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CAN(PLAN)+: Extending the Operational Semantics of the BDI Architecture to deal with Uncertain Information
"... The BDI architecture, where agents are modelled based on their beliefs, desires and intentions, pro-vides a practical approach to develop large scale systems. However, it is not well suited to model complex Supervisory Control And Data Acquisi-tion (SCADA) systems pervaded by uncertainty. In this pa ..."
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The BDI architecture, where agents are modelled based on their beliefs, desires and intentions, pro-vides a practical approach to develop large scale systems. However, it is not well suited to model complex Supervisory Control And Data Acquisi-tion (SCADA) systems pervaded by uncertainty. In this paper we address this issue by extending the operational semantics of CAN(PLAN) into CAN(PLAN)+. We start by modelling the beliefs of an agent as a set of epistemic states where each state, possibly using a different representation, models part of the agent’s beliefs. These epis-temic states are stratified to make them commen-surable and to reason about the uncertain beliefs of the agent. The syntax and semantics of a BDI agent are extended accordingly and we identify fragments with computationally efficient seman-tics. Finally, we examine how primitive actions are affected by uncertainty and we define an ap-propriate form of lookahead planning.
Probabilistic Planning in AgentSpeak using the POMDP framework
"... Abstract. AgentSpeak is a logic-based programming language, based on the Belief-Desire-Intention paradigm, suitable for building complex agent-based systems. To limit the computational complexity, agents in AgentSpeak rely on a plan library to reduce the planning problem to the much simpler problem ..."
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Abstract. AgentSpeak is a logic-based programming language, based on the Belief-Desire-Intention paradigm, suitable for building complex agent-based systems. To limit the computational complexity, agents in AgentSpeak rely on a plan library to reduce the planning problem to the much simpler problem of plan selection. However, such a plan library is often inadequate when an agent is situated in an uncertain environment. In this work, we propose the AgentSpeak+ framework, which extends AgentSpeak with a mechanism for probabilistic planning. The beliefs of an AgentSpeak+ agent are represented using epistemic states to allow an agent to reason about its uncertain observations and the uncertain effects of its actions. Each epistemic state consists of a POMDP, used to encode the agent’s knowledge of the environment, and its associated probability distribution (or belief state). In addition, the POMDP is used to select the optimal actions for achieving a given goal, even when faced with uncertainty. 1
Iterative Online Planning in Multiagent Settings with Limited Model Spaces and PAC Guarantees
"... Methods for planning in multiagent settings often model other agents ’ possible behaviors. However, the space of these models – whether these are policy trees, finite-state controllers or inten-tional models – is very large and thus arbitrarily bounded. This may exclude the true model or the optimal ..."
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Methods for planning in multiagent settings often model other agents ’ possible behaviors. However, the space of these models – whether these are policy trees, finite-state controllers or inten-tional models – is very large and thus arbitrarily bounded. This may exclude the true model or the optimal model. In this paper, we present a novel iterative algorithm for online planning that consid-ers a limited model space, updates it dynamically using data from interactions, and provides a provable and probabilistic bound on the approximation error. We ground this approach in the context of graphical models for planning in partially observable multiagent settings – interactive dynamic influence diagrams. We empirically demonstrate that the limited model space facilitates fast solutions and that the true model often enters the limited model space.
AgentSpeak+: AgentSpeak with Probabilistic Planning
"... Abstract—AgentSpeak is a logic-based programming lan-guage, based on the Belief-Desire-Intention (BDI) paradigm, suitable for building complex agent-based systems. To limit the computational complexity, agents in AgentSpeak rely on a plan library to reduce the planning problem to the much simpler pr ..."
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Abstract—AgentSpeak is a logic-based programming lan-guage, based on the Belief-Desire-Intention (BDI) paradigm, suitable for building complex agent-based systems. To limit the computational complexity, agents in AgentSpeak rely on a plan library to reduce the planning problem to the much simpler problem of plan selection. However, such a plan library is often inadequate when an agent is situated in an uncertain environ-ment. In this paper, we propose the AgentSpeak+ framework, which extends AgentSpeak with a mechanism for probabilistic planning. The beliefs of an AgentSpeak+ agent are represented using epistemic states to allow an agent to reason about its uncertain observations and the uncertain effects of its actions. Each epistemic state consists of a POMDP, used to encode the agent’s knowledge of the environment, and its associated probability distribution (or belief state). In addition, the POMDP is used to select the optimal actions for achieving a given goal, even when facing uncertainty. I.
1Adaptive uncertain information fusion to enhance plan selection in BDI agent systems
"... Abstract—Correctly modelling and reasoning with uncertain information from heterogeneous sources in large-scale systems is critical when the reliability is unknown and we still want to derive adequate conclusions. To this end, context-dependent merging strategies have been proposed in the literature ..."
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Abstract—Correctly modelling and reasoning with uncertain information from heterogeneous sources in large-scale systems is critical when the reliability is unknown and we still want to derive adequate conclusions. To this end, context-dependent merging strategies have been proposed in the literature. In this paper we investigate how one such context-dependent merging strategy (originally defined for possibility theory), called largely partially maximal consistent subsets (LPMCS), can be adapted to Dempster-Shafer (DS) theory. We identify those measures for the degree of uncertainty and internal conflict that are available in DS theory and show how they can be used for guiding LPMCS merging. A simplified real-world power distribution scenario illustrates our framework. We also briefly discuss how our approach can be incorporated into a multi-agent programming language, thus leading to better plan selection and decision making. Keywords-Dempster-Shafer theory; information fusion; context-dependent merging; BDI plan selection.
Fast Solving of Influence Diagrams for Multiagent Planning on GPU-enabled Architectures
"... Abstract: Planning under uncertainty in multiagent settings is highly intractable because of history and plan space com-plexities. Probabilistic graphical models exploit the structure of the problem domain to mitigate the com-putational burden. In this paper, we introduce the first parallelization o ..."
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Abstract: Planning under uncertainty in multiagent settings is highly intractable because of history and plan space com-plexities. Probabilistic graphical models exploit the structure of the problem domain to mitigate the com-putational burden. In this paper, we introduce the first parallelization of planning in multiagent settings on a CPU-GPU heterogeneous system. In particular, we focus on the algorithm for exactly solving interactive dynamic influence diagrams, which is a recognized graphical models for multiagent planning. Beyond paral-lelizing the standard Bayesian inference, the computation of decisions ’ expected utilities are parallelized. The GPU-based approach provides significant speedup on two benchmark problems. 1
