George Alexander, Anita Raja, and David Musliner
- Published in print:
- 2011
- Published Online:
- August 2013
- ISBN:
- 9780262014809
- eISBN:
- 9780262295284
- Item type:
- chapter
- Publisher:
- The MIT Press
- DOI:
- 10.7551/mitpress/9780262014809.003.0005
- Subject:
- Computer Science, Artificial Intelligence
This chapter describes efforts to add metalevel control capabilities to the Informed Unroller agent (IU-agent), a scheduling agent based on the Markov decision process (MDP) formalism designed to ...
More
This chapter describes efforts to add metalevel control capabilities to the Informed Unroller agent (IU-agent), a scheduling agent based on the Markov decision process (MDP) formalism designed to operate in a cooperative multiagent environment. It begins by reviewing related work in metalevel control, especially in the area of performance profiling. It then provides background information on Markov decision processes and the TAEMS modeling language (a derivative of which was used to represent the IU- agent’s tasks) along with a description of the IU-agent. This is followed by a discussion of the implemented metalevel control approach and experimental results indicating its advantages for the IU-agent. The chapter concludes by presenting lessons learned over the course of implementing metalevel control for the IU-agent and suggestions for future work in metalevel control.Less
This chapter describes efforts to add metalevel control capabilities to the Informed Unroller agent (IU-agent), a scheduling agent based on the Markov decision process (MDP) formalism designed to operate in a cooperative multiagent environment. It begins by reviewing related work in metalevel control, especially in the area of performance profiling. It then provides background information on Markov decision processes and the TAEMS modeling language (a derivative of which was used to represent the IU- agent’s tasks) along with a description of the IU-agent. This is followed by a discussion of the implemented metalevel control approach and experimental results indicating its advantages for the IU-agent. The chapter concludes by presenting lessons learned over the course of implementing metalevel control for the IU-agent and suggestions for future work in metalevel control.
Anita Raja, George Alexander, Victor R. Lesser, and Michael Krainin
- Published in print:
- 2011
- Published Online:
- August 2013
- ISBN:
- 9780262014809
- eISBN:
- 9780262295284
- Item type:
- chapter
- Publisher:
- The MIT Press
- DOI:
- 10.7551/mitpress/9780262014809.003.0013
- Subject:
- Computer Science, Artificial Intelligence
This chapter presents a generalized metalevel control framework for multiagent systems and discusses the issues involved in extending single-agent metalevel control to a team of cooperative agents ...
More
This chapter presents a generalized metalevel control framework for multiagent systems and discusses the issues involved in extending single-agent metalevel control to a team of cooperative agents requiring coordination. It presents a methodology for constructing a class of Markov decision processes (MDPs) that can model the interactions necessary for coordinating metalevel control among multiple agents.Less
This chapter presents a generalized metalevel control framework for multiagent systems and discusses the issues involved in extending single-agent metalevel control to a team of cooperative agents requiring coordination. It presents a methodology for constructing a class of Markov decision processes (MDPs) that can model the interactions necessary for coordinating metalevel control among multiple agents.
Michael T. Cox and Anita Raja (eds)
- Published in print:
- 2011
- Published Online:
- August 2013
- ISBN:
- 9780262014809
- eISBN:
- 9780262295284
- Item type:
- book
- Publisher:
- The MIT Press
- DOI:
- 10.7551/mitpress/9780262014809.001.0001
- Subject:
- Computer Science, Artificial Intelligence
The capacity to think about our own thinking may lie at the heart of what it means to be both human and intelligent. Philosophers and cognitive scientists have investigated these matters for many ...
More
The capacity to think about our own thinking may lie at the heart of what it means to be both human and intelligent. Philosophers and cognitive scientists have investigated these matters for many years. Researchers in artificial intelligence have gone further, attempting to implement actual machines that mimic, simulate, and perhaps even replicate this capacity, called metareasoning. This book offers a variety of perspectives—drawn from philosophy, cognitive psychology, and computer science—on reasoning about the reasoning process. It offers a simple model of reasoning about reason as a framework for its discussions. Following this framework, the contributors consider metalevel control of computational activities, introspective monitoring, distributed metareasoning, and, putting all these aspects of metareasoning together, models of the self. Taken together, the chapters offer an integrated narrative on metareasoning themes from both artificial intelligence and cognitive science perspectives.Less
The capacity to think about our own thinking may lie at the heart of what it means to be both human and intelligent. Philosophers and cognitive scientists have investigated these matters for many years. Researchers in artificial intelligence have gone further, attempting to implement actual machines that mimic, simulate, and perhaps even replicate this capacity, called metareasoning. This book offers a variety of perspectives—drawn from philosophy, cognitive psychology, and computer science—on reasoning about the reasoning process. It offers a simple model of reasoning about reason as a framework for its discussions. Following this framework, the contributors consider metalevel control of computational activities, introspective monitoring, distributed metareasoning, and, putting all these aspects of metareasoning together, models of the self. Taken together, the chapters offer an integrated narrative on metareasoning themes from both artificial intelligence and cognitive science perspectives.
Michael T. Cox and Anita Raja
- Published in print:
- 2011
- Published Online:
- August 2013
- ISBN:
- 9780262014809
- eISBN:
- 9780262295284
- Item type:
- chapter
- Publisher:
- The MIT Press
- DOI:
- 10.7551/mitpress/9780262014809.003.0001
- Subject:
- Computer Science, Artificial Intelligence
This introductory chapter begins with a brief discussion of the concept of metareasoning. It then provides manifesto in an attempt to present in plain language and simple diagrams a description of a ...
More
This introductory chapter begins with a brief discussion of the concept of metareasoning. It then provides manifesto in an attempt to present in plain language and simple diagrams a description of a model of metareasoning that mirrors the action-selection and perception cycle in first-order reasoning. Many theories and implementations are covered by this model, including those concerning metalevel control, introspective monitoring, distributed metareasoning, and models of self. An overview of the subsequent chapters is also presented.Less
This introductory chapter begins with a brief discussion of the concept of metareasoning. It then provides manifesto in an attempt to present in plain language and simple diagrams a description of a model of metareasoning that mirrors the action-selection and perception cycle in first-order reasoning. Many theories and implementations are covered by this model, including those concerning metalevel control, introspective monitoring, distributed metareasoning, and models of self. An overview of the subsequent chapters is also presented.
Paul Robertson and Robert Laddaga
- Published in print:
- 2011
- Published Online:
- August 2013
- ISBN:
- 9780262014809
- eISBN:
- 9780262295284
- Item type:
- chapter
- Publisher:
- The MIT Press
- DOI:
- 10.7551/mitpress/9780262014809.003.0007
- Subject:
- Computer Science, Artificial Intelligence
This chapter describes metareasoning in an image interpretation architecture called GRAVA (Grounded Reflective Adaptive Vision Architecture), where the goal is to produce good image interpretations ...
More
This chapter describes metareasoning in an image interpretation architecture called GRAVA (Grounded Reflective Adaptive Vision Architecture), where the goal is to produce good image interpretations under a wide range of environmental conditions. GRAVA employs metareasoning because it provides the mechanisms necessary to support two of the core problems of self-adaptive software—a mechanism for reasoning about the state of the computational system and a mechanism for making changes to it. These are referred to as introspective monitoring and metalevel control, respectively.Less
This chapter describes metareasoning in an image interpretation architecture called GRAVA (Grounded Reflective Adaptive Vision Architecture), where the goal is to produce good image interpretations under a wide range of environmental conditions. GRAVA employs metareasoning because it provides the mechanisms necessary to support two of the core problems of self-adaptive software—a mechanism for reasoning about the state of the computational system and a mechanism for making changes to it. These are referred to as introspective monitoring and metalevel control, respectively.
