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37 results on '"McCluskey, Thomas Leo"'

4. Autonomic Road Transport Support Systems: An Introduction

Author

McCluskey, Thomas Leo, Kotsialos, Apostolos, Müller, Jörg P., Klügl, Franziska, Rana, Omer F., Schumann, René, Brazier, Frances M.T., Series editor, Rana, Omer, Series editor, Strassner, John C., Series editor, McCluskey, Thomas Leo, editor, Kotsialos, Apostolos, editor, Müller, Jörg P., editor, Klügl, Franziska, editor, and Schumann, René, editor

Published
2016
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5. Ontological Support for Modelling Planning Knowledge

Author

Wickler, Gerhard, Chrpa, Lukáš, McCluskey, Thomas Leo, Diniz Junqueira Barbosa, Simone, Series editor, Chen, Phoebe, Series editor, Du, Xiaoyong, Series editor, Filipe, Joaquim, Series editor, Kara, Orhun, Series editor, Kotenko, Igor, Series editor, Liu, Ting, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Fred, Ana, editor, Dietz, Jan L. G., editor, Aveiro, David, editor, and Liu, Kecheng, editor

Published
2015
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11. Learning Action Models for Planning: An Overview of the Hedlamp Project

Author

McCluskey, Thomas Leo, Chrpa, Lukas, Tate, Austin, and Wickler, Gerhard

Abstract

Hedlamp is a UK EPSRC grant funded research project in which we aim to tackle challenges with knowledge engineering of automated planning techniques when applied to real applications. Normally, successful deployment of planning technology relies on groups of planning experts encoding detailed domain models and investing large amounts of time maintaining them.We are developing a high level, application-oriented knowledge engineering framework usable by application developers who want to experiment with the potential of AI Planning,while encoding a precise domain model of some valuable application area. We are developing tools and theory for the framework which support knowledge acquisition, validation and operationality of the domain model. In particular, this project aims to explore the opportunities of applying model translation, adaptation and reformulation techniques to improve the model’s quality, and that of the planning function of which it is a part.In this paper we outline the main areas that Hedlamp has addressed,and overview an automated knowledge acquisition technique that has been tested with real industrial process data.

Published
2016

13. Autonomic Road Transport Support Systems / edited by Thomas Leo McCluskey, Apostolos Kotsialos, J rg P. M ller, Franziska Kl gl, Omer Rana, Ren Schumann.

Author

McCluskey, Thomas Leo. editor., Kotsialos, Apostolos. editor., M ller, J rg P. editor., Kl gl, Franziska. editor., Rana, Omer. editor., Schumann, Ren . editor., SpringerLink (Online service), McCluskey, Thomas Leo. editor., Kotsialos, Apostolos. editor., M ller, J rg P. editor., Kl gl, Franziska. editor., Rana, Omer. editor., Schumann, Ren . editor., and SpringerLink (Online service)

Abstract

The work on Autonomic Road Transport Support (ARTS) presented here aims at meeting the challenge of engineering autonomic behavior in Intelligent Transportation Systems (ITS) by fusing research from the disciplines of traffic engineering and autonomic computing. Ideas and techniques from leading edge artificial intelligence research have been adapted for ITS over the last 30 years. Examples include adaptive control embedded in real time traffic control systems, heuristic algorithms (e.g. in SAT-NAV systems), image processing and computer vision (e.g. in automated surveillance interpretation). Autonomic computing which is inspired from the biological example of the body’s autonomic nervous system is a more recent development. It allows for a more efficient management of heterogeneous distributed computing systems. In the area of computing, autonomic systems are endowed with a number of properties that are generally referred to as self-X properties, including self-configuration, self-healing, self-optimization, self-protection and more generally self-management. Some isolated examples of autonomic properties such as self-adaptation have found their way into ITS technology and have already proved beneficial. This edited volume provides a comprehensive introduction to Autonomic Road Transport Support (ARTS) and describes the development of ARTS systems. It starts out with the visions, opportunities and challenges, then presents the foundations of ARTS and the platforms and methods used and it closes with experiences from real-world applications and prototypes of emerging applications. This makes it suitable for researchers and practitioners in the fields of autonomic computing, traffic and transport management and engineering, AI, and software engineering. Graduate students will benefit from state-of-the-art description, the study of novel methods and the case studies provided.

Published
2016

14. Autonomic Road Transport Support Systems

Author

McCluskey, Thomas Leo, Kotsialos, Apostolos, Müller, Jörg P., Klügl, Franziska, Rana, Omer, Schumann, René, McCluskey, Thomas Leo, Kotsialos, Apostolos, Müller, Jörg P., Klügl, Franziska, Rana, Omer, and Schumann, René

Published
2016
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20. On the completeness of replacing primitive actions with macro-actions and its generalization to planning operators and macro-operators.

Author

Chrpa, Lukáš, McCluskey, Thomas Leo, and Osborne, Hugh

Subjects
*COMPLETENESS theorem, *GENERALIZATION, *AUTOMATED planning & scheduling, *MATHEMATICAL reformulation, *ENCODING, *OPERATOR theory
Abstract

Automated planning, which deals with the problem of generating sequences of actions, is an emerging research topic due to its potentially wide range of real-world application domains. As well as developing and improving planning engines, the acquisition of domain-specific knowledge is a promising way to improve the planning process. Domain-specific knowledge can be encoded into the modelling language that a range of planning engines can accept. This makes encoding domain-specific knowledge planner-independent, and entails reformulating the domain models and/or problem specifications. While many encouraging practical results have been derived from such reformulation methods (e.g. learning macro-actions), little attention has been paid to the theoretical properties such as completeness (keeping solvability of reformulated problems). In this paper, we focus on a special case - removing primitive actions replaced by macro-actions. We provide a theoretical study and come up with conditions under which it is safe to remove primitive actions, so completeness of reformulation is preserved. We extend this study also for planning operators (actions are instances of operators). [ABSTRACT FROM AUTHOR]

Published
2016
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21. Determining Redundant Actions in Sequential Plans.

Author

Chrpa, Luká, Mccluskey, Thomas Leo, and Osborne, Hugh

Abstract

Automated planning even in its simplest form, classical planning, is a computationally hard problem. With the increasing involvement of intelligent systems in everyday life there is a need for more and more advanced planning techniques able to solve planning problems in little (or real) time. However, planners designed to solve planning problems as fast as possible often provide solution plans of low quality. The quality of solution plans can be improved by their post-planning analysis by which redundant actions or optimizable sub plans can be identified. In this paper, we present techniques for determining redundancy of actions in plans. Especially, we present techniques for efficient redundancy checking of pairs of inverse actions. These techniques are accompanied with necessary theoretical foundations and are also empirically evaluated using existing planning systems and standard planning benchmarks. [ABSTRACT FROM PUBLISHER]

Published
2012
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24. An Organic Computing Approach to Resilient Traffic Management

Author

Sommer, Matthias, Tomforde, Sven, Hähner, Jörg, Brazier, Frances M.T., Series editor, Rana, Omer, Series editor, Strassner, John C., Series editor, McCluskey, Thomas Leo, editor, Kotsialos, Apostolos, editor, Müller, Jörg P., editor, Klügl, Franziska, editor, and Schumann, René, editor

Published
2016
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26. A Self-Optimization Traffic Model by Multilevel Formalism

Author

Stoilov, Todor, Stoilova, Krasimira, Brazier, Frances M.T., Series editor, Rana, Omer, Series editor, Strassner, John C., Series editor, McCluskey, Thomas Leo, editor, Kotsialos, Apostolos, editor, Müller, Jörg P., editor, Klügl, Franziska, editor, and Schumann, René, editor

Published
2016
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27. Traffic Signal Control with Autonomic Features

Author

Kosonen, Iisakki, Ma, Xiaoliang, Brazier, Frances M.T., Series editor, Rana, Omer, Series editor, Strassner, John C., Series editor, McCluskey, Thomas Leo, editor, Kotsialos, Apostolos, editor, Müller, Jörg P., editor, Klügl, Franziska, editor, and Schumann, René, editor

Published
2016
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28. TIMIPLAN: A Tool for Transportation Tasks

Author

García, Javier, Torralba, Álvaro, Florez, José E., Borrajo, Daniel, López, Carlos Linares, García-Olaya, Ángel, Brazier, Frances M.T., Series editor, Rana, Omer, Series editor, Strassner, John C., Series editor, McCluskey, Thomas Leo, editor, Kotsialos, Apostolos, editor, Müller, Jörg P., editor, Klügl, Franziska, editor, and Schumann, René, editor

Published
2016
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29. A Multiagent Approach to Modeling Autonomic Road Transport Support Systems

Author

Fiosins, Maksims, Friedrich, Bernhard, Görmer, Jana, Mattfeld, Dirk, Müller, Jörg P., Tchouankem, Hugues, Brazier, Frances M.T., Series editor, Rana, Omer, Series editor, Strassner, John C., Series editor, McCluskey, Thomas Leo, editor, Kotsialos, Apostolos, editor, Müller, Jörg P., editor, Klügl, Franziska, editor, and Schumann, René, editor

Published
2016
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31. Learning-Based Control Algorithm for Ramp Metering

Author

Gregurić, Martin, Ivanjko, Edouard, Mandžuka, Sadko, Brazier, Frances M.T., Series editor, Rana, Omer, Series editor, Strassner, John C., Series editor, McCluskey, Thomas Leo, editor, Kotsialos, Apostolos, editor, Müller, Jörg P., editor, Klügl, Franziska, editor, and Schumann, René, editor

Published
2016
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32. Applying the PAUSETA Protocol in Traffic Management Plans

Author

Prades-Farrón, Miguel, García, Luis A., Tomás, Vicente R., Brazier, Frances M.T., Series editor, Rana, Omer, Series editor, Strassner, John C., Series editor, McCluskey, Thomas Leo, editor, Kotsialos, Apostolos, editor, Müller, Jörg P., editor, Klügl, Franziska, editor, and Schumann, René, editor

Published
2016
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34. An Autonomic Methodology for Embedding Self-tuning Competence in Online Traffic Control Systems

Author

Kouvelas, Anastasios, Manolis, Diamantis, Kosmatopoulos, Elias, Papamichail, Ioannis, Papageorgiou, Markos, Brazier, Frances M.T., Series editor, Rana, Omer, Series editor, Strassner, John C., Series editor, McCluskey, Thomas Leo, editor, Kotsialos, Apostolos, editor, Müller, Jörg P., editor, Klügl, Franziska, editor, and Schumann, René, editor

Published
2016
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35. Electric Vehicles in Road Transport and Electric Power Networks

Author

Marmaras, Charalampos, Xydas, Erotokritos, Cipcigan, Liana M., Rana, Omer, Klügl, Franziska, Brazier, Frances M.T., Series editor, Rana, Omer, Series editor, Strassner, John C., Series editor, McCluskey, Thomas Leo, editor, Kotsialos, Apostolos, editor, Müller, Jörg P., editor, Klügl, Franziska, editor, and Schumann, René, editor

Published
2016
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37. Efektivní reprezentace a konverze plánovacích problémů

Author

Toropila, Daniel, Barták, Roman, McCluskey, Thomas Leo, and Pěchouček, Michal

Subjects
stavové proměnné, constraint satisfaction, splňování podmínek, domain-independent planning, umělá inteligence, state variables, problem modeling, doménově nezávislé plánování, modelování problémů, artificial intelligence
Abstract

Title E cient Representations and Conversions of Planning Problems Author Daniel Toropila Department Department of Theoretical Computer Science and Mathematical Logic Supervisor prof. RNDr. Roman Barták, Ph.D. Abstract The e ciency of all types of planning systems is strongly dependent on the in- put formulation, the structure of which must be exploited in order to provide an improved e ciency. Hence, the state-variable representation (SAS+ ) has be- come the input of choice for many modern planners. As majority of planning problems is encoded using a classical representation, several techniques for trans- lation into SAS+ have been developed in the past. These techniques, however, ignore the instance-specific information of planning problems. Therefore, we in- troduce a novel algorithm for constructing SAS+ that fully utilizes the information from the goal and the initial state. By performing an exhaustive experimental evaluation we demonstrate that for many planning problems the novel approach generates a more e cient encoding, providing thus an improved solving time. Finally, we present an overview and performance evaluation of several constraint models based on SAS+ and finite-state automata, showing that they represent a competitive alternative in the category of constraint-based planners. Keywords...

Published
2015

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