Probabilistic verification of a decentralized policy for conflict resolution in multi-agent systems

Pallottino, Lucia; Scordio, V.G.; Bicchi, Antonio

doi:10.1109/robot.2006.1642069

Cited by 25 publications

(10 citation statements)

References 17 publications

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“…For verification the multi-agent system is transformed to finite state Markov chains for establishing quantitative temporal properties of the system. Similar to our evaluation of plan/intention selection strategies, the work of [42] provides a quantitative assessment for a decentralised control policies in multi-vehicle scenarios. Specifically they study conflict resolution policies to ensure that a policy never causes collisions under some mild assumptions on the initial conditions.…”

Section: Related Workmentioning

confidence: 99%

Probabilistic BDI Agents: Actions, Plans, and Intentions

Archibald

Calder

Sevegnani

et al. 2021

Lecture Notes in Computer Science

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The Belief-Desire-Intention (BDI) architecture is a popular framework for rational agents, yet most verification approaches are limited to analysing qualitative properties, for example whether an intention completes. BDI-based systems, however, operate in uncertain environments with dynamic behaviours: we may need quantitative analysis to establish properties such as the probability of eventually completing an intention. We define a probabilistic extension to the Conceptual Agent Notation (CAN) for BDI agents that supports probabilistic action outcomes, and probabilistic plan and intention selection. The semantics is executable via an encoding in Milner's bigraphs and the BigraphER tool. Quantitative analysis is conducted using PRISM. While the new semantics can be applied to any CAN program, we demonstrate the extension by comparing with standard plan and intention selection strategies (e.g. ordered or fixed schedules) and evaluating probabilistic action executions in a smart manufacturing scenario. The results show we can improve significantly the probability of intention completion, with appropriate probabilistic distribution. We also show the impact of probabilistic action outcomes can be marginal, even when the failure probabilities are large, due to the agent making smarter intention selection choices.

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Section: Related Workmentioning

confidence: 99%

Probabilistic BDI Agents: Actions, Plans, and Intentions

Archibald

Calder

Sevegnani

et al. 2021

Lecture Notes in Computer Science

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“…The component implementation is based on a decentralized collision avoidance protocol, called "generalized roundabout policy" (GRP), that has been recently proposed for mobile agents evolving on the plane [8], [9], [10]. The GRP is briefly described for the reader's convenience.…”

Section: A the Collision Avoidance Policymentioning

confidence: 99%

A component-based approach to localization and collision avoidance for mobile multi-agent systems

Alriksson

Nordh

Årzén

et al. 2007

2007 European Control Conference (ECC)

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Abstract-In the RUNES project a disaster relief tunnel scenario is being developed in which mobile robots are used to restore the radio network connectivity in a stationary sensor network. A component-based software development approach has been adopted. Two components are described in this paper. A localization component that uses ultrasound and dead reckoning to decide the robot positions and a collision avoidance component that ensures that the robots do not collide with each other.

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“…The path deformation method suggested in [7] is based on a partial a priori knowledge of the world and does not guarantee safety and convergence to the goal. [8] proposes a decentralized policy for conflict resolution in multi-agent systems with aircraft-like agents, but does not handle stationary obstacles.…”

Section: Introductionmentioning

confidence: 99%

Hybrid control of a constrained velocity unicycle with local sensing

Oikonomopoulos

Loizou

Kyriakopoulos

2008

2008 47th IEEE Conference on Decision and Control

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Abstract-This paper introduces a hybrid control scheme for steering a non-holonomic agent with limited sensing capabilities and input constraints through a stationary but unknown workspace, occupied by arbitrarily shaped obstacles. The considered constraints (i.e. constant linear and bounded angular velocities) apply to a wide category of vehicle systems. Limited sensing is realized by a radially bounded sensing device. The proposed hybrid controller respects the kinematic constraints while guaranteeing obstacle avoidance and convergence to a specified goal configuration. In addition to the analytical guarantees, we demonstrate the effectiveness of the proposed hybrid control scheme through non-trivial computer simulations.

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Probabilistic verification of a decentralized policy for conflict resolution in multi-agent systems

Cited by 25 publications

References 17 publications

Probabilistic BDI Agents: Actions, Plans, and Intentions

Probabilistic BDI Agents: Actions, Plans, and Intentions

A component-based approach to localization and collision avoidance for mobile multi-agent systems

Hybrid control of a constrained velocity unicycle with local sensing

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