Kutluhan Erol

Bio: Kutluhan Erol is an academic researcher from University of Maryland, College Park. The author has contributed to research in topics: Hierarchical task network & Automated planning and scheduling. The author has an hindex of 18, co-authored 25 publications receiving 2591 citations. Previous affiliations of Kutluhan Erol include İzmir University of Economics.

HTN planning: complexity and expressivity

Abstract: Most practical work on AI planning systems during the last fifteen years has been based on hierarchical task network (HTN) decomposition, but until now, there has been very little analytical work on the properties of HTN planners. This paper describes how the complexity of HTN planning varies with various conditions on the task networks.

UMCP: a sound and complete procedure for hierarchical task-network planning

Abstract: One big obstacle to understanding the nature of hierarchical task network (HTN) planning has been the lack of a clear theoretical framework In particular, no one has yet presented a clear and concise HTN algorithm that is sound and complete In this paper, we present a formal syntax and semantics for HTN planning Based on this syntax and semantics, we are able to define an algorithm for HTN planning and prove it sound and complete

Semantics for hierarchical task-network planning

Abstract: : One big obstacle to understanding the nature of hierarchical task network (HTN) planning has been the lack of a clear theoretical framework. In particular, no one has yet presented a clear and concise HTN algorithm that is sound and complete. In this paper, the authors present a formal syntax and semantics for HTN planning. Based on this syntax and semantics, they are able to define an algorithm for HTN planning and prove that it is sound and complete. They also develop several definitions of expressivity for planning languages and prove that HTN planning is strictly more expressive than STRIPS-style planning according to those definitions.

Complexity results for HTN planning

Abstract: Most practical work on AI planning systems during the last fifteen years has been based on Hierarchical Task Network (HTN) decomposition, but until now, there has been very little analytical work on the properties of HTN planners. This paper describes how the complexity of HTN planning varies with various conditions on the task networks, and how it compares to STRIPS-style planning.

Artificial Intelligence: A New Synthesis

Abstract: Intelligent agents are employed as the central characters in this new introductory text. Beginning with elementary reactive agents, Nilsson gradually increases their cognitive horsepower to illustrate the most important and lasting ideas in AI. Neural networks, genetic programming, computer vision, heuristic search, knowledge representation and reasoning, Bayes networks, planning, and language understanding are each revealed through the growing capabilities of these agents. The book provides a refreshing and motivating new synthesis of the field by one of AI's master expositors and leading researchers. Artificial Intelligence: A New Synthesis takes the reader on a complete tour of this intriguing new world of AI. * An evolutionary approach provides a unifying theme * Thorough coverage of important AI ideas, old and new * Frequent use of examples and illustrative diagrams * Extensive coverage of machine learning methods throughout the text * Citations to over 500 references * Comprehensive index Table of Contents 1 Introduction 2 Stimulus-Response Agents 3 Neural Networks 4 Machine Evolution 5 State Machines 6 Robot Vision 7 Agents that Plan 8 Uninformed Search 9 Heuristic Search 10 Planning, Acting, and Learning 11 Alternative Search Formulations and Applications 12 Adversarial Search 13 The Propositional Calculus 14 Resolution in The Propositional Calculus 15 The Predicate Calculus 16 Resolution in the Predicate Calculus 17 Knowledge-Based Systems 18 Representing Commonsense Knowledge 19 Reasoning with Uncertain Information 20 Learning and Acting with Bayes Nets 21 The Situation Calculus 22 Planning 23 Multiple Agents 24 Communication Among Agents 25 Agent Architectures

Sold!: auction methods for multirobot coordination

Abstract: The key to utilizing the potential of multirobot systems is cooperation. How can we achieve cooperation in systems composed of failure-prone autonomous robots operating in noisy, dynamic environments? We present a method of dynamic task allocation for groups of such robots. We implemented and tested an auction-based task allocation system which we call MURDOCH, built upon a principled, resource centric, publish/subscribe communication model. A variant of the Contract Net Protocol, MURDOCH produces a distributed approximation to a global optimum of resource usage. We validated MURDOCH in two very different domains: a tightly coupled multirobot physical manipulation task and a loosely coupled multirobot experiment in long-term autonomy. The primary contribution of the paper is to show empirically that distributed negotiation mechanisms such as MURDOCH are viable and effective for coordinating physical multirobot systems.

Pushing the envelope: planning, propositional logic, and stochastic search

Abstract: Planning is a notoriously hard combinatorial search problem. In many interesting domains, current planning algorithms fail to scale up gracefully. By combining a general, stochastic search algorithm and appropriate problem encodings based on propositional logic, we are able to solve hard planning problems many times faster than the best current planning systems. Although stochastic methods have been shown to be very effective on a wide range of scheduling problems, this is the first demonstration of its power on truly challenging classical planning instances. This work also provides a new perspective on representational issues in planning.

SHOP2: an HTN planning system

Abstract: The SHOP2 planning system received one of the awards for distinguished performance in the 2002 International Planning Competition. This paper describes the features of SHOP2 which enabled it to excel in the competition, especially those aspects of SHOP2 that deal with temporal and metric planning domains.