systems Lab., IBM, Watson Res. Ctr., IBM, Watson Res. Ctr., 702 Welch Road, Bldg. C, P.O. Box 704, P.O. Box 704, Palo Alto, CA 94304. Yorktown Heights, NY 10598. Yorktown Heights, NY 10598. Abstract Effective reasoning ab... | 1992 | 121 |
1,189 | harat Rae and Stephen C-Y. Knowledge-Based Engineering Systems Laboratory University of Illinois at Urbana-Champaign Ahtract This paper discusses discovery of mathemati- cal models from engineering data sets. KEDS, a Knowledge-based Equation Discovery System, identifies s... | 1992 | 122 |
1,190 | radley L. Dept. of Computer Sciences University of Texas Austin, Texas 787 12 bradley@cs.utexas.cdu Dept. of Artificial Intelligence University of Edinburgh 80 South Bridge Edinburgh EHl 1 HN Scotland, UK inak@ai.cd.ac.uk ers Dept. of Computer Sciences U... | 1992 | 123 |
1,191 | Torn Bylander Laboratory for Artificial Intelligence Research Department of Computer and Information Science The Ohio State University Columbus, Ohio 43210 email: byland@cis.ohio-state.edu Abstract Korf (1985) presents a method for learning macro- operators and shows t... | 1992 | 124 |
1,192 | Technical University Vienna, Christian Doppler Laboratory for Expert Systems, Paniglgasse 16 A- 1040 Vienna, Austria email: dom@vexpert.dbai.tuwien.ac.at Abstract Temporal reasoning is widely used in AI, especially for natural language processing. Existing methods for temporal reasoning... | 1992 | 125 |
1,193 | artin charles Gchmbic IBM Israel Scientific Center Technion City, Haifa, Israel; and Bar-Ilan University, Ramat Gan, Israel on Sl-mmir Computer Science Dept. Tel Aviv University Tel-Aviv 69978, Israel golumbic@israearn.bitnet shami&math.tau.ac.il Interval consistency problems deal with ... | 1992 | 126 |
1,194 | e Computation n! ral Projection an * Bernhard Nebel German Research Center for Artificial Intelligence (DFKI) Stuhlsatzenhausweg 3 D-6600 Saarbriicken 11, Germany nebel@dfki.uni-sb.de Abstract One kind of temporal reasoning is temporal projection-the computation of ... | 1992 | 127 |
1,195 | rose Robert Dianne, Eric Mays, Frank J. Oles IBM T. J. Watson Research Center P.0. Box 218 Yorktown Heights, NY 10598 Abstract In this paper, we propose a new approach to inten- sional semantics of term subsumption languages. We introduce concept algebras, whose signatures are given ... | 1992 | 128 |
1,196 | Jochen ernhard Nebel, an German Research Center for Artificial Intelligence (DFKI) Stuhlsatzenhausweg 3 W-6600 Saarbriicken, Germany e-mail: (last name)@dfki.uni-sb.de rofitlich Abstract The family of terminological representation sys- tems has its roots in the representation system KL-... | 1992 | 129 |
1,197 | School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213-3890 Robert.Doorenbos@CS.CMU.EDU, Milind.Tambe@CS.CMU.EDU, and Allen.Newell@CS.CMU.EDU Abstract This paper describes an initial exploration into large learning systems, i.e., systems that learn a large number of ... | 1992 | 13 |
1,198 | Reeognit ion Algorithms obert M. MacGregor and avid rill USC/Information Sciences Institute 4676 Admiralty Way Marina de1 Rey, CA 90292 macgregor@isi.edu, brill@isi.edu Abstract Most of today’s terminological representation sys- tems implement hybrid reasoning architectures ... | 1992 | 130 |
1,199 | Computing Least Common Subsumers in Description Logics William W. Cohen Alex Borgida* Haym Hirsh AT&T Bell Laboratories Dept. of Computer Science Dept. of Computer Science 600 Mountain Avenue Rutgers University Rutgers University Murray Hill, NJ 07974 New Brunswick, NJ 08... | 1992 | 131 |
Subsets and Splits
SQL Console for Seed42Lab/AI-paper-crawl
Finds papers discussing interpretability and explainability in machine learning from after 2010, offering insight into emerging areas of research focus.
Interpretability Papers Since 2011
Reveals papers from the AAAI dataset after 2010 that discuss interpretability or explainability, highlighting key research in these areas.
SQL Console for Seed42Lab/AI-paper-crawl
Searches for papers related to interpretability and explainability in NIPS proceedings after 2010, providing a filtered dataset for further analysis of research trends.
AI Papers on Interpretability
Finds papers discussing interpretability or explainability published after 2010, providing insight into recent trends in research focus.
ICML Papers on Interpretability
Retrieves papers from the ICML dataset after 2010 that mention interpretability or explainability, offering insights into trends in model transparency research.
ICLR Papers on Interpretability
Retrieves papers from the ICLR dataset that discuss interpretability or explainability, focusing on those published after 2010, providing insights into evolving research trends in these areas.
ICCV Papers on Interpretability
Finds papers from the ICCV dataset published after 2010 that discuss interpretability or explainability, providing insight into trends in research focus.
EMNLP Papers on Interpretability
Retrieves papers related to interpretability and explainability published after 2010, providing a focused look at research trends in these areas.
ECCV Papers on Interpretability
The query retrieves papers from the ECCV dataset related to interpretability and explainability published after 2010, providing insights into recent trends in these research areas.
CVPR Papers on Interpretability
Retrieves papers from the CVPR dataset published after 2010 that mention 'interpretability' or 'explainability', providing insights into the focus on these topics over time.
AI Papers on Interpretability
Retrieves papers from the ACL dataset that discuss interpretability or explainability, providing insights into research focus in these areas.