Adaptive and context-aware reconciliation of reactive and pro-active behavior in evolving systems

Goce Trajcevski; Peter Scheuermann

doi:10.1007/978-3-540-77503-4_4

Adaptive and context-aware reconciliation of reactive and pro-active behavior in evolving systems

Goce Trajcevski^*, Peter Scheuermann

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

One distinct characteristics of the context-aware systems is their ability to react and adapt to the evolution of the environment, which is often a result of changes in the values of various (possibly correlated) attributes. Based on these changes, reactive systems typically take corrective actions, e.g., adjusting parameters in order to maintain the desired specifications of the system's state. Pro-active systems, on the other hand, may change the mode of interaction with the environment as well as the desired goals of the system. In this paper we describe our (ECA)² paradigm for reactive behavior with proactive impact and we present our ongoing work and vision for a system that is capable of context-aware adaptation, while ensuring the maintenance of a set of desired behavioral policies. Our main focus is on developing a formalism that provides tools for expressing normal, as well as defeasible and/or exceptional specification. However, at the same time, we insist on a sound semantics and the capability of answering hypothetical "what-if" queries. Towards this end, we introduce the high-level language that can be used to describe the dynamics of the problem domain, specify triggers under the (ECA)² paradigm, and reason about the consequences of the possible evolutions.

Original language	English (US)
Title of host publication	Active Conceptual Modeling of Learning - Next Generation Learning-Base System Development
Pages	30-46
Number of pages	17
DOIs	https://doi.org/10.1007/978-3-540-77503-4_4
State	Published - 2008
Event	1st International Active Conceptual Modeling of Learning Workshop - Tucson, AZ, United States Duration: Nov 8 2006 → Nov 8 2006

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4512 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	1st International Active Conceptual Modeling of Learning Workshop
Country/Territory	United States
City	Tucson, AZ
Period	11/8/06 → 11/8/06

Funding

★ Research supported by the Northrop Grumman Corp., contract: P.O. 8200082518. ★★ Research supported by the NSF grant, contract: IIS-0325144/003.

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-540-77503-4_4

Cite this

Trajcevski, G., & Scheuermann, P. (2008). Adaptive and context-aware reconciliation of reactive and pro-active behavior in evolving systems. In Active Conceptual Modeling of Learning - Next Generation Learning-Base System Development (pp. 30-46). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4512 LNCS). https://doi.org/10.1007/978-3-540-77503-4_4

Trajcevski, Goce ; Scheuermann, Peter. / Adaptive and context-aware reconciliation of reactive and pro-active behavior in evolving systems. Active Conceptual Modeling of Learning - Next Generation Learning-Base System Development. 2008. pp. 30-46 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Adaptive and context-aware reconciliation of reactive and pro-active behavior in evolving systems",

abstract = "One distinct characteristics of the context-aware systems is their ability to react and adapt to the evolution of the environment, which is often a result of changes in the values of various (possibly correlated) attributes. Based on these changes, reactive systems typically take corrective actions, e.g., adjusting parameters in order to maintain the desired specifications of the system's state. Pro-active systems, on the other hand, may change the mode of interaction with the environment as well as the desired goals of the system. In this paper we describe our (ECA)2 paradigm for reactive behavior with proactive impact and we present our ongoing work and vision for a system that is capable of context-aware adaptation, while ensuring the maintenance of a set of desired behavioral policies. Our main focus is on developing a formalism that provides tools for expressing normal, as well as defeasible and/or exceptional specification. However, at the same time, we insist on a sound semantics and the capability of answering hypothetical {"}what-if{"} queries. Towards this end, we introduce the high-level language that can be used to describe the dynamics of the problem domain, specify triggers under the (ECA)2 paradigm, and reason about the consequences of the possible evolutions.",

author = "Goce Trajcevski and Peter Scheuermann",

note = "Funding Information: ★ Research supported by the Northrop Grumman Corp., contract: P.O. 8200082518. ★★ Research supported by the NSF grant, contract: IIS-0325144/003.; 1st International Active Conceptual Modeling of Learning Workshop ; Conference date: 08-11-2006 Through 08-11-2006",

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doi = "10.1007/978-3-540-77503-4_4",

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Trajcevski, G & Scheuermann, P 2008, Adaptive and context-aware reconciliation of reactive and pro-active behavior in evolving systems. in Active Conceptual Modeling of Learning - Next Generation Learning-Base System Development. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4512 LNCS, pp. 30-46, 1st International Active Conceptual Modeling of Learning Workshop, Tucson, AZ, United States, 11/8/06. https://doi.org/10.1007/978-3-540-77503-4_4

Adaptive and context-aware reconciliation of reactive and pro-active behavior in evolving systems. / Trajcevski, Goce; Scheuermann, Peter.
Active Conceptual Modeling of Learning - Next Generation Learning-Base System Development. 2008. p. 30-46 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4512 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

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AU - Trajcevski, Goce

AU - Scheuermann, Peter

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AB - One distinct characteristics of the context-aware systems is their ability to react and adapt to the evolution of the environment, which is often a result of changes in the values of various (possibly correlated) attributes. Based on these changes, reactive systems typically take corrective actions, e.g., adjusting parameters in order to maintain the desired specifications of the system's state. Pro-active systems, on the other hand, may change the mode of interaction with the environment as well as the desired goals of the system. In this paper we describe our (ECA)2 paradigm for reactive behavior with proactive impact and we present our ongoing work and vision for a system that is capable of context-aware adaptation, while ensuring the maintenance of a set of desired behavioral policies. Our main focus is on developing a formalism that provides tools for expressing normal, as well as defeasible and/or exceptional specification. However, at the same time, we insist on a sound semantics and the capability of answering hypothetical "what-if" queries. Towards this end, we introduce the high-level language that can be used to describe the dynamics of the problem domain, specify triggers under the (ECA)2 paradigm, and reason about the consequences of the possible evolutions.

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DO - 10.1007/978-3-540-77503-4_4

M3 - Conference contribution

AN - SCOPUS:49949084313

SN - 3540775021

SN - 9783540775027

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 30

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Y2 - 8 November 2006 through 8 November 2006

ER -

Trajcevski G, Scheuermann P. Adaptive and context-aware reconciliation of reactive and pro-active behavior in evolving systems. In Active Conceptual Modeling of Learning - Next Generation Learning-Base System Development. 2008. p. 30-46. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-77503-4_4

Adaptive and context-aware reconciliation of reactive and pro-active behavior in evolving systems

Abstract

Publication series

Other

Funding

ASJC Scopus subject areas

Access to Document

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