A survey of research in deliberative real-time artificial intelligence

Garvey, Alan; Lesser, Victor

doi:10.1007/bf01088630

Cited by 76 publications

(41 citation statements)

References 29 publications

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“…One major problem in using anytime algorithm is in generating performance profiles of search-based algorithms which allow backtracking [53]. A performance profile is a function that returns the expected quality of an anytime algorithm when given a certain amount of time.…”

Section: Anytime Algorithmsmentioning

confidence: 99%

Supporting Deliberative Real-Time AI Systems: A Fixed Priority Scheduling Approach

Chu

Burns

2007

19th Euromicro Conference on Real-Time Systems (ECRTS'07)

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Constructing deliberative real-time AI systems is challenging due to the high execution-time variance in AI algorithms and the requirement of worst-case bounds for hard real-time guarantees, often resulting in poor use of system resources. Using a motivating case study based on RoboCup, the general problem of resource usage maximization in a real-time AI agent is addressed.In this thesis it was shown that by employing a hybrid task model, for imprecise computation of the form "Prologue-Optional-Epilogue", a variety of AI algorithms with different hard and optional (anytime) timing requirements can be supported. An exact schedulability, based on a simple offset between the Prologue and Epilogue components, is devised but shown to be computationally prohibitive when a large number of imprecise tasks are present. For this reason, a tractable sufficient schedulability test was also devised, inspired by Tindell's analysis, where the time complexity of calculating the busy period of each task is reduced from O(2 n ) to O(2n).Further, with a novel scheduling scheme based on Dual Priority Scheduling, schedulability can be guaranteed for the hard Prologue and Epilogue tasks while the latter can be delayed as much as possible for allowing optional and anytime components to be executed for enhancing system utility. Suggestions on how aperiodic tasks can be scheduled effectively within the framework and how tasks can be prioritized based on their utilities by an efficient algorithm are also provided.The works presented in this thesis provide new advances in fixed priority response time analysis for imprecise computation which, together with present results in the literature, should provide a more comprehensive scheduling framework where real-time AI systems can be suitably supported.i

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Section: Anytime Algorithmsmentioning

confidence: 99%

Supporting Deliberative Real-Time AI Systems: A Fixed Priority Scheduling Approach

Chu

Burns

2007

19th Euromicro Conference on Real-Time Systems (ECRTS'07)

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“…There are various proposals to adapt AI techniques to real-time requirements; the most promising algorithms within this field being Anytime [8] and approximate processing [9]. One line of research in Real-Time AI is related to large applications or hybrid system architectures that embody real-time concerns in many components [9], such as Guardian [10], Phoenix [11], or SA-CIRCA [12].…”

Section: Introductionmentioning

confidence: 99%

Approaching Real-Time Intrusion Detection through MOVICAB-IDS

Navarro

Herrero

Corchado

et al. 2010

Advances in Intelligent and Soft Computing

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This paper presents an extension of MOVICAB-IDS, a Hybrid Intelligent Intrusion Detection System characterized by incorporating temporal control to enable real-time processing and response. The original formulation of MOVICAB-IDS combines artificial neural networks and case-based reasoning within a multiagent system to perform Intrusion Detection in dynamic computer networks. The contribution of the anytime algorithm, one of the most promising to adapt Artificial Intelligent techniques to real-time requirements; is comprehensively presented in this work.

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“…Temporal events, specification of the activities in the states and notes with temporal restrictions are shown in this figure. Finally, the possibility of specifying common methods in the area of Real-Time Artificial Intelligence, such as anytime algorithms [7], multiple methods [13] and anytime progressive algorithms [4] has been included. These specifications can be done by means of their corresponding state diagrams.…”

Section: Goal/task Modelmentioning

confidence: 99%

Developing real-time multi-agent systems

Julián

Botti

2004

ICA

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Abstract. The application of multi-agent systems to real-time environments is an interesting line of work that can provide new solutions to very complex and restrictive systems such as real-time systems. A suitable method for real-time multi-agent system development must take into account the intrinsic characteristics of systems of this type. This work presents an approach for the development of real-time multi-agent systems. The proposed method is called RT-MESSAGE and is based on the MESSAGE methodology.

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A survey of research in deliberative real-time artificial intelligence

Cited by 76 publications

References 29 publications

Supporting Deliberative Real-Time AI Systems: A Fixed Priority Scheduling Approach

Supporting Deliberative Real-Time AI Systems: A Fixed Priority Scheduling Approach

Approaching Real-Time Intrusion Detection through MOVICAB-IDS

Developing real-time multi-agent systems

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