26,418 research outputs found

    Inference in distributed multiagent reasoning systems in cooperation with artificial neural networks

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    This research is motivated by the need to support inference in intelligent decision support systems offered by multi-agent, distributed intelligent systems involving uncertainty. Probabilistic reasoning with graphical models, known as Bayesian networks (BN) or belief networks, has become an active field of research and practice in artificial intelligence, operations research, and statistics in the last two decades. At present, a BN is used primarily as a stand-alone system. In case of a large problem scope, the large network slows down inference process and is difficult to review or revise. When the problem itself is distributed, domain knowledge and evidence has to be centralized and unified before a single BN can be created for the problem. Alternatively, separate BNs describing related subdomains or different aspects of the same domain may be created, but it is difficult to combine them for problem solving, even if the interdependency relations are available. This issue has been investigated in several works, including most notably Multiply Sectioned BNs (MSBNs) by Xiang [Xiang93]. MSBNs provide a highly modular and efficient framework for uncertain reasoning in multi-agent distributed systems. Inspired by the success of BNs under the centralized and single-agent paradigm, a MSBN representation formalism under the distributed and multi-agent paradigm has been developed. This framework allows the distributed representation of uncertain knowledge on a large and complex environment to be embedded in multiple cooperative agents and effective, exact, and distributed probabilistic inference. What a Bayesian network is, how inference can be done in a Bayesian network under the single-agent paradigm, how multiple agents’ diverse knowledge on a complex environment can be structured as a set of coherent probabilistic graphical models, how these models can be transformed into graphical structures that support message passing, and how message passing can be performed to accomplish tasks in model compilation and distributed inference are covered in details in this thesis

    Asymptotically idempotent aggregation operators for trust management in multi-agent systems

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    The study of trust management in multi-agent system, especially distributed, has grown over the last years. Trust is a complex subject that has no general consensus in literature, but has emerged the importance of reasoning about it computationally. Reputation systems takes into consideration the history of an entity’s actions/behavior in order to compute trust, collecting and aggregating ratings from members in a community. In this scenario the aggregation problem becomes fundamental, in particular depending on the environment. In this paper we describe a technique based on a class of asymptotically idempotent aggregation operators, suitable particulary for distributed anonymous environments

    Automated Verification of Quantum Protocols using MCMAS

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    We present a methodology for the automated verification of quantum protocols using MCMAS, a symbolic model checker for multi-agent systems The method is based on the logical framework developed by D'Hondt and Panangaden for investigating epistemic and temporal properties, built on the model for Distributed Measurement-based Quantum Computation (DMC), an extension of the Measurement Calculus to distributed quantum systems. We describe the translation map from DMC to interpreted systems, the typical formalism for reasoning about time and knowledge in multi-agent systems. Then, we introduce dmc2ispl, a compiler into the input language of the MCMAS model checker. We demonstrate the technique by verifying the Quantum Teleportation Protocol, and discuss the performance of the tool.Comment: In Proceedings QAPL 2012, arXiv:1207.055

    Cognitive visual tracking and camera control

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    Cognitive visual tracking is the process of observing and understanding the behaviour of a moving person. This paper presents an efficient solution to extract, in real-time, high-level information from an observed scene, and generate the most appropriate commands for a set of pan-tilt-zoom (PTZ) cameras in a surveillance scenario. Such a high-level feedback control loop, which is the main novelty of our work, will serve to reduce uncertainties in the observed scene and to maximize the amount of information extracted from it. It is implemented with a distributed camera system using SQL tables as virtual communication channels, and Situation Graph Trees for knowledge representation, inference and high-level camera control. A set of experiments in a surveillance scenario show the effectiveness of our approach and its potential for real applications of cognitive vision
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