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231
Planning in Interplanetary Space: Theory and Practice
, 2000
"... On May 17th 1999, NASA activated for the first time an AI-based planner/scheduler running on the flight processor of a spacecraft. This was part of the Remote Agent Experiment (RAX), a demonstration of closedloop planning and execution, and model-based state inference and failure recovery. This ..."
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Cited by 165 (22 self)
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On May 17th 1999, NASA activated for the first time an AI-based planner/scheduler running on the flight processor of a spacecraft. This was part of the Remote Agent Experiment (RAX), a demonstration of closedloop planning and execution, and model-based state inference and failure recovery. This paper describes the RAX Planner/Scheduler (RAX-PS), both in terms of the underlying planning framework and in terms of the fielded planner. RAX-PS plans are networks of constraints, built incrementally by consulting a model of the dynamics of the spacecraft. The RAX-PS planning procedure is formally well defined and can be proved to be complete. RAX-PS generates plans that are temporally flexible, allowing the execution system to adjust to actual plan execution conditions without breaking the plan. The practical aspect, developing a mission critical application, required paying attention to important engineering issues such as the design of methods for programmable search contr...
Bridging the gap between planning and scheduling
- KNOWLEDGE ENGINEERING REVIEW
, 2000
"... Planning research in Artificial Intelligence (AI) has often focused on problems where there are cascading levels of action choice and complex interactions between actions. In contrast, Scheduling research has focused on much larger problems where there is little action choice, but the resulting orde ..."
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Cited by 118 (12 self)
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Planning research in Artificial Intelligence (AI) has often focused on problems where there are cascading levels of action choice and complex interactions between actions. In contrast, Scheduling research has focused on much larger problems where there is little action choice, but the resulting ordering problem is hard. In this paper, we give an overview of AI planning and scheduling techniques, focusing on their similarities, differences, and limitations. We also argue that many difficult practical problems lie somewhere between planning and scheduling, and that neither area has the right set of tools for solving these vexing problems.
Model Checking AgentSpeak
- AAMAS'03
, 2003
"... This paper introduces AgentSpeak(F), a variation of the BDI logic programming language AgentSpeak(L) intended to permit the model-theoretic verification of multi-agent systems. After briefly introducing AgentSpeak(F) and discussing its relationship to AgentSpeak(L), we show how AgentSpeak(F) program ..."
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Cited by 111 (18 self)
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This paper introduces AgentSpeak(F), a variation of the BDI logic programming language AgentSpeak(L) intended to permit the model-theoretic verification of multi-agent systems. After briefly introducing AgentSpeak(F) and discussing its relationship to AgentSpeak(L), we show how AgentSpeak(F) programs can be transformed into Promela, the model specification language for the Spin model-checking system. We also describe how specifications written in a simplified form of BDI logic can be transformed into Spin-format linear temporal logic formul. With our approach, it is thus possible to automatically verify whether or not multi-agent systems implemented in AgentSpeak(F) satisfy specifications expressed as BDI logic formul. We illustrate our approach with a short case study, in which we show how BDI properties of a simulated auction system implemented in AgentSpeak(F) were verified.
Cognitive architectures: Research issues and challenges
, 2002
"... In this paper, we examine the motivations for research on cognitive architectures and review some candidates that have been explored in the literature. After this, we consider the capabilities that a cognitive architecture should support, some properties that it should exhibit related to representat ..."
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Cited by 108 (13 self)
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In this paper, we examine the motivations for research on cognitive architectures and review some candidates that have been explored in the literature. After this, we consider the capabilities that a cognitive architecture should support, some properties that it should exhibit related to representation, organization, performance, and learning, and some criteria for evaluating such architectures at the systems level. In closing, we discuss some open issues that should drive future research in this important area. Key words: cognitive architectures, intelligent systems, cognitive processes 1
Adjustable autonomy for human-centered autonomous systems
- on Mars,” in First International Conference of the Mars Society
, 1998
"... We expect a variety of autonomous systems, from rovers to life-support systems, to play a critical role in the success of manned Mars missions. The crew and ground support personnel will want to control and be informed by these systems at varying levels of detail depending on the situation. Moreover ..."
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Cited by 104 (6 self)
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We expect a variety of autonomous systems, from rovers to life-support systems, to play a critical role in the success of manned Mars missions. The crew and ground support personnel will want to control and be informed by these systems at varying levels of detail depending on the situation. Moreover, these systems will need to operate safely in the presence of people and cooperate with them effectively. We call such autonomous systems human-centered in contrast with traditional “black-box ” autonomous systems. Our goal is to design a framework for human-centered autonomous systems that enables users to interact with these systems at whatever level of control is most appropriate whenever they so choose, but minimize the necessity for such interaction. This paper discusses on-going research at the NASA Ames Research Center and the Johnson Space Center in developing human-centered autonomous systems that can be used for a manned Mars mission.
Back to the Future for Consistency-based Trajectory Tracking
- Proceedings of the National Conference on Artificial Intelligence. Menlo Park, CA: AAAI
"... Given a model of a physical process and a sequence of com-mands and observations received over time, the task of an autonomous controller is to determine the likely states of the process and the actions required to move the process to a desired configuration. We introduce a representation and algori ..."
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Cited by 69 (0 self)
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Given a model of a physical process and a sequence of com-mands and observations received over time, the task of an autonomous controller is to determine the likely states of the process and the actions required to move the process to a desired configuration. We introduce a representation and algorithms for incrementally generating approximate belief states for a restricted but relevant class of partially observ-able Markov decision processes with very large state spaces. The algorithm incrementally generates, rather than revises, an approximate belief state at any point by abstracting and sum-marizing segments of the likely trajectories of the process. This enables applications to efficiently maintain a partial be-lief state when it remains consistent with observations and re-visit past assumptions about the process’s evolution when the belief state is ruled out. The system presented has been im-plemented and results on examples from the domain of space-craft control are presented.
Collaborative Control: A Robot-Centric Model for Vehicle Teleoperation
, 1998
"... Telerobotic systems have traditionally been designed and operated from a human point of view. Though this approach suffices for some domains, it is clearly sub-optimal for tasks such as operating multiple vehicles or controlling planetary rovers. Thus, I believe it is worthwhile to examine a new tel ..."
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Cited by 67 (6 self)
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Telerobotic systems have traditionally been designed and operated from a human point of view. Though this approach suffices for some domains, it is clearly sub-optimal for tasks such as operating multiple vehicles or controlling planetary rovers. Thus, I believe it is worthwhile to examine a new teleoperation approach: collaborative control. In this robot-centric model, instead of the human always being "in charge", the robot works as a peer and makes requests of the human. In other words, the human is treated as an imprecise, limited source of planning and information, just like sensors and maps and other noisy modules. To examine the numerous human-machine interaction and system design issues raised by this new approach, I propose to build a vehicle teleoperation system based on collaborative control. In my research, I will show how this approach enables efficient teleoperation and optimizes use of human resources.
A survey of Autonomic Computing -- degrees, models and applications
"... Autonomic Computing is a concept that brings together many fields of computing with the purpose of creating computing systems that self-manage. In its early days it was criticised as being a “hype topic” or a rebadging of some Multi Agent Systems work. In this survey, we hope to show that this was n ..."
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Cited by 65 (1 self)
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Autonomic Computing is a concept that brings together many fields of computing with the purpose of creating computing systems that self-manage. In its early days it was criticised as being a “hype topic” or a rebadging of some Multi Agent Systems work. In this survey, we hope to show that this was not indeed ’hype ’ and that, though it draws on much work already carried out by the Computer Science and Control communities, its innovation is strong and lies in its robust application to the specific self-management of computing systems. To this end, we first provide an introduction to the motivation and concepts of autonomic computing and describe some research that has been seen as seminal in influencing a large proportion of early work. Taking the components of an established reference model in turn, we discuss the works that have provided significant contributions to that area. We then look at larger scaled systems that compose autonomic systems illustrating the hierarchical nature of their architectures. Autonomicity is not a well defined subject and as such different systems adhere to different degrees of Autonomicity, therefore we cross-slice the body of work in terms of these degrees. From this we list the key applications of autonomic computing and discuss the research work that is missing and what we believe the community should be considering.
Verifying Multi-Agent Programs by Model Checking
- Journal of Autonomous Agents and Multi-Agent Systems
, 2006
"... Abstract. This paper gives an overview of our recent work on an approach to verifying multi-agent programs. We automatically translate multi-agent systems programmed in the logic-based agent-oriented programming language AgentSpeak into either Promela or Java, and then use the associated Spin and JP ..."
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Cited by 58 (12 self)
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Abstract. This paper gives an overview of our recent work on an approach to verifying multi-agent programs. We automatically translate multi-agent systems programmed in the logic-based agent-oriented programming language AgentSpeak into either Promela or Java, and then use the associated Spin and JPF model checkers to verify the resulting systems. We also describe the simplified BDI logical language that is used to write the properties we want the systems to satisfy. The approach is illustrated by means of a simple case study.
