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527
Systematic Nonlinear Planning
 In Proceedings of the Ninth National Conference on Artificial Intelligence
, 1991
"... This paper presents a simple, sound, complete, and systematic algorithm for domain independent STRIPS planning. Simplicity is achieved by starting with a ground procedure and then applying a general, and independently verifiable, lifting transformation. Previous planners have been designed directly ..."
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Cited by 449 (3 self)
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This paper presents a simple, sound, complete, and systematic algorithm for domain independent STRIPS planning. Simplicity is achieved by starting with a ground procedure and then applying a general, and independently verifiable, lifting transformation. Previous planners have been designed directly as lifted procedures. Our ground procedure is a ground version of Tate's NONLIN procedure. In Tate's procedure one is not required to determine whether a prerequisite of a step in an unfinished plan is guaranteed to hold in all linearizations. This allows Tate's procedure to avoid the use of Chapman's modal truth criterion. Systematicity is the property that the same plan, or partial plan, is never examined more than once. Systematicity is achieved through a simple modification of Tate's procedure.
Planning as heuristic search,
 5 – 33, ISSN
, 2001
"... Abstract In the AIPS98 Planning Contest, the HSP planner showed that heuristic search planners can be competitive with stateoftheart Graphplan and SAT planners. Heuristic search planners like HSP transform planning problems into problems of heuristic search by automatically extracting heuristics ..."
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Cited by 421 (33 self)
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Abstract In the AIPS98 Planning Contest, the HSP planner showed that heuristic search planners can be competitive with stateoftheart Graphplan and SAT planners. Heuristic search planners like HSP transform planning problems into problems of heuristic search by automatically extracting heuristics from Strips encodings. They differ from specialized problem solvers such as those developed for the 24Puzzle and Rubik's Cube in that they use a general declarative language for stating problems and a general mechanism for extracting heuristics from these representations. In this paper, we study a family of heuristic search planners that are based on a simple and general heuristic that assumes that action preconditions are independent. The heuristic is then used in the context of bestfirst and hillclimbing search algorithms, and is tested over a large collection of domains. We then consider variations and extensions such as reversing the direction of the search for speeding node evaluation, and extracting information about propositional invariants for avoiding deadends. We analyze the resulting planners, evaluate their performance, and explain when they do best. We also compare the performance of these planners with two stateoftheart planners, and show that the simplest planner based on a pure bestfirst search yields the most solid performance over a large set of problems. We also discuss the strengths and limitations of this approach, establish a correspondence between heuristic search planning and Graphplan, and briefly survey recent ideas that can reduce the current gap in performance between general heuristic search planners and specialized solvers.
Adopt: asynchronous distributed constraint optimization with quality guarantees
 ARTIFICIAL INTELLIGENCE LABORATORY, MASSACHUSETTS INSTITUTE OF TECHNOLOGY
, 2005
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Motion Planning in Dynamic Environments using Velocity Obstacles
 International Journal of Robotics Research
, 1998
"... Abstract!llis paper presents a new approach for rot)ot]notion planning in dynamic environments, based on the concept of Velocity Obstacle.,4 velocity obstacle defines the set of robot velocities that would result in a collision between tlie robot and an obstacle moving at a given velocity. The avo ..."
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Cited by 206 (6 self)
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Abstract!llis paper presents a new approach for rot)ot]notion planning in dynamic environments, based on the concept of Velocity Obstacle.,4 velocity obstacle defines the set of robot velocities that would result in a collision between tlie robot and an obstacle moving at a given velocity. The avoidance maneuver a { a specific time is thus computed by selecting robot’s velocities out of that set. ~’lle set [If all avoid inp, velocities is redllced to the dynamica]]y fcasib]e maneuvers by considering the robot’s acceleration constraints. This computation is re])eatecl at regular time intro vals to account for genera] obstacle trajectories. The t rajtxtory from start to goal can be com])uted by searching a tree of feasible avoidance maneuvers C.olnputecl at discrete time ini ervals. An exhaustive search of the tree yields nearoptima] trajectories that either minimize distance or motion time. A heuristic search of t}le tree yields trajectories that satisfy a l)rioritized list of objectives, such as reaching t}~e goal, maximizing speed, and achieving a desired trajectory structure. The heuristic approach is computational]y ctlicientl a])plic.al)le to online planning of industrial robots, performing assembly tasks cm lnoving conveyers, and to intelligent vehicles negotiating freeway traffic. The method is demonstrated for planning the trajectory of an automated vehicle in an Intelligent Vehicle Highway System scenario. 1.
The Utility of Knowledge in Inductive Learning
, 1992
"... In this paper, we demonstrate how different forms of background knowledge can be integrated with an inductive method for generating constantfree Horn clause rules. Furthermore, we evaluate, both theoretically and empirically, the effect that these types of knowledge have on the cost of learning a r ..."
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Cited by 154 (22 self)
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In this paper, we demonstrate how different forms of background knowledge can be integrated with an inductive method for generating constantfree Horn clause rules. Furthermore, we evaluate, both theoretically and empirically, the effect that these types of knowledge have on the cost of learning a rule and on the accuracy of a learned rule. Moreover, we demonstrate that a hybrid explanationbased and inductive learning method can advantageously use an approximate domain theory, even when this theory is incorrect and incomplete. 1 Introduction Most existing systems that combine empirical and explanationbased learning severely restrict the complexity of the language for expressing the concept definition. For example, some systems require that the concept definition be expressed in terms of attributevalue pairs (Lebowitz, 1986; Danyluk, 1989). Others effectively restrict the concept definition language to that of propositional calculus, by only allowing unary predicates (Hirsh, 1989;...
Disjoint pattern database heuristics
 Artificial Intelligence
, 2002
"... We explore a method for computing admissible heuristic evaluation functions for search problems. It utilizes pattern databases (Culberson & Schaeffer, 1998), which are precomputed tables of the exact cost of solving various subproblems of an existing problem. Unlike standard pattern database heu ..."
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Cited by 141 (36 self)
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We explore a method for computing admissible heuristic evaluation functions for search problems. It utilizes pattern databases (Culberson & Schaeffer, 1998), which are precomputed tables of the exact cost of solving various subproblems of an existing problem. Unlike standard pattern database heuristics, however, we partition our problems into disjoint subproblems, so that the costs of solving the different subproblems can be added together without overestimating the cost of solving the original problem. Previously (Korf & Felner, 2002) we showed how to statically partition the slidingtile puzzles into disjoint groups of tiles to compute an admissible heuristic, using the same partition for each state and problem instance. Here we extend the method and show that it applies to other domains as well. We also present another method for additive heuristics which we call dynamically partitioned pattern databases. Here we partition the problem into disjoint subproblems for each state of the search dynamically. We discuss the pros and cons of each of these methods and apply both methods to three different problem domains: the slidingtile puzzles, the 4peg Towers of Hanoi problem, and finding an optimal vertex cover of a graph. We find that in some problem domains, static partitioning is most effective, while in others dynamic partitioning is a better choice. In each of these problem domains, either statically partitioned or dynamically partitioned pattern database heuristics are the best known heuristics for the problem.
An Asynchronous Complete Method for Distributed Constraint Optimization
 In AAMAS
, 2003
"... We present a new polynomialspace algorithm, called Adopt, for distributed constraint optimization (DCOP). DCOP is able to model a large class of collaboration problems in multiagent systems where a solution within given quality parameters must be found. Existing methods for DCOP are not able to pr ..."
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Cited by 132 (30 self)
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We present a new polynomialspace algorithm, called Adopt, for distributed constraint optimization (DCOP). DCOP is able to model a large class of collaboration problems in multiagent systems where a solution within given quality parameters must be found. Existing methods for DCOP are not able to provide theoretical guarantees on global solution quality while operating both efficiently and asynchronously. Adopt is guaranteed to find an optimal solution, or a solution within a userspecified distance from the optimal, while allowing agents to execute asynchronously and in parallel. Adopt obtains these properties via a distributed search algorithm with several novel characteristics including the ability for each agent to make local decisions based on currently available information and without necessarily having global certainty. Theoretical analysis shows that Adopt provides provable quality guarantees, while experimental results show that Adopt is significanfly more efficient than synchronous methods. The speedups are shown to be partly due to the novel search strategy employed and partly due to the asynchrony of the algorithm.
Optimal Composition of RealTime Systems
 ARTIFICIAL INTELLIGENCE
, 1996
"... Realtime systems are designed for environments in which the utility of actions is strongly timedependent. Recent work by Dean, Horvitz and others has shown that anytime algorithms are a useful tool for realtime system design, since they allow computation time to be traded for decision quality. In ..."
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Cited by 123 (21 self)
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Realtime systems are designed for environments in which the utility of actions is strongly timedependent. Recent work by Dean, Horvitz and others has shown that anytime algorithms are a useful tool for realtime system design, since they allow computation time to be traded for decision quality. In order to construct complex systems, however, we need to be able to compose larger systems from smaller, reusable anytime modules. This paper addresses two basic problems associated with composition: how to ensure the interruptibility of the composed system
A Prolog Technology Theorem Prover: Implementation by an Extended Prolog Compiler
 Journal of Automated Reasoning
, 1987
"... A Prolog technology theorem prover (PTTP) is an extension of Prolog that is complete for the full firstorder predicate calculus. It differs from Prolog in its use of unification with the occurs check for soundness, the modelelimination reduction rule that is added to Prolog inferences to make the ..."
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Cited by 110 (2 self)
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A Prolog technology theorem prover (PTTP) is an extension of Prolog that is complete for the full firstorder predicate calculus. It differs from Prolog in its use of unification with the occurs check for soundness, the modelelimination reduction rule that is added to Prolog inferences to make the inference system complete, and depthfirst iterativedeepening search instead of unbounded depthfirst search to make the search strategy complete. A Prolog technology theorem prover has been implemented by an extended PrologtoLISP compiler that supports these additional features. It is capable of proving theorems in the full firstorder predicate calculus at a rate of thousands of inferences per second. 1 This is a revised and expanded version of a paper presented at the 8th International Conference on Automated Deduction, Oxford, England, July 1986, and is to appear in Journal of Automated Reasoning. This research was supported by the Defense Advanced Research Projects Agency under Co...