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

Efficient Probabilistic Inference Algorithms for Cooperative Multiagent Systems

Probabilistic reasoning methods, Bayesian networks (BNs) in particular, have emerged as an effective and central tool for reasoning under uncertainty. In a multi-agent environment, agents equipped with local knowledge often need to collaborate and reason about a larger uncertainty domain. Multiply sectioned Bayesian networks (MSBNs) provide a solution for the probabilistic reasoning of cooperative agents in such a setting. In this thesis, we first aim to improve the efficiency of current MSBN exact inference algorithms. We show that by exploiting the calculation schema and the semantic meaning of inter-agent messages, we can significantly reduce an agent\u27s local computational cost as well as the inter-agent communication overhead. Our novel technical contributions include 1) a new message passing architecture based on an MSBN linked junction tree forest (LJF); 2) a suite of algorithms extended from our work in BNs to provide the semantic analysis of inter-agent messages; 3) a fast marginal calibration algorithm, designed for an LJF that guarantees exact results with a minimum local and global cost. We then investigate how to incorporate approximation techniques in the MSBN framework. We present a novel local adaptive importance sampler (LLAIS) designed to apply localized stochastic sampling while maintaining the LJF structure. The LLAIS sampler provides accurate estimations for local posterior beliefs and promotes efficient calculation of inter-agent messages. We also address the problem of online monitoring for cooperative agents. As the MSBN model is restricted to static domains, we introduce an MA-DBN model based on a combination of the MSBN and dynamic Bayesian network (DBN) models. We show that effective multi-agent online monitoring with bounded error is possible in an MA-DBN through a new secondary inference structure and a factorized representation of forward messages

Assessments of the Indian Mackerel (Rastrelliger kanagurta) and the Hilsa shad (Tenualosa ilisha) fisheries in the BOBLME countries

Assessment of the Indian Mackerel (Rastrelliger kanagurta) and the Hilsa shad (Tenualosa ilisha) fisheries in BOBLME countries.Each country was benchmarked against three principles; status of stocks, impact of fisheries on the environment and management frameworks in place. A wide range of indicators was used with a simple color-coded scoring system allowing easy identification of both strengths and weaknesses in those three areas. Individual country assessments are also included