- Email: [email protected]
Introduction to the special volume on constraint-based reasoning
Artificial Intelligence 58 (1992) 1-2 Elsevier ARTINT 947
Introduction to the Special Volume on Constraint-Based Reasoning E u g e n e C. F r e u d e r Department of Computer Science, University of New Hampshire, Durham, NH 03824, USA
A l a n K. M a c k w o r t h Department of Computer Science, University of British Columbia, Vancouver, BC, Canada V6 T I Z2
Constraint-based reasoning has long been a productive research focus for researchers in artificial intelligence. Richard Fikes, for example, reported an early contribution in the very first issue of this journal. Lately, the subject has enjoyed markedly increased attention. This interest has been influenced by, and in turn influenced, an acceleration of progress on many fronts. This special volume reports new results in three important areas: paradigms, tractability, and applications.
Paradigms The first paper, by Alan Mackworth, places the standard AI formalization of constraint satisfaction problems within a space of logical representation and reasoning systems. Freuder and Wallace extend standard constraint satisfaction methods to cope with situations in which it is unnecessary or impossible to satisfy all the constraints. Eero Hyv6nen presents a new approach to satisfying constraints on intervals. Van Hentenryck, Simonis and Dincbas use constraint logic programming to solve practical problems.
Tractability Minton, Johnston, Philips and Laird, demonstrate that for some problems it can be very efficient to satisfy constraints by refining an initial imperfect Correspondence to: E.C. Freuder, Department of Computer Science, University of New Hampshire, Durham, NH 03824, USA. Fax: (603) 862-3775. E-mail: [email protected]. 0004-3702/92/$05.00 © 1992--Elsevier Science Publishers B.V. All rights reserved
2
E.C. Freuder, A . K . Mackworth
solution. Cooper and Swain demonstrate how parallelism can speed up constraint satisfaction. Dechter and Pearl discuss identification of computationally tractable structures. Zweben, Davis, Daun, Drascher, Deale and Eskey use a form of explanation-based learning to acquire search control knowledge for constraint-based scheduling.
Applications Peter van Beck shows how to reason about qualitative temporal constraints. Glenn Kramer describes a geometric constraint engine. Qiang Yang develops a computational theory of conflict resolution in planning using constraint satisfaction techniques. Of course, this tripartite framework for the volume does not result in mutually exclusive categories. For example, the "tractability" paper by Zweben et al. presents a scheduling application while the "applications" paper of van Beck presents computationally efficient algorithms. The volume itself has its roots in the Eleventh International Joint Conference on Artificial Intelligence (IJCAI-89) and the Eighth National Conference on Artificial Intelligence (AAAI-90), where interest in constraint-based reasoning blossomed into two workshops and a plethora of excellent conference papers. The editors invited the authors of a selection of these papers to submit new papers with their latest results. Unfortunately, the selection was necessarily limited: many fine papers could not be considered due to a variety of constraints such as limited space, breadth of coverage and availability of the paper. Submissions were sent out for formal review; the revised, accepted papers appear here. We should note that the editors' papers appear first because, although they are intended as research contributions, we hope that each in its own way also serves a tutorial function in this volume: the first in placing constraint-based reasoning in its logical context, the second in reviewing some of the standard constraint satisfaction algorithms. The most satisfying outcome of bringing this special volume together is the demonstration of the coherence of the constraint-based reasoning paradigm. It provides a fundamental set of theoretical and practical tools that cuts across (and unifies) the traditional division of artificial intelligence into task domains such as perception, language use, reasoning, planning, learning and action. Another outcome is the solidification of the connections of this approach with related developments in areas such as logic programming, numerical computation, algorithm design, complexity theory, operations research, graphics and robotics.
Introduction to the Special Volume on Constraint-Based Reasoning E u g e n e C. F r e u d e r Department of Computer Science, University of New Hampshire, Durham, NH 03824, USA
A l a n K. M a c k w o r t h Department of Computer Science, University of British Columbia, Vancouver, BC, Canada V6 T I Z2
Constraint-based reasoning has long been a productive research focus for researchers in artificial intelligence. Richard Fikes, for example, reported an early contribution in the very first issue of this journal. Lately, the subject has enjoyed markedly increased attention. This interest has been influenced by, and in turn influenced, an acceleration of progress on many fronts. This special volume reports new results in three important areas: paradigms, tractability, and applications.
Paradigms The first paper, by Alan Mackworth, places the standard AI formalization of constraint satisfaction problems within a space of logical representation and reasoning systems. Freuder and Wallace extend standard constraint satisfaction methods to cope with situations in which it is unnecessary or impossible to satisfy all the constraints. Eero Hyv6nen presents a new approach to satisfying constraints on intervals. Van Hentenryck, Simonis and Dincbas use constraint logic programming to solve practical problems.
Tractability Minton, Johnston, Philips and Laird, demonstrate that for some problems it can be very efficient to satisfy constraints by refining an initial imperfect Correspondence to: E.C. Freuder, Department of Computer Science, University of New Hampshire, Durham, NH 03824, USA. Fax: (603) 862-3775. E-mail: [email protected]. 0004-3702/92/$05.00 © 1992--Elsevier Science Publishers B.V. All rights reserved
2
E.C. Freuder, A . K . Mackworth
solution. Cooper and Swain demonstrate how parallelism can speed up constraint satisfaction. Dechter and Pearl discuss identification of computationally tractable structures. Zweben, Davis, Daun, Drascher, Deale and Eskey use a form of explanation-based learning to acquire search control knowledge for constraint-based scheduling.
Applications Peter van Beck shows how to reason about qualitative temporal constraints. Glenn Kramer describes a geometric constraint engine. Qiang Yang develops a computational theory of conflict resolution in planning using constraint satisfaction techniques. Of course, this tripartite framework for the volume does not result in mutually exclusive categories. For example, the "tractability" paper by Zweben et al. presents a scheduling application while the "applications" paper of van Beck presents computationally efficient algorithms. The volume itself has its roots in the Eleventh International Joint Conference on Artificial Intelligence (IJCAI-89) and the Eighth National Conference on Artificial Intelligence (AAAI-90), where interest in constraint-based reasoning blossomed into two workshops and a plethora of excellent conference papers. The editors invited the authors of a selection of these papers to submit new papers with their latest results. Unfortunately, the selection was necessarily limited: many fine papers could not be considered due to a variety of constraints such as limited space, breadth of coverage and availability of the paper. Submissions were sent out for formal review; the revised, accepted papers appear here. We should note that the editors' papers appear first because, although they are intended as research contributions, we hope that each in its own way also serves a tutorial function in this volume: the first in placing constraint-based reasoning in its logical context, the second in reviewing some of the standard constraint satisfaction algorithms. The most satisfying outcome of bringing this special volume together is the demonstration of the coherence of the constraint-based reasoning paradigm. It provides a fundamental set of theoretical and practical tools that cuts across (and unifies) the traditional division of artificial intelligence into task domains such as perception, language use, reasoning, planning, learning and action. Another outcome is the solidification of the connections of this approach with related developments in areas such as logic programming, numerical computation, algorithm design, complexity theory, operations research, graphics and robotics.