Graded BDI Models for Agent Architectures

Casali, Ana; Godo, Lluı́s; Sierra, Carles

doi:10.1007/11533092_8

Cited by 39 publications

(41 citation statements)

References 17 publications

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“…For each goal G, let P = {P 1 , · · · , P m } be the set of plans to achieve G. 2 For obtaining a probability distribution from a knowledge base, see [11].…”

Section: A Semanticsmentioning

confidence: 99%

“…While these works studies on plan selection or intention revision, they seldom consider uncertainty that can emerge in agent's beliefs (see [13], [9], [12]). However, in multiagent systems, especially in BDI systems, uncertainty has been discussed in many research articles (e.g., [14], [17], [8], [2]) in relation to both incomplete information about an environment and an agent's uncertain beliefs about some observations. As a consequence, it is reasonable to expect these two trends be combined together, i.e., to study plan selection strategies in uncertainty-enabled BDI systems.…”

Section: Introduction Since Bratman Proposed His Planning Theory Omentioning

confidence: 99%

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Plan Selection for Probabilistic BDI Agents

Liu

Hong

et al. 2014

2014 IEEE 26th International Conference on Tools With Artificial Intelligence

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Abstract-When an agent wants to fulfill its desires about the world, the agent usually has multiple plans to choose from and these plans have different pre-conditions and additional effects in addition to achieving its goals. Therefore, for further reasoning and interaction with the world, a plan selection strategy (usually based on plan cost estimation) is mandatory for an autonomous agent. This demand becomes even more critical when uncertainty on the observation of the world is taken into account, since in this case, we consider not only the costs of different plans, but also their chances of success estimated according to the agent's beliefs. In addition, when multiple goals are considered together, different plans achieving the goals can be conflicting on their preconditions (contexts) or the required resources. Hence a plan selection strategy should be able to choose a subset of plans that fulfills the maximum number of goals while maintaining context consistency and resource-tolerance among the chosen plans. To address the above two issues, in this paper we first propose several principles that a plan selection strategy should satisfy, and then we present selection strategies that stem from the principles, depending on whether a plan cost is taken into account. In addition, we also show that our selection strategy can partially recover intention revision.

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“…For each goal G, let P = {P 1 , · · · , P m } be the set of plans to achieve G. 2 For obtaining a probability distribution from a knowledge base, see [11].…”

Section: A Semanticsmentioning

confidence: 99%

Section: Introduction Since Bratman Proposed His Planning Theory Omentioning

confidence: 99%

Plan Selection for Probabilistic BDI Agents

Liu

Hong

et al. 2014

2014 IEEE 26th International Conference on Tools With Artificial Intelligence

Self Cite

View full text Add to dashboard Cite

show abstract

“…The edges are associated with a number called the strength of the connection which gives an estimate of how coherent the two elements are 3 . The strength value of an edge (ϕ, γ), noted σ(ϕ, γ), respects the strength values that it has with other connected edges.…”

Section: Coherence Graphmentioning

confidence: 99%

A Coherence Based Framework for Institutional Agents

Joseph

Sierra

Schorlemmer

Coordination, Organizations, Institutions, and Norms in Agent Systems III

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Abstract. We introduce in this paper an agent model based on coherence theory. We give a formalization of Thagard's theory on coherence and use it to explain the reasoning process of an intentional agent that permits the agent to drop beliefs or to violate norms in order to keep a maximal state of coherence. The architecture is illustrated in the paper and a discussion on the possible use of this approach in the design of institutional agents is presented.

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“…Along this research line we are concerned with developing a general model for Graded BDI Agents, specifying an architecture able to deal with the environment uncertainty and with graded mental attitudes, see [2,3] for first results. In these works, belief degrees represent to what extent the agent believes a formula is true.…”

Section: Introductionmentioning

confidence: 99%

“…Between their potential applications, the tourist domain seems to be a good candidate as the offers of tourism products are in constant growth. In this paper, using the graded BDI agent model presented in [2] and extended in [3], we propose an specific architecture for a Travel Assistant Agent, a recommender agent that helps a tourist to choose holiday packages in Argentina. For this purpose, we present the necessary theories for the different contexts and some of the bridge rules.…”

Section: Introductionmentioning

confidence: 99%