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published_or_final_version / Chinese / Master / Master of Philosophy

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This dissertation proposes a suite of novel approaches for solving multiagent decision and optimization problems based on the Collaborative Design Network (CDN), a framework for multiagent decision making. The framework itself is distributed, decision-theoretic and was originally proposed for multiagent component-centred design. This application is a novel use of the CDN, and demonstrate the generality of the CDN framework for general decision-theoretic planning. First, the framework is applied towards tackling a multiagent decision problem outside of collaborative design called multiagent expedition (MAE), a testbed problem which abstracts many of the features of real-world multiagent decision-making problems. We formally introduce MAE, and show it to be a subclass of a decentralized partially observable Markov Decision process (Dec-POMDP). We apply the CDN to the online MAE planning problem. We demonstrate that the CDN can plan in MAE with conditional optimality given a set of basic assumptions on the structure and organization of the agent team. We introduce a set of knowledge representational aspects to achieve conditionally optimal planning. We experimentally verify our approach on a series of benchmark problems created for this dissertation to test the various aspects of our CDN solution. We also investigate further methods for scalability and speedup in MAE. The concept of \emph{partial evaluation} (PE) is introduced, based on the assumption that an agent has an intended effect given an agent's action and considers all other effects unintended. This assumption is used to derive a bound for planning that partitions the set of joint plans into a set of fully evaluated and a set of partial evaluated plans. Plans which are partially evaluated can significantly speed up planning in the centralized case. PE is also applied to the CDN, to both public decisions between agents and private decisions local to an agent. We demonstrate that applying PE to public decisions in the CDN results in either intractable communication or suboptimal planning. When applied to private decisions, we show PE can still be very effective in decreasing planning runtime.

Despite the many clear advantages that ontology has to offer as a standardized knowledge representation language, many intelligent system developers still find it difficult to jump on the band wagon and represent all their application knowledge using ontology. This difficulty and hesitation stems primarily from the fact that, while most ontology languages provide native support for reasoning about the domain's structures, they do not provide adequate support for computational planning -- the kind of reasoning used by many intelligent systems to derive their purposeful behaviors. To overcome this challenge, a lot of work has been done to discover a practical way to seamlessly incorporate planning into ontology languages. As it has been well-established in the literature however, this is a very challenging task from both a theoretical and practical stand point, and many of the reported works in this direction either have had very limited success, or have been done in ad hoc and less reusable manners. In this thesis, we report our pursuit of a new approach to integrating planning into ontology-driven applications. This approach promises to overcome the difficulties faced by many of the existing approaches. In addition to producing a reusable and extensible framework for doing computational planning in ontology-driven applications, our pursuit also raises and answers some interesting ontology research questions that could have potential impacts on several application domains beyond the integration of planning and ontological modeling.

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