Split (11)

AAAI · 18.8k rows

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
2,000	Situation Calculus on A Dense Flow of Time In this uation Akira F’usaoka Department of Computer Science Ritsumeikan University 1916,Nojicho,Kusatsu-city,SIGA,525,JAPAN fusaoka@cs.ritsumei.ac.jp Abstract paper, we attempt to reconstruct the sit- calculus on a dense flow...	1996	94
2,001	Reasoning about Continuous Christoph S. IIerrmann FG Intellektik, TH Darmstadt Alexanderstr. 10, D-64283 Darmstadt Abstract Overcoming the disadvantages of equidistant dis- cretization of continuous actions, we introduce an ap- proach that separates time into slices of varying len...	1996	95
2,002	Splitting a efault Theory Hudson Turner Department of Computer Sciences University of Texas at Austin Austin, TX 78712-1188, USA hudson@cs.utexas.edu Abstract This paper presents mathematical results that can sometimes be used to simplify the task of ...	1996	96
2,003	Formalizing Narratives using Nested Circumscription Chitta Baral, Alfred0 Gabaldon and Alessandro ProvettiS *Department of Computer Science University of Texas at El Paso El Paso, Texas 79968 U.S.A. { chitta,alfredo} @cs.utep.edu Abstract The rep...	1996	97
2,004	Reasoning about Nondeterministic and Concurrent Actions: A Process Algebra Approach In this paper, we study reasoning about actions fol- lowing a model checlcing approach in contrast to the usual validity checlcing one. Specifically, we model a dynamic ...	1996	98
2,005	On the Range of Applicability of Baker’s Approach to the Frame Problem G. Neelakantan Kartha Honeywell Technology Center 3600 Technology Drive Minneapolis, Minnesota 55418 kartha@src.honeywell.com Abstract We investigate the range of applicability of Baker’s ap- proach to th...	1996	99
2,006	Modeling Emotions and ther Motivations in Synthetic Agents Juan . Velkquez MIT Artificial Intelligence Laboratory 545 Technology Square, NE43-812, Cambridge, MA 02139 jvelas@ai.mit.edu Abstract We present Cathexis, a distributed, computational model which offers an alternative approach to ...	1997	1
2,007	Symbolic Nearest Mean Classifiers Piew Datta and Dennis Kibler Department of Information and Computer Science University of California Irvine, CA 92717 {pdatta, kibler}@ics.uci.edu Abstract The minimum-distance classifier summarizes each class with a prototype...	1997	10
2,008	Local Search Algorithms for Partial MAXSAT Byungki Cha y Kazuo Iwama , Yahiko Mambayashi** and Shuichi Miyazaki* *Department of Computer Science Kyushu University Fukuoka 812, Japan {cha, iwama, shuichi)@csce.kyushu-u.ac.jp Abstract MAXSAT solutions, i.e., near-satisfying ...	1997	100
2,009	Solving Linear Pseudo-Boolean Co Joachim R Walses Programming Systems Lab Universitgt des Saarlandes, Postfach 15 1150 6604 1 Saarbriicken, Germany walser@ps.uni-sb.de Abstract Stochastic local search is one of the most successful methods for model finding in propositional satisfiability. ...	1997	101
2,010	Tabu Search for SAT Bertrand Mamre Lakhdar Sails lhic Gr6goire CRIL - Universite d’Artois rue de l’universite SP 16 F-62307 Lens Cedex France {mazure,sais,gregoire}@cril.univ-artois.fr Abstract In this paper, tabu search for SAT is investi- gated from an experimental point of view. T...	1997	102
2,011	Variable-Selection Heuristics in Local Search for SAT Alex S. Fukunaga* Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Drive, MS 525-3660 Pasadena, CA 91109-8099 alex.fukunaga@jpl.nasa.gov Abstract One of the important components o...	1997	103
2,012	Models of Continual Co Eric Horvitz Microsoft Research Redmond, Washington 98025 horvitz@microsoft.com Abstract Automated problem solving is viewed typically as the expenditure of computation to solve one or more prob- lems passed to a reason...	1997	104
2,013	Complex Goal Criteria and Its Application in Design-to-Criteria Scheduling *t Thomas Wagner Alan Garvey Victor Lesser Computer Science Department University of Massachusetts Department of Computer Science Truman State University Computer Science Department University of Massachusetts ...	1997	105
2,014	CS Dept Stanford University Stanford, CA 94305-90 10 getoor@cs.stanford.edu CS Dept Uppsala University Box 311, S-751 05 Uppsala, Sweden greger@csd.uu.se Abstract The use of heuristics as a means to improve constraint solver performance has been researched widely. However, m...	1997	106
2,015	AngeBs Oddi* Dipartimento di Informatica e Sistemistica Universita’ di Roma “La Sapienza” oddi@assi.dis.uniromaf.it Pu this paper, we investigate the use of stochastic vari- able and v&e ordering heuristics for solving job shop scheduling problems with n...	1997	107
2,016	The Scaling of Search Cost* Ian P. Gent and Ewan Machtyre and Patrick Presser and Toby Walsh APES Research Group, Department of Computer Science, University of Strathclyde, Glasgow Gl lXH, Scotland Email: {ipg,em,pat,tw}@cs.strath.ac.uk Abstract We show that a resea...	1997	108
2,017	Evidence for Invariants in Local Search David McAllester, Bart Selman, and Henry Kautz AT&T Laboratories 600 Mountain Avenue Murray Hill, NJ 07974 { dmac, selman, kautz} @research.att.com Abstract It is well known that the performance of a stochastic lo- cal search procedure depends upon t...	1997	109
2,018	resenting Aetio Sheila A. McIlraith ase Xerox Palo Alto Research Center Knowledge Systems Laboratory 3333 Coyote Hill Road Stanford University Palo Alto, CA 94301 Stanford, CA 943059020 mcilrait@parc.xerox.com sam @ ksl .stanford.edu Abstract In this paper we examine an important ...	1997	11
2,019	Summarizing CSP hardness with continuous pro distributions Daniel Frost, Irina Rish, and Lluis Vila Dept. of Information and Computer Science University of California, Irvine, CA 92717-3425 { dfrost ,irinar,vila}@ics.uci.edu Abstract We present ...	1997	110
2,020	xploiting Satisfaction Pro Tad Hogg Xerox Palo Alto Research Center Palo Alto, CA 94304, U.S.A. hogg@parc.xerox.com Abstract The deep structme of constraint satisfaction pmbkms ex- plains the association of hard search instances with a phase transition in problem solubility. This structu.re i...	1997	111
2,021	Clustering at the Phase ansit ion Andrew J. Pa&es CIS Dept. and CIRL 1269 University of Oregon Eugene, OR 97403-1269 parkes@cirl.uoregon.edu Abstract Many problem ensembles exhibit a phase transition that is associated with a large peak in the a...	1997	112
2,022	Redtime Generation of Customize Animated Explanations for nowledge-Based Learning Environments* Bares and James C. Lester Multimedia Laboratory Department of Computer Science North Carolina State University Raleigh, NC 2769543206 {whbares,lester}@eos.ncsu.edu ...	1997	113
2,023	The Sounds of Silence: Towards Automated Evaluation of Student Learning in a Reading Tutor that Listens Jack Mostow and Gregory Aist Project LISTEN, Carnegie Mellon University 215 Cyert Hall, 4910 Forbes Avenue Pittsburgh, PA 15213 http://www.cs.cmu.edu/-listen mostow@cs.cmu.edu, aist-t...	1997	114
2,024	C. Lee Giles and Steve Lawrence NEC Research Institute, 4 Independence Way, Princeton NJ 08540 {giles,lawrence}@research.nj.nec.com Abstract Experimental results reported in the machine learning AI lit- erature can be misleading. This paper investigates the com- mon processes of data averaging (report...	1997	115
2,025	Peter Clark Boeing Company PO Box 3707, Seattle, WA 98124 clarkp@redwood.rt.cs.boeing.com Abstract Our goal is to build knowledge-based systems capa- ble of answering a wide variety of questions, including questions that are unanticipated when the knowledge base is built. For systems to achieve ...	1997	116
2,026	ach to MO Ydanda Gil and Marcello Tallis Information Sciences Institute University of Southern California Marina de1 Rey, CA 90292 gil@isi.edu,tallis@isi.edu Abstract Our goal is to build knowledge acquisition tools that support users in modifying knowledge-based syste...	1997	117
2,027	Waym Hissh and Daniel Kudenko Zastname@cs.rutgers.edu Department of Computer Science Rutgers University Piscataway, NJ 08855 Abstract Representing and manipulating sequences in descrip- tion logics (DLs) h as typically been achieved through the use of new sequenc...	1997	118
2,028	P-CLASSIC: A traCtabk escription logic Daphne Koller Alon Levy Computer Science Department AT&T Labs Stanford University 600 Mountain Ave. Stanford, CA 9430590 10 Murray Hill, NJ 07974 koller@cs.stanford.edu levy @research.att.com Avi Pfeffer Computer Science Department Stan...	1997	119
2,029	Fast Context Switching in Real-time Propositional I? Pandurang l&yak and Brian C. Williams Recom Technologies, NASA Ames Research Center Mail Stop, 269-2 Moffett Field, CA 94035. {nayak,williams}@ptolemy.arc.nasa.gov Abstract The trend to increasingly capable and affordable con- trol proce...	1997	12
2,030	A reflective of sy s Pierre E. Bonzon University of Lausanne 1015 Lausanne, Switzerland pbonzon @ hec.unil.ch’ Abstract We consider the problem of building an automated proof system for reasoning in contexts. Towards that goal, we first define a language of...	1997	120
2,031	Obvious erties sf Co uter Programs Robert Divan Department of Computer Science Brown University, Box 1910, Providence, RI 02912 rlg@cs.brown.edu, http:// www.cs.brown.edu/people/rlg/ Abstract We explore the question of what properties of LISP pr...	1997	121
2,032	Detecting Redundant reduction James 6. Schmolze Dept. of Electrical Eng. and Computer Science Wayne Snyder Dept. of Computer Science Tufts University Medford, MA 02155 USA schmolze@eecs.tufts.edu Abstract We present a general method ...	1997	122
2,033	Applications of Rule-Base Coverage Measures to Expert System Evahat ioln Valerie Barr Department of Computer Science Hofstra University Hempstead, NY 11550 vbarr@magic.hofstra.edu Abstract Often a rule-based system is tested by checking its p...	1997	123
2,034	Grigoris Pant OlliOU Griffith University, CIT Nathan, QLD 4111, Australia ga@cit.gu.edu.au Abstract Default logic is computationally expensive. One of the most promising ways of easing this problem and de- veloping powerful implement...	1997	124
2,035	Reasoning with minimal negation as Dipartimento di Informatica e Sistemistica Universita di Roma “La Sapienza” Via Salaria 113,OO 198 Roma, Italy rosati@dis.uniromal .it Abstract We study the computational properties of the proposi- tional fragment of MBNF, the logic of minimal belief ...	1997	125
2,036	Tools For Assembling Modular ntologies in Ontolingua Richard Ekes, Adam Farquhar, James Rice Knowledge Systems Laboratory Stanford University Stanford, CA 94305 { axf, fikes, rice} @ksl.stanford.edu Abstracb The Ontolingua ontology development environment provides a suite of ontology...	1997	126
2,037	Efficient Management of Very Large Ontologies* Kilian Stoffel, Merwyn Taylor and Jim Hendler University of Maryland Computer Science Department College Park, MD, 20742 {stoffel, mtaylor, hendler}@cs.umd.edu Abstract This paper describes an environment for supporting very ...	1997	127
2,038	eyond imizing C Abstract Tom Costello Dept of Computer Science, Stanford, CA 94305 costelloQcs.Stanford.EDU Hntroduetion Reasoning about action has been one of the favorite domains for non-monotonic reasoning. One of the problems with non-monotonic approach...	1997	128
2,039	Adding Knowledge to the Action escript ion anguage ,A Jorge Lobe* Gisela Mendez Department of EECS Departamento de Matematicas University of Illinois at Chicago Universidad Central de Venezuela University of Illinois at Chicago jorge@eecs.uic.edu gmendez@ciens...	1997	129
2,040	Visual Prompts and Graphical Design: A ramework for Exploring the Design Space of 2-D Charts and Graphs Vibhu 0. Mitt& Computer Science Department and ERDC University of Pittsburgh Pittsburgh, PA 15260 Abstract Graphical presentations can be very effective in communi- cating large da...	1997	13
2,041	Norman M&aim and udson YiTl..wner Department of Computer Sciences University of Texas at Austin Austin, TX 78712-1188, USA {mccain,hudson)@cs.utexas.edu Abstract For many commonsense reasoning tasks associated with action domains, only a r...	1997	130
2,042	Qualified ifications Michael Thielscher FG Intellektik, TH Darmstadt Alexanderstr. 10, 64283 Darmstadt (Germany) mit@informatik.th-darmstadt.de Abstract We consider the problem of ramifications, i.e., indirect effects of actions, having exceptions. It is argue...	1997	131
2,043	avid A. Plaisted and Yunshan Zhu Computer Science Department University of North Carolina Chapel Hill, NC 27599-3175 {plaisted,zhu}@cs.unc.edu Fax: (919)962-1799 Abstract In this paper, we present a novel first order theo- rem proving strategy - ordered semant...	1997	132
2,044	Extending the Regular Restriction of Resolution to Non- Subdeductions Bruce Spencer and J. D. Horton University of New Brunswick P.O. Box 4400, Fredericton, New Brunswick, Canada E3B 5A3 bspencer@unb.ca, jdh@unb.ca, http://www.cs.unb.ca Abstract A binary resolution proof, repre...	1997	133
2,045	etecting an St&S Ella M. Atkins Edmund H. Dusfee Kang G. Shin University of Michigan AI Lab, 1101 Beal Ave. Ann Arbor, MI 48109 {marbles, durfee, kg&in} @umich.edu ABSTRACT The degree to which a planner succeeds and meets response deadlines depends on the correctness and ...	1997	134
2,046	Reinforcement Learning with Time Daishi Harada daishi@cs.berkeley.edu Dept. EECS, Computer Science Division University of California, Berkeley Abstract This paper steps back from the standard infinite hori- zon formulation of reinforcement learning problems to consider the simpler case ...	1997	135
2,047	TOlra ander Division of Computer Science The University of Texas at San Antonio San Antonio, Texas 78249 bylander@cs.utsa.edu The absolute loss is the absolute difference between the de- sired and predicted outcome. I demonstrate worst-case upper bounds on the absolute loss for the perceptron...	1997	136
2,048	etic Algorit arrell Whitle y Soraya B. Rana Computer Science Department Colorado State University Fort Collins, Colorado 80523 email: {whitley,rana}@cs.colostate.edu Abstract Wolpert and Macready’s No Free Lunch theorem proves that no search algorithm is better than...	1997	137
2,049	Haym Hirsh Nina Mishra hirsh@cs.rutgers.edu nmishra@uiuc.edu Computer Science Department Computer Science Department Rutgers University University of Illinois at Urbana New Brunswick, NJ 08903 Urbana, IL 61801 Leonard Pitt pitt@cs.uiuc.edu Computer Science Depart...	1997	138
2,050	Pattern iscovery i istributed at abases Raj Bhatnagar, Sriram Srinivasan ECECS Department, University of Cincinnati Cincinnati, OH 45221 Raj .Bhatnagar@uc.edu Abstract Most algorithms for learning and pattern discovery in data assume that ...	1997	139
2,051	Navigation and Planning in a Mixed-Initiative User Interface Robert St. Amant Department of Computer Science North Carolina State University Raleigh, NC 276958206 stamant@csc.ncsu.edu Abstract Mixed-initiative planning is one approach to building ...	1997	14
2,052	More Efficient Windowi Austrian Research Institute for Artificial Intelligence Schottengasse 3, A-1010 Wien, Austria E-mail: juffi@ai.univie.ac.at Abstract Windowing has been proposed as a procedure for efficient memory use in the ID3 decision tree learning algorithm. Howeve...	1997	140
2,053	Rallil E. Vald&-P&ez Computer Science Ca Vladimir Pericliev epartment of Mathematical Linguistics athematics and Informa ad. of Ski., UP3 Sofia - Abstract Data-driven model building is an important task of scientific discovery that ...	1997	141
2,054	am&h Yip and Gerald Artificial Intelligence Laboratory Department of Electrical Engineering and Computer Science I%ssachusetts Institute of Technology Cambridge, iVIA 02139 Abstract Humans rapidly and reliably learn many kinds of regu- larities and generalizat...	1997	142
2,055	Leo Kuvayev C. L. Giles and J. Philbin and EL Ckjtin Department of Coml&ter Science NEC Research Institute University of Massachusetts 4 Independence Way Amherst, MA 01002 Princeton, NJ 08540 kuvayev@cs.umass.edu (giles,philbin,henry)@research.nj.nec.com Abstract The speed of I/O ...	1997	143
2,056	Learning Bayesia Networks from Incorn Moninder Singh Department of Computer and Information Science University of Pennsylvania Philadelphia, PA 19104-6389 msingh@gradient.cis.upenn.edu Abstract Much of the current research in learning Bayesian Network...	1997	144
2,057	irical Evaluation of agging ad Richard Maclin Computer Science Department University of Minnesota-Duluth Duluth, MN 55812 email: rmaclin@d.umn.edu Abstract An ensemble consists of a set of independently trained classifiers (such as neural networks or decision trees) whose predictions ar...	1997	145
2,058	New ection Institute for Research in Cognitive Science NEC Research Institute University of Pennsylvania Philadelphia, PA 19104-6228 daes@linc.cis.upenn.edu Abstract We introduce a new approach to model selection that performs better than the standard...	1997	146
2,059	s in Neural Network ining: verfitti ecte Steve Lawrence1 $6. Lee Gilesl, A ’ NEC Research, 4 Independence Way, Princeton, NJ 08540, 2 Faculty of Informatics, Uni. of Wollongong, Australia {lawrence,giles}Oresearch.nj.nec.com,Ah-Chung-Tsoi~uow.edu.au Abstract For many reasons, neural networks hav...	1997	147
2,060	aximizing the Benefits of arallel Sear-c iane J. Cook and Et. Craig Varnell University of Texas at Arlington Box 19015, Arlington, TX 76019 {cook,varnell}@cse.uta.edu Abstract Many of the artificial intelligence techniques developed to date rely on heuristic search through large spaces. Un...	1997	148
2,061	Richard Kufrin National Center for Supercomputing Applications University of Illinois at Urbana-Champaign Urbana, IL 61801 rkufrin@ncsa.uiuc.edu Abstract Induction systems that represent concepts in the form of production rules have proven to be useful in a vari- ety of d...	1997	149
2,062	Possibilistic and stanclard probabilistic semantics of con itional ]knowledge Salem Benferhat, Didier Dubois and Hem-i Prade Institut de Recherche en Informatique de Toulouse (IRIT) - CNRS Universite Paul Sabatier, 118 route de Narbonne 3 1062 Toulouse Cedex 4, France Email: { benfer...	1997	15
2,063	Transferring and etraining Learned William W. Cohen AT&T Labs-Research 600 Mountain Avenue Murray Hill, NJ 07974 wcohen@research.att.com Abstract Any system that learns how to filter documents will suffer poor performance during an initial...	1997	150
2,064	Active Learning with Committees for Text Categorization Ray Liere rasad Tadepalli lierer0research.cs.orst.edu tadepall0research.cs.orst.edu Department of Computer Science, Oregon State University, Dearborn Hall 303, Corvallis, OR 97331-3202, USA Abstract In many real-world domains, supervised...	1997	151
2,065	Statistical Parsi Statistics * Eugene Charniak Department of Computer Science, Brown University ec@cs.brown.edu Abstract s:rose We describe a parsing system based upon a language model for English that is, in turn, based upon assign- ing probabilit...	1997	152
2,066	A New Supervised for Word Sense Ted Pedersen and Rebecca Bruce Department of Computer Science and Engineering Southern Methodist University Dallas, TX 75275-0122 {pedersen,rbruce}@seas.smu.edu Abstract The Naive Mix is a new supervised learning algo- rit...	1997	153
2,067	School of Computer Science 1125 Colonel By Drive Carleton University Ottawa, Ontario KlS 5B6 CANADA { wsaba,jeanpier}@scs.carleton.ca Abstraca Quantification in natural language is an important phenomena that seems to touch on some pragmatic and inferential aspects of langua...	1997	154
2,068	aratives in Context IQF> Computational Linguistics Research Group & Graduate Programm on “Human and Artificial Intelligence” Freiburg University Platz der Alten Synagoge 1, D-79085 Freiburg, Germany {staab,hahn}@coling.uni-freiburg.de Abstract We propose a model of semantic interpretation of ...	1997	155
2,069	Multi-document Summarization by Graph Search and Mate Inderjeet Mani The MITRE Corporation 7525 Colshire Drive, W640 McLean, VA 22102, USA imani@mitre.org Abstract We describe a new method for summarizing similar- ities and differences in a pair of related documents using...	1997	156
2,070	Department of Computer Science University of Toronto Toronto, Ontario Canada M5S 3G4 marcu@cs.toronto.edu Abstract We present a new, data-driven approach to text plan- ning, which can be used not only to map full knowledge pools into natural language texts, but also to generate texts that ...	1997	157
2,071	. Interference as a Tool for esigning and EvaBnat ing obot Controllers Dani Goldberg & Maja J Matar% Volen Center for Complex Systems, Computer Science Department Brandeis University Waltham, MA 02254 dani@cs.brandeis.edu & maja@cs.brandeis.edu ...	1997	158
2,072	Using Communication to ee Locality in M Maja J MataG Volen Center for Complex Systems Computer Science Department Brandeis University Waltham, MA 02254 maja@cs.brandeis.edu Abstract This paper attempts to bridge the fields of ma- ...	1997	159
2,073	On the Axiomatization of ualitative Decision Ronen I. Brafrnan Computer Science Department University of British Columbia Vancouver, B.C., Canada V6T 124 brafman@cs.ubc.ca Abstract Qualitative decision tools have been used in AI and CS in various contexts, but their adequacy is still un...	1997	16
2,074	Spat ial navigation with uncertain deviations Michel de Rougemont University Paris-II & LRI, Batiment 490 91400 Orsay, France mdr@lri.fr Abstract : We consider geometrical scenes with obs- tacles and landmarks that can’t necessarily be distin...	1997	160
2,075	A Color Interest Operator for Landmark- Zachary Dodds and Gregory D. ager Department of Computer Science Yale Univ., P.O. Box 208285 New Haven, CT 06520 Abstract Landmark-based approaches to robot navigation re- quire an “interest operator” to estimate t...	1997	161
2,076	Combining Approxi Frank Klassner Victor Lesser Hamid Nawabt Computer Science Department University of Massachusetts Amherst, MA 01003 (klassner lesser}@cs.umass.edu Abstract When dealing with signals from complex environ- ments, where ...	1997	162
2,077	Jim Blythe Manuela Veloso Computer Science Department Carnegie Mellon University Pittsburgh, PA 15213, USA jblythe,veloso@cs.cmu.edu Abstract Recently, several planners have been designed that can create conditionally branching plans to solve problems which involve uncertainty....	1997	163
2,078	Case-Based Si Estimating Adap David B. Leake, And Computer Science Department Lindley Hall 215, Indiana University Bloomington, IN 47405 {leake, kinley, wilson)@cs.indiana.edu Abstract Case-based problem-solving systems rely on similar- ity assessment to select stored cases whose solutions...	1997	164
2,079	Dynamic Abstraction Robert I?. Goldman, David J. Musliner, Kurt D. Krebsbach, Mark S. Boddy Automated Reasoning Group Honeywell Technology Center 3660 Technology Drive Minneapolis, MN 55418 {goldman,musliner,krebsbac,boddy}@htc.honeywell.com Abstract This paper d...	1997	165
2,080	Abductive Completion of Karen L. Myers AI Center, SRI International 333 Ravenswood Ave. Menlo Park, CA 94025 myers@ai.sri.com Abstract Most work on AI planning has focused on the develop- ment of fully automated methods for generating plans that satisfy user-specified ...	1997	166
2,081	A Linear Programming Heuristic for 0 g’om Bylander Division of Computer Science The University of Texas at San Antonio San Antonio, Texas 78249 bylander@cs.utsa.edu Abstract I introduce a new search heuristic for propositional STRIPS planning that is based on transforming planning insta...	1997	167
2,082	Finding Optimal Solutions to Rubik’s Cube Using Pattern Databases Richard E. Korf Computer Science Department University of California, Los Angeles Los Angeles, Ca. 90095 korf@cs.ucla.edu Abstract We have found the first optimal solutions to random instances of Rubik’s Cube....	1997	168
2,083	ing by Rewriti tl-y Generati ig Jo& Luis Ambite and Craig A. Knoblock Information Sciences Institute and Department of Computer Science University of Southern California 4676 Admiralty Way, Marina de1 Rey, CA 90292 { ambite, knoblock}@isi.edu Abstract Domai...	1997	169
2,084	The Effect of Observations on the Complexity of Model- Diagnosis Adnan Darwiche Department of Mathematics American University of Beirut PO Box 11 - 236, Beirut, Lebanon darwiche@aub.edu.Eb Abstract This paper shows how to efficiently diagnose sy...	1997	17
2,085	st and Fast Action Selection ism for F%nning* Blai Bonet G&or Lokrincs M&tor Geffner Departamento de Computacibn Universidad Simbn Bolivar Aptdo. 89000, Caracas 1080-A, Venezuela { bonet,gloerinc,hector}@usb.ve Abstract The ability to plan and react in dynamic en...	1997	170
2,086	Planning with Concurrent Interacting Actions Craig Boutilier and Ronen I. Brafrnan Computer Science Department University of British Columbia Vancouver, B.C., Canada V6T lZ4 { cebly,brafman} @cs.ubc.ca Abstract In order to generate plans for agents with multiple actuators or agent teams, w...	1997	171
2,087	euristic Varia s Ronen I. Brafman Department of Computer Science University of British Columbia Vancouver, B.C. V6T 124 Canada brafman@cs.ubc.ca http:Nwww.cs.ubc.ca/spider/brafman Abstract Partially observable Markov decision processes (POMDPs) are an appealing tool for modeling plan...	1997	172
2,088	Incremental methods for computing Markov decision Milos Hauskrecht MIT Laboratory for Computer Science, NE43-421 545 Technology Square Cambridge, MA 02139 milos@medg.lcs. mit. edu servable Abstract Partially observable M arkov decision processes (POMDPS) al...	1997	173
2,089	Effective Bayesian Inference for Stochastic Programs 1 Daphne Koller Stanford University koller@cs.stanford.edu avid McAllester AT&T Research dmac@research.att.com Avi Pfeffer Stanford University avi @cs.stanford.edu Abstract In this paper, we propose a stochastic version of a genera...	1997	174
2,090	robabilistic Propositional Planning: Representations and Complexity Michael L. Littman Department of Computer Science Duke University, Durham, NC 27708-0129 mlittman@cs.duke.edu Abstract Many representations for probabilistic propositional planning problems have been studied. This paper re- v...	1997	175
2,091	Monitoring, Predic and Fault Isolation in Dynamic Pieter J. Mosterman and Gautam Biswas Center for Intelligent Systems Box 1679, Sta B Vanderbilt University Nashville, TN 37235. pjm,biswasQvuse.vanderbilt.edu Abstract ‘Diagnosis of dynamic physi...	1997	18
2,092	Model Minimization in Markov Decision Thomas Dean andi Robert Givan Department of Computer Science Brown University Box 1910, Providence, RI 02912 {tld,rlg}@cs.brown.edu Abstract We use the notion of stochastic bisimulation horno- gene&y to analyze planning probl...	1997	19
2,093	If at First You Don’t Succeed... Kentaro Toyama and Gregory II. Hager Department of Computer Science, Yale University, P.O. Box 208285 New Haven, CT, 06520 Abstract One quality that makes biological systems appear in- telligent ...	1997	2
2,094	Structured Sohtion Methods for Fahiem Bacchus Craig Boutilier Dept. of Computer Science Dept. of Computer Science University of Waterloo University of British Columbia Waterloo, Ontario Vancouver, B.C. Canada, N2L 3Gl Canada, V6T 124 fbacchus@logos.uwaterloo.ca cebly@cs.ubc.ca ...	1997	20
2,095	Model Decomposition and Simulation: A component based qualitative simulation algorithm * Daniel J. Clancy and Benjamin Kuipers Department of Computer Sciences University of Texas at Austin Austin, Texas 78712 clancy@cs.utexas.edu and kuipers@cs.utexas...	1997	21
2,096	Static and dynamic abstraction s es the problem of chatter i gualit at ive s Daniel J. Chancy and Benjamin Kuipers Department of Computer Sciences University of Texas at Austin Austin, Texas 78712 clancy@cs.utexas.edu and kuipers@cs.utexas.edu Abst...	1997	22
2,097	ete Murray Shanahan Department of Computer Science, Queen Mary and Westfield College, Mile End Road, London El 4NS, England. mps@dcs.qmw.ac.uk Abstract This paper presents a logical account of sensor data assimilation in a mobile robot, based on abduction. ...	1997	23
2,098	Projective relations for 3D space: Computational model, application, and psychological evaluation* Constanze Vorwerg Gudrun Socher’ Thomas Fuhr Gerhard Sagerer Gert Rickheit Universitat Bielefeld, SFB 360 “Situierte Ktinstliche Kommunikatoren”, Postfach 10013 I,33501 Bielefeld, Germany ...	1997	24
2,099	Integrating a Spatial Reasoner wit esolution T rover Thomas R. Ioerger Department of Computer Science Texas A&M University College Station, TX 77843 ioerger@cs.tamu.edu Abstract Some spatial reasoning systems use images to solve prob...	1997	25

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.