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.; The thesis is organized into six chapters. The first chapter, Chapter 1, introduces Bayesian networks as a concise representation of probabilistic knowledge and demonstrates the idea of belief updating by concise message passing using Pearl's algorithm (lambda - pi algorithm). Chapter 2 describes stepwise how to compile a BN into a junction tree (JT) model and covers probabilistic inference by concise message passing under the single-agent paradigm. Also, algorithms for belief updating by passing potentials as messages in a JT are presented. Chapter 3 explains in details the framework for the distributed representation of probabilistic knowledge in a cooperative multi-agent reasoning system. It is shown that the MSBN restrictions follow directly from a set of very basic assumptions and required properties. It also presents a set of distributed algorithms used to compile an MSBN into a collection of related junction tree models, termed a linked junction forest (LJF) for effective multi-agent belief updating. A set of algorithms for performing effective, exact, and distributed inference by the agents in an MSBN organized as an LJF is covered in Chapter 4. In this Chapter, we propose an efficient method that can guarantee performing belief assignment over all d-sepnodes in a hypertree. The method aims to relax the constraint of Xiang's model [Xiang93], namely the assumption of JPDs to be consistent with agent's belief. The model proposed can start from an arbitrary agent and its associated belief and establish the consistency in a practical manner. Chapter 5 addresses the issue of slow inference in multi-agent the model and how to overcome this problem by providing some enhancement to speed it up. A new model is introduced here using concepts from artificial neural network (ANN). An overview of ANN is given too. Chapter 6 presents an efficient object-oriented Bayesia...