Extrospection: agents reasoning about the environment

Acay, Daghan L.; Pasquier, Philippe; Sonenberg, Liz

doi:10.1049/cp:20070372

Cited by 9 publications

(8 citation statements)

References 5 publications

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“…More generally, artifacts constitute a suitable abstraction for modelling the interaction between agents and generic infrastructure components. Acay et al [1] introduce OWL-T as an ontology for enabling agents to discover and use artifacts placed in MAS environment-there called tools-with no need of any a priori knowledge about them.…”

Section: Artifacts and Mas Languages And Infrastructuresmentioning

confidence: 99%

Artifacts in the A&A meta-model for multi-agent systems

Omicini

Ricci

Viroli

2008

Auton Agent Multi-Agent Syst

271

136

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In this article we focus on the notion of artifact for agents in multi-agent systems (MAS) as a basis for a new meta-model promoting the modelling and engineering of agent societies and MAS environment as first-class entities. Its conceptual foundations lay upon theories and results coming from computational sciences as well as from organisational and cognitive sciences, psychology, computer supported cooperative work (CSCW), anthropology and ethology. In the resulting agents & artifacts (A&A) meta-model, agents are the (pro-)active entities in charge of the goals/tasks that altogether build up the whole MAS behaviour, whereas artifacts are the reactive entities providing the services and functions that make individual agents work together in a MAS, and that shape agent environment according to the MAS needs. After presenting the scientific background, we define the notions of artifact in the A&A meta-model, discuss how it affects the notion of intelligence in MAS, and show its application to a number of agent-related research fields.

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Section: Artifacts and Mas Languages And Infrastructuresmentioning

confidence: 99%

Artifacts in the A&A meta-model for multi-agent systems

Omicini

Ricci

Viroli

2008

Auton Agent Multi-Agent Syst

271

136

View full text Add to dashboard Cite

show abstract

“…Since AOE uses RDF 5 as the representation of observable environment it is easy to extends it with ontologies, which can be used to enable semantic observation capabilities (Kesäniemi et al, 2009). Acay et al coined the term extrospection in (Acay et al, 2007), which refers to a sort of cognitive situatedness in agent's ability to discover, select, use and reason about environmental resources based on their formal semantic descriptions. They argue that the combination of artifact and its usage manual, or cognitive artifact, would benefit the MAS development by making it possible for agents to complete their design at run-time through the process of discovery, use and reuse of components and allowing domain independent meta-level reasoning to be built into agents (Acay et al, 2009).…”

Section: Semantic Description Of Environmentmentioning

confidence: 99%

“…OWL-T (OWL Tool) is an ontology for describing infrastructural components (Acay et al, 2007). The approach taken in the information modeling is pragmatic as opposed to epistemic, meaning that instead of trying to classify and label thing and relations between them, the goal of OWL-T has been define the functional nature of an object independent of its physical properties.…”

Section: Owl-tmentioning

confidence: 99%

See 1 more Smart Citation

Agent-Environment Interaction in MAS - Introduction and Survey

Kesäniemi¹,

Terziyan²

2011

Multi-Agent Systems - Modeling, Interactions, Simulations and Case Studies

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“…Acay et al [15] present a tool ontology that maintains information about tools in the environment, and explores an agent being able to reason about tools. In their representation, a tool manual that is a repository for what a tool is used for and how it is used is maintained in the environment, separate from the agent.…”

Section: Related Workmentioning

confidence: 99%

Evolution of artifact capabilities

Mokom

Kobti

2011

2011 IEEE Congress of Evolutionary Computation (CEC)

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The subject of artifact or tool use is considered in many fields to be a vital area of research in the study of general human competence. Recently in artificial intelligence, formalizations of the mental attitudes of intentional agents have been extended to include agent capabilities with respect to artifacts or tools. We consider understanding how these individual capabilities are learned and how they evolve as important steps towards formally defining, representing and implementing complex group capabilities. In this paper, a theoretical model for artifact capability is extended to incorporate evolution and learning through exploratory methods. A representation of artifacts and the cognition of a rational agent that can learn artifact use are provided. Supervised learning is assumed and combined with historical knowledge and genetic algorithms to provide an implementation of a multi-agent simulation. The simulation is built to support an agent with the ability to learn an artifact capability through observations of its own behavior, as well as through observations of other agents in a social environment. Results obtained from the simple yet practical approach, show that learned use of artifacts outperforms random use and rational agents can learn artifact use more efficiently as a social species than on their own.

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Extrospection: agents reasoning about the environment

Cited by 9 publications

References 5 publications

Artifacts in the A&A meta-model for multi-agent systems

Artifacts in the A&A meta-model for multi-agent systems

Agent-Environment Interaction in MAS - Introduction and Survey

Evolution of artifact capabilities

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