26,418 research outputs found
Inference in distributed multiagent reasoning systems in cooperation with artificial neural networks
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
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
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
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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