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
500	GEhERATING MEDICAL CASE REPORTS WITH THE LINGUISTIC STRING PARSER* Ping-Yang Li, Martha Evens, and Daniel Hier Department of Computer and Computer Science Department Depart,ment of Neurology Information Sciences Illinois Institute of Technolo...	1986	56
501	A Relational Representation of Modification Samuel Bayer The MITRE Corporation 1 Burlington Road Bedford, MA 01730 Mail Stop A045 Abstract The KING KONG parser being developed at The MITRE Corpora- tion combines an argument-structure shorthand with recent work on the relationship ...	1986	57
502	Cat egorial Disambiguat ion Gavan Duffy Department of Government The University of Texas at Austin Austin, Texas 78712 ARPAnet: AI.DuffyQRZO.UTEXAS.EDU When considering a design for the categorial disambiguator, an immediate inspiration was Waltz’ [4] constraint-propag...	1986	58
503	FOCUSING AND REFERENCE RESOLUTION IN PUNDIT Deborah A. Dahl Research and Development Division SDC -- A Burroughs Company PO Box 517 Paoli, PA 19301 ABSTRACT This paper describes the use of focusing in the PUN- DIT text processing system.* Focusing, as discussed by [Sidncr...	1986	59
504	Merging Objects and Logic Programming: Relational Semantics Herve Gallaire European Computer-Industry Research Centre (E.C.R.C) Arabellastr. X7, D-8000 Muenchen 81 FRG Abstract This paper proposes new semantics for merging object pro- gramming i...	1986	6
505	ATRANS: Automatic Processing of Money Transfer Messages Steven L. Lytinen and Anatole Gershman Cognitive Systems, Inc. 234 Church Street New Haven, CT. 06510 ABSTRACT Unformatted natural-language nioney.transfer messages play an important role in t...	1986	60
506	ROBOT NAVIGATION IN UNKNOWN TERRAINS OF CONVEX POLYGONAL OBSTACLES USING LEARNED VISIBILITY GRAPHS B. John Oommen S.S. Iyengar and Nagerwara S.V. Rao School of Computer Science Department of Computer Science Carleton University Lousiana State University Ottawa: K...	1986	61
507	PLANNING SENSORLESS ROBOT MANIPULATION OF SLIDING OBJECTS M. A. Peshkin and A. C. Sanderson * Robotics Institute Carnegie-Mellon University Pittsburgh, Pennsylvania 15213 ABSTRACT: The physics of motion of a sliding object can be used to plan senso...	1986	62
508	AND/OR GRAPH REPRESENTATION OF ASSEMBLY PI.ANS* Luiz S. Momcm dc Mcllo and Arthur C. Sandcrson Department of Electrical and Computer Engineering and Robotics Institute Carnegie-Mellon University Pittsburgh Pennsylvania 15213 ABSTRAC’f This paper presents a compact representation of all ...	1986	63
509	A Mobile Robot with Onboard Parallel Processor and Large Workspace Arm Rodney A. Brooks, Jon Connell, and Anita Flynn MIT Artificial Intelligence Lab 545 Technology Square Cambridge, Mass, 02139 ABSTRACT a. The MIT AI Lab’s second mobile robot, MOBOT-2, has a num...	1986	64
510	NOISETOLERANT RANGE ANALYSIS FOR AUTONOMOUS NAVIGATION l Aviv Bergman and Cregg K. Cowan Robotics Laboratory, SRI International 333 Ravenswood Avenue, Menlo Park, California 94025 ABSTRACT Techniques for detecting horizontal regions, obstacles, ditches, and shoulders along a...	1986	65
511	A REAL-TIME ROAD FOLLOWING AND ROAD JUNCTION DETECTION VISION SYSTEM FOR AUTONOMOUS VEHKL= .Darwin Kuan, Gary Phipps, and A-Chuan Hsueh Artificial Intelligence Center Central Engineering Laboratories FMC Corporation 1185 Coleman Ave. Santa Clara, CA 9505...	1986	66
512	OBJECT RECOGNITION IN STRUCTURED AND RANDOM ENVIRONMENTS: LOCATING ADDRESS BLOCKS ON MAIL PIECES’ Ching-Huei Wang and Sargur N. Srihari Department of Computer Science State L‘niversity of New J’ork at Buffalo Buffalo, NJ‘ 14260 ABSTRACT .4 framelvork for determining special interest ...	1986	67
513	A SIGNAL-SYMBOL APPROACH TO CHANGE DETECTION B. G. Lee, V. T. Tom and M. J. Carlotto The Analytic Sciences Corp. I Jacob Way Reading, MA 01867 ABSTRACT A hybrid (signal-symbol) approach for detecting significant changes in imagery uses a signal-based change de...	1986	68
514	1 REASONING WITH SIMPLIFYING ASSUMPTIONS: A METHODOLOGY AND EXAMPLE Yishai A. Feldman and Charles Rich The Artificial Intelligence Laboratory Massachusetts Institute of Technology 545 Technology Square Cambridge, Mass. 02139 ARPANET: YishaiQMC, RichfR...	1986	69
515	DOhIAINS IN LOGIC PROCNAMhfJNG Arahellast, 17 D-WJO Munich 81 \\ cst-(;ernlan> European Computer-industr) Resrarrh (‘entrfs (E. (‘.I] (’ ABSTRACT. When confronted with constraint sat isfaction prcjblerns (CSP). the “generate b test“ strateg! ...	1986	7
516	Tucety - still Flying Some Reti on Abnormal Birds, Applicable Rules and a Default Prover Gerhard Bewka Gesellschaft fiir Mathematik und Datenverarbeitung Forschungsgruppe Expertensysteme Postfach 12 40 D 5205 Sankt Augustin, Federal Republic of Germany ABSTRACT l’his p...	1986	70
517	Representing Actions with an Assumption-Based Truth Maintenance System Paul H. Morris and Robert A. Nado IntelliCorp 1975 El Camino Real West Mountain View, California 94040 ABSTRACT The Assumption-based Truth Maintenance System, introduced by ...	1986	71
518	Automatic Compilation of Logical Specifications into Efficient Programs Donald Cohen’ USC. Information Sciences Institute 4676 Admiralty Way Marina del Rey, Ca. 90292 Abstract We describe an automatic programmer, or “compiler” which accepts as input a predicate calculus spec...	1986	72
519	FACTUAL KNOWLEDGE FOR DEVELOPING CONCURRENT PROGRAMS Albert0 Pettorossi IASI-CNR Viale Manzoni 30 00185 Roma (Italy) ABSTRACT We propose a system for the derivation of algo- rithms which allows us to use "factual knowledge" for the development of concurrent programs. From pr...	1986	73
520	CONCEPTUAL CLUSTERING USING RELATIONAL INFORMATION Hernd Nordhauscm Department of Information aid Computer Science IJniversity of California, lrvine Irvine, CA 92717 ArpaNet: berndCQic:s.uci.edu Abstract Work in conceptual clustering has focused on...	1986	74
521	Learning by Failing to Explain’ Robert J. Hall Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge, MA 02139 Abstract Explanation-based Generalization depends on having an expla- nation on which to base generalization. Thus...	1986	75
522	Mapping Explanation-Based Generalization onto Soar’ Paul S. Rosenbloom Knowledge Systems Laboratory Department of Computer Science Stanford University 701 Welch Road (Bldg. C) Palo Alto, CA 94304 ABSTRACT Explanation-based generalization (EBG) is a powerful ap- proach ...	1986	76
523	Learning to Anticipate and Avoid Planning Problems through the Explanation of Failures* Kristian J. Hammond Department of Computer Science Yale University ABSTRACT This paper presents an approach to learning during planning that focuses on learning...	1986	77
524	A DOMAIN INDEPENDENT EXPLANATION-BASED GENERALIZER Raymond J. Mooney Scott W. Bennett Coordinated Science Laboratory University of Illinois at Urbana-Champaign 1101 W. Springfield Ave. Urbana, IL 61801 ABSTRACT A domain independent technique for generalizing a broad...	1986	78
525	The Role of Prior Causal Theories in Generalization Michael Pazzani, Michael Dyer, Margot Flowers Artificial Intelligence Laboratory 3531 Boelter Hall UCLA Los Angeles, CA 90024 Abstract OCCAM is a program which organizes memories of events and learns by creating generalizations describing ...	1986	79
526	Comments on Kornfeld’e “Equality for Prolog”: e-unification as a mechanism for augmenting the Prolog search strategy. E. W. Elcock and P. Hoddinott Department of Computer Science The University of Western Ontario London, Ontario, Canada N6A 5B7 Abstract ...	1986	8
527	Constructing and Refining Causal Explanations from an Inconsistent Domain Theory 1 Richard J. Doyle Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge, MA 02139 Abstract Recent work in the field of machine learning ...	1986	80
528	Not the Path to Perdition: The Utility of Similarity-Based Learning Michael Lebowitz’ Department of Computer Science -- Columbia University New York, NY 10027 Abstract A large portion of the research in machine learning has involved a paradigm of comparing many examples and analyzing them in ...	1986	81
529	STAHLp: Belief Revision in Scientific Discovery 1)epartmenI of Inforrr~aLiori and Computer Science IJniversity of California, Irvine 92717 RrpaNet: drose~lC:S.IJ(II.I~:1)1J, lar~gl~~y~I~~S.lJ~~I.~~~I~IJ Abstract III this paper we describe the STAlILp sys...	1986	82
530	A CASE-BASED REASONING SYSTEM FOR SUBJECTIVE ASSESSMENT* William M. Bain Yale University Computer Science Department Abstract People tend to improve their abilities to reason about situations by amassing experiences in reasoning. Resorting to previous...	1986	83
531	Factorization in Experiment Generation Devika Subramanian Joan Feigenbaum Department of Computer Science Stanford University Stanford, CA 94305 ABSTRACT Experiment generation is an important part of incremental concept learning. One basic function of experimentation ...	1986	84
532	GENER.ATING PREDICTIONS TO AID THE SCIENTIFIC DISC:OVERY PROCESS Randy .Jor1es Department of Information and Computer Science University of California, Irvine Irvine, CA $2717 Abstract NGLAIIBER is a system which models the scientific discoverv ...	1986	85
533	Beyond incremental processing: Tracking concept drift JefI’rt:y C. Schlirrlrr~er arid Ric-hard 11. Grarlger, Jr. I)epartrnent of’ Irif’orrriaCion arid Computer Science IJniversity of’ Catif’ornia, Irvine 92717 ArpaNet: Schlin~rr~et~OI(~S.IJ(~I.l1:1)17, C:rarlgt:r...	1986	86
534	A Case Study of Incremental Concept Induction Abstract Applicat,ioli of niactiirie inductiori l,cx-tlrliques in corriplcx doltlailis promises to push the computational limits of nofi- incrc~rnerila.1, search ilitensive induction methods. 1,earning t~f...	1986	87
535	Quantifying the inductive bias in concept learning (extended abstract) David Haussler Department of Mathematics and Computer Science, University of Denver, Denver, Colorado 80208. Abstract We show that the notion of bias in inductive concept lea...	1986	88
536	PRELIMINARY STEPS TOWARD THE AUTOMATION OF INDUCTION Stuart J. Russell Department of Computer Science Stanford University Stanford, CA 94305 ABSTRACT Rational inductive behaviour is strongly influenced by ex- isting knowledge of the world. This paper begins...	1986	89
537	DESIGN AND EXPERIHENTATION OF AN EXPERT SYSTEM FOR PROGRANNING IN-THE-LARGE Giovanni Guida, Marco Guida, Sergio Gusmeroli, Marco Somalvico Milan Polytechnic Artificial Intelligence Project Politecnico di Milan0 Mi Lano, Italy 1. INTRODUCTION The results ...	1986	9
538	INDUCTIVE Abstract INFERENCE BY REFINEMENT P. D. Laird* Department of Computer Science Yale University New Haven, Ct., 06520 A model is presented for the class of inductive inference problems that are solved by refinement algorithms - that is, algo...	1986	90
539	OPTIMAL ALLOCATION OF VERY LIMITED SEARCH RESOURCES David Mutchler t Naval Research Laboratory, Code 7591 Washington, D.C. 20375-5000 Abstract This paper presents a probabilistic model for studying the question: given n search resources, w...	1986	91
540	Selecting Appropriate Representations for Learning from Examples Nicholas S. Flann and Thomas G. Dietterich Department of Computer Science Oregon State University Corvallis, Oregon 97331 Abstract The task of inductive learning from examples places constraints on ...	1986	92
541	DISCOVERING FUNCTIONAL FORMULAS THROUGH CHANGING REPRESENTATION BASE Mieczyslaw M. Kokar Department of Industrial Engineering and Information Systems Northeastern University 360 Huntington Avenue, Boston, MA 02115 ABSTRACT This paper deals with computer generation ...	1986	93
542	On Debugging Rule Sets When Reasoning Under Uncertainty David C. Wilkins and Bruce G. Buchanan Department of Computer Science Stanford University Stanford, CA 94305 ABSTRACT Heuristic inference rules with a measure of strength less than certaint,y have a...	1986	94
543	RULE REFINEMENT USING THE PROBABILISTIC RULE GENERATOR Won D. Lee and Sylvian R. Ray Department of Computer Science, University of Illinois, Urbana, Illinois ABSTRACT This work treats the case of expert-originated hypotheses which are to be m...	1986	95
544	A METALINGUISTIC APPROACH TO THE CONSTRUCTION OF KNOWLEDGE BASE REFLVEMENT SYSTEMS Allen Ginsberg AT&T Bell Laboratories Holmdel, NJ 07733 Abstract A variety of approaches to knowledge base refinement [3, 81 and rule acquistion [4] have appeared recently. This paper is concerned with th...	1986	96
545	The FERMI System: Inducing Iterative Macro-operators from Experience Patricia W. Cheng and Jaime G. Carbonell Computer Science Department Carnegie-Mellon University Pittsburgh PA 15213 Abstract Automated methods of exploiting past experience to reduce search vary from analogical transfer to c...	1986	97
546	GENERALIZED PLAN RECOGNITION Henry A. Kautz James F. Allen Department of Computer Science University of Rochester Rochester, New York 14627 ABSTRACT This pa r er outlines a new theory of plan recognition that is significant y more powerful than previous approaches. Con- curr...	1986	98
547	A LOGIC OF DELIBERATION Marvin Belzer Advanced Computational Methods Center University of Georgia Athens, GA 30602 ABSTRACT Deliberation typically involves the formation of a plan or intention from a set of values and beliefs. I suggest that deliberation, or “pra...	1986	99
548	Donald @. Allen, Seth A. Steinberg, and Lawrence A. Stabile BBN Advanced Computers, Inc. 10 Fawcett Street Cambridge, Massachusetts 02238 Abstract II. The Butterfly Architecture This paper describes recent enhancements to the Common Lisp system that BBN is developing’ for its ...	1987	1
549	CP as a general- urpose constraint-language Vijay A. Saraswat Computer Science Department Carnegie Group Inc Carnegie-Mellon University Station Square Pittsburgh Pa 15213 Pittsburgh Pa 15209 Abstract In this paper we present the notion of concurrent, controllable constraint systems. We ...	1987	10
550	Inference In Text Understanding Peter Norvig Computer Science Dept., Evans Hall University of California, Berkeley Berkeley CA 94720 Abstract The problem of deciding what was implied by a writ- ten text, of “reading between the lines’ ’ is the problem of inference. To extract proper inferences f...	1987	100
551	Department of Computer Science Brandeis University Waltham, 02254 617-736-2709 jamesp@ brandeis.csnet-relay There has recently been a great deal of interest in the struc- ture of the lexicon for natural language understanding and generation. One of the major problems encountered has ...	1987	101
552	Ambiguity Procrastination Elaine Rich ent Wittenburg MCC 3500 West Balcones Center Drive Austin, Texas 78759 bstract In this paper we present the procrastination approach to the treatment of ambiguity, particularly in the context of natural language interfaces. In this approach, we try ...	1987	102
553	Memory-Based Reasoning Applied to English Pronunciation Craig W. Stanfill Thinking Machines Corporation 245 First Street Cambridge, MA 02142 Abstract Memory-based Reasoning is a paradigm for AI in which best-match recall from memory is the primary inference mechanism. ...	1987	103
554	David A. Wroblews htlcc 3500 West Balcones Center Drive Austin, Texas 78759 Abstract Graph unification is sometimes implemented as a destructive operation, making it neccesary to copy the argument graphs before beginning the actual unification. Previous research on graph unification claimed t...	1987	104
555	oices in Problems Sanjay Mittal and Felix Frayman Intelligent Systems Laboratory, Xerox PARC, 3333 Coyote Hill Rd., Palo Alto, CA. 94304 Constraint problems derived from design and configurations tasks often use components (structured values) as domains of constrained var...	1987	105
556	PROMPT: An Innovative Design Tool Seshashayee S. Murthy and Sanjaya Addanki IBM T.J.Watson Research Center B.O. Box 704 Yorktown Heights, NY 10598 ABSTRACT We describe a system, Prompt, used to design physical systems. Prompt employs a multi-level approach to design. When simple ...	1987	106
557	s Toyoaki Nishida and Shuji Departs of Pnformation Science oto university an ABSTRACT Intuitively, discontinuous changes can be seen as very rapid continuous changes. A couple of alternative methods based on this ontology are presented and compared. One, called t...	1987	107
558	Hierarchical Reasoning about Inequalities Elisha Sacks’ MIT Laboratory for Computer Science 545 Technology Square, Room 370 Cambridge, MA 02139, USA Abstract This paper describes a program called BOUNDER that proves inequalities between functions over finite sets...	1987	108
559	Piecewise Linear Reasoning Elisha ISacks’ MIT Laboratory for Computer Science 545 Technology Square, Room 370 Cambridge, MA 02139, USA Abstract This paper describes a new technique called piecewise linear reasoning (PLR) for analyzing dynamic systems describable by ...	1987	109
560	Non-Deterministic Lisp with Dependency-Directed Backtracking Ramin Zabiht, David McAllester and David Chapman Artificial Intelligence Laboratory Massachusetts Institute of Technology Abstract Extending functional Lisp with McCarthy’s non- deterministic operator ...	1987	11
561	Robabilistic Semantics ualitative Influences Michael P. Wellman MIT Laboratory for Computer Science 545 Technology Square Cambridge, MA 02139 Abstract What’s in an infiuence link? To answer this founda- tional question, I propose a semantics for qualitative influences: a pos...	1987	110
562	Extracting Qualitative Dynamics from Numerical Experiments Kenneth Man-kam Yip MIT Artificial Intelligence Laboratory NE 43 - 438 545 Technology Square, Cambridge, MA 02139. Abstract The Phase Space is a powerful tool for representing and reasoning about the qualitativ...	1987	111
563	olecullar colllections Collins and Kenneth ID. Forbus Qualitative Reasoning Group Department of Computer Science University of Illinois Abstract Hayes has identified two distinct ontologies for reasoning about liquids. Most qualitative physics r...	1987	112
564	Extending the Mathematics in Qualitative Process Bruce D’Ambrosio Department of Computer Science Oregon State University Abstract We present a semi-quantitative extension to the quali- tative value and relationship representations in Quali- tative Process (QP) theory. Examinati...	1987	113
565	Philippe Dague and Qlivier Raiman IBM Scientific Ccntcr Electronique Serge Dass’nult 36 Ave R. Poincark 55 Quai M. Dassault 75116 Paris France S Cloud 92214 France Abstract This paper shows how order of magnitude reasoning has been Successfully used for troubleshooting complex analog ci...	1987	114
566	Explanation-Based Faillure Ajay Gupta Hewlett-Packard Laboratories Filton Road, Bristol BS12 6&Z, UK. email: ag@hplb.csnet Abstract Interactions are inherent in design-type problem- solving tasks where only partially compiled opera- tors are available. Failures arising fr...	1987	115
567	Department of Computer Science Courant Institute of Mathematical Sciences, New York University 251 Mercer Street, New York, NY 10012 Abstract This paper describes a two-step algorithm for the qualitative analysis of mechanical devices. The first step ...	1987	116
568	CRITICAL HYPERSURFACES AND THE QUANTITY SPACE Mieczyslaw M. Kokar Northeastern University 360 Huntington Avenue Boston, Massachusetts 02115 KOKAR&WHUB.ACS.NORTHEASTERN.EDU@RELAY.CS.NET ABSTRACT1 Qualitative reasoning about physical processes is based on the notion of "quantity spa...	1987	117
569	Abstraction by Time-Scale in ua~itati~e Sedation Benjamin Kuipers Department of Computer Sciences University of Texas at Austin Austin, Texas 78712 Abstract Qualitative simulation faces an intrinsic problem of scale: the number of limit hypotheses grows exponentially with the number ...	1987	118
570	Michael IL.. Mavrsvouniotis and George Stephanopoulos Laboratory for Intelligent Systems in Process Engineering Department of Chemical Engineering, Massachusetts Institute of Technology Cambridge, Massachusetts 02139 Abstract The O[M] formalism for representing orders of magnitude and approximate ...	1987	119
571	A. AN INTELLIGENT TUTORING SYSTEM FOR INTERPRETING GROUND TRACKS DT. Kathleen Swigger* Lt. Cal. Hugh Burns Harry Loveland Capt. Terresa Jackson Air Force Human Resources Laboratory Intelligent Systems Branch Brooks Air Force Base, Brooks Texas 78235 Abstract This paper describes an inte...	1987	12
572	An Architecture For Intelligent Task Autsmatio Jeffrey M. Becker and Fred E. Garrett Martin Marietta Denver Aerospace P.O. Box 179, M.S. 0428 Denver, CO 80201 Abstract This report discusses the Martin Marietta Intelligent Task Automation Project (ITA). The purpose of the ITA project is ...	1987	120
573	REACTIVE REASONING AND PLANNING Micha.el P. Georgeff Amy L. Lansky Artificial Intelligence Center, SRI International 333 Ravenswood Avenue, Menlo Park, California Center for the Study of Language and Information, Stanford University Abstract In this p...	1987	121
574	Fred Lakin Center for the Study of Language and Information, Stanford University Center for Design Research, Stanford University Rehabilitation R&D Center, Palo Alto Veterans Hospital 3801 Miranda Ave, Palo Alto, California 94304 ARPAnet: lakinQcsli.stanford.edu ABSTRACT In modern user interf...	1987	122
575	QUALITATIVE LANDMARK-BASED PAT PLANNING ” AND FOLLOWING Tod S. Levitt, Daryl ‘I’. Lawton, David M. Chelberg, Philip C. Eelson Advanced Decision Systems, Mountain View, California 94040 ABSTRACT This paper develops a theory for path planning and following using vi...	1987	123
576	sestions using Geometric Analysis an 1 A 560 with David R. Strip Intelligent Machine Principles Division 1411 San&a National Laboratories P. 0. Box 580Q Albuquerque, New Mexico 87185 Abstract Automatic programming of insertions is an essential step in achievin...	1987	124
577	Bounds on translational and angular velocity components from first order derivatives of ‘hnage flow Muralidhara Subbarao Department of Electrical Engineering, State University of New York Stony Brook, NY 11794. Abstract A moving rigid object produces a moving image on the...	1987	125
578	shard Szeliski Computer Science Department Carnegie-Mellon University Pittsburgh, PA 15213 Abstract Many of the processing tasks arising in early vision involve the sclution of ill-posed inverse problems. Two techniques that are often used to solve these inverse problems are reg- uhuization and ...	1987	126
579	ENERGY CONSTRAINTS ON DEFORMA Recovering Shape and Non-Rigid Motion Demetri Terzopoulos Andrew Wit kin, Michael Kass Schlumberger Palo Alto Research 3340 Hillview Avenue, Palo Alto, CA 94304 Abstract We propose a panadigm for shape and motion reconstmsction ba...	1987	127
580	Shadow Stereo -- Loc William B. Thompson Michael T. Cheeky Computer Science Department Computer Science Department Systems Development D&ision University of Minnesota University of Minnesota Honeywell Inc. Minneapolis, MN 55455 Minneapolis, MN 55455 Golden Valley, MN 55427 Abstrac...	1987	128
581	Perceptual Significance Hierarchy: A Co uter Vision Theory for Color Se Deborah Walters and Ganapathy Krishnan Computer Science Department, University at Buffalo (SUNY), Buffalo, NY Abstract A Perceptual Significance Hierarchy (PSH) for line art images is developed which represent...	1987	129
582	Plan inference and student modelin Y #M. Visetti CNRS / LIMSI . Universite Paris-Sud (Batiment 508) ORSAY 91406 FRANCE ABSTRACT This paper adresses the problem of building user mod& within the framework of Computer Assisted Instruction (ICAI), and more part...	1987	13
583	Visual Estimation of 3-D Line Segments From Motion - A Mobile Robot Vision System 1 William M. Wells III Artificial Intelligence Center SRI International 333 Ravenswood Avenue Menlo Park, Ca 94025 ARPANET: WellsQai.ai.sri.com Abstract An efficient technique...	1987	130
584	The Sensitivity Of Motion and Structure Computations John L. Barron, Allan D. Jepson* and John K. Tsotsos* Department of Computer Science University of Toronto Toronto, Canada, M5S lA4 Abstract We address the problem of interpreting image velocity fields generated by a moving monocular observer vie...	1987	131
585	Using Generic Geometric Models for Intelligent Sha e Extraction Pascal Fua and Andrew J. Hanson * Artificial Intelligence Center Computer and Information Sciences Division SRI International Abstract Object delineation that is based only on low-level segmen- tatio...	1987	132
586	Detecting Runways in Aerial Images’ 4 A. Huertas, W. Cole and R. Nevatia Institute for Robotics and Intelligent Systems University of Southern California Los Angeles, California 90089-0273 Abstract We are pursuing the detection of runways in aerial images as part of a project to automat...	1987	133
587	HYIWI’I-ESIS TESTING IN A COMPUTATI0NAL THEORY OF VISUAL WORD RECOGMTION Jonathan J. Hull Department of Computer Science State University of New York at Buffalo Buffalo, New York 14260 hull@cs.buff alo.edu ABSTRACT A computational theory of reading and an algorithmic r...	1987	134
588	Department of Computer Science Columbia University New York, N.Y. 10027 This paper describes an approach which integrates several conflicting and corroborating shape-from-texture methods in a single system. The system uses a new data structure, the augmented texel, which combines multiple con...	1987	135
589	K.Prazdny Translation-, rotation-, and scale-invariant recognition of multiple, superimposed, partially specified or occluded objects can be accomplished in a fast, simple, distributed and parallel fashion using localizable features with intrinsic orientation. All known objects are r...	1987	136
590	GRASP Laboratory Department sf Computer and Information Sciences University of Pennsylvania Philadelphia, Pennsylvania 6910445389, USA Abstract Although mail pieces can be classified by shape into paral- lelopipeds and cylinders, they do not conform exactly to these perfect geometrical shapes due t...	1987	137
591	CLOSED FORM SOLUTION TO THE STRUCTURE FROM MOTION PROBLEM FROM LINE CORRESPONDENCES Minas E. Spetsakis John (Yiannis) Aloimonos Center for Automation Research University of Maryland College Park, MD 20742 ABSTRACT A theory is presented for the computation o...	1987	138
592	Data Validation During Diagnosis, A Step Beyond Traditional Sensor Validation. B. Chandrasekaran and W.F Punch III’ Laboratory for Artificial Intelligence Research The Ohio State University Abstract A well known problem in diagnosis is the difficulty of providing correct diagno...	1987	139
593	Building a Common Intelligent Beverlly Woolf and Pat Cunninghamt Department of Computer and Information Science, University of Massachusetts, Amherst, Massachusetts 01003 tThe Hartford Graduate Center, Hartford, Conn 06101 Abstract This article discusses the need fo...	1987	14
594	IM. Development Environment for respective Reasoning Systems Pad R. C&en and Michael Greenberg and Jefferson DeLisio Experimental Knowledge Systems Laboratory University of Massachusetts Amherst, Massachusetts 01IO03~ Abstract We describe a style of problem so...	1987	140
595	ia mprovement ensitivity A ggregatioga Keith L. Downing * Department of Computer and Information Science University of Oregon Eugene, Oregon 97403 Abstract This paper lays the foundation for a diagnostic system that improves its performance by deriv- ing sympt...	1987	141
596	CAMEX - AN EXPERT SYSTEM FOR PROCESS PLANNING ON CNC MACHINES 0. Eliyahu, L. Zaidenberg and M. Ben-Bassat IET - Intelligent Electronics and Faculty of Management, 14 Esser Tachanot St, Tel Aviv University Ramat Hachayal, Tel Aviv, Tel Aviv...	1987	142
597	sesentation A oath to Understan i&al Circuits Robert J. Hall Richard EL Lathrop Artificial Intelligence Lab Artificial Intelligence Lab M.I.T. M.I.T. Cambridge, MA 02139 Cambridge, MA 02139 Robert S. Kirk Semiconductor Division Gould, Inc. ...	1987	143
598	Assistant Professor Department of Civil Engineering St&ml University Stanfold, CA 94305 AbStraCt Database management systems (DBMSs) are impor- tant components of existing integrated computer-aided engineering (CAE) systems. Expert systems (ESs) am being applied to a broad range of engineerin...	1987	144
599	Artificial htelligence Department oneywelll Corporate Systems Development Divisiola 1000 Boome Avenue North Golden Valley, Minanessta 5542 7 A system for continuously providing advice about the operation of some other device or process, rather t...	1987	145

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

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ICML Papers on Interpretability

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ICLR Papers on Interpretability

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ICCV Papers on Interpretability

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EMNLP Papers on Interpretability

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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

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AI Papers on Interpretability

Retrieves papers from the ACL dataset that discuss interpretability or explainability, providing insights into research focus in these areas.