This thesis examines the problem of an autonomous agent learning a causal world model of its environment. Previous approaches to learning causal world models have concentrated on environments that are too "easy" (deterministic finite state machines) or too "hard" (containing much hidden state). We describe a new domain --- environments with manifest causal structure --- for learning. In such environments the agent has an abundance of perceptions of its environment. Specifically, it perceives almost all the relevant information it needs to understand the environment. Many environments of interest have manifest causal structure and we show that an agent can learn the manifest aspects of these environments quickly using straightforward learning techniques. We present a new algorithm to learn a rule-based causal world model from observations in the environment. The learning algorithm includes (1) a low level rule-learning algorithm that converges on a good set of specific rules, (2) a concept learning algorithm that learns concepts by finding completely correlated perceptions, and (3) an algorithm that learns general rules. In addition this thesis examines the problem of finding a good expert from a sequence of experts. Each expert has an "error rate"; we wish to find an expert with a low error rate. However, each expert's error rate and the distribution of error rates are unknown. A new expert-finding algorithm is presented and an upper bound on the expected error rate of the expert is derived.

Thesis (Ph. D.)--University of Texas at Austin, 2003. / Supervisor: Kathleen S. Barber. Vita. Includes bibliographical references. Available also from UMI Company.

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It is important for designers, developers, and end-users to comprehend (or explain) why a software agent acts in a particular way when situated in its operating environment. Comprehending agent behaviors in an agent-based system is a challenging task due to environmental uncertainty and the dynamics and multitude of agent interactions, which must be captured, processed, and analyzed by the human user. While traditional software comprehension answers "what is happening in the implementation?", this research takes a step further to facilitate comprehension by answering "why is the behavior happening in the implementation?". To explain agent behaviors in the implemented system, this research takes the model-checking approach for representing abstracted software behavior and the reverse engineering approach for verifying the expected behavior model against the implementation's actual behavior, while assimilating the terminology and framework from abductive reasoning. This research empirically shows that maintaining accurate background knowledge of how the implementation is expected to behave is crucial in generating accurate explanations of agent behavior. The resulting Tracing Method and accompanying Tracer Tool build on ideas from existing approaches and extend the state-of-the-art to better assist human users (of various skill levels) in comprehending agent-based software by automating many reasoning tasks. The Tracing Method is applied to two domains to demonstrate the capabilities of the Tracer Tool in (1) suggesting background knowledge updates, (2) interpreting actual behaviors from implementation executions, and (3) explaining observed agent behaviors. This research aims to help designers who want to improve agent behavior; developers who need to debug and verify agent behavior; and end-users who want to comprehend agent behaviors. / text

This thesis presents the innovative design of intelligent agent architectures specifically focusing on their human-agent teaming ability for use in a simulation environment. Agent teaming has gained popularity in recent years. It is believed that three important aspects, communication, co-ordination and co-operation play important roles in agent teaming. Multi agent teaming takes inspiration from human organisational models of team operation, where role playing such as leadership and communicative, co-operative and collaborative skills empower the success of the team. Additionally, the second major step in agent teaming is the “human-centric” nature of the agent. The current trend of agent development is very much concentrated on its agent only interaction within teams. This thesis contributes to the understanding of human-agent teaming within current agent architectures such as Belief Desire Intention (BDI) architectures. / Thesis (PhDComputerSystemsEng)--University of South Australia, 2006.

Thesis (Ph. D.)--University of Washington, 1997. / Vita. Includes bibliographical references (p. [166]-173).

Thesis (Master)--İzmir Institute of Technology, İzmir, 2006. / Keywords: Agents, security protocols, software, software development, software security. Includes bibliographical references (leaves. 73-76).

Thesis (Ph. D.)--University of Texas at Austin, 2005. / Vita. Includes bibliographical references.

Thesis (Ph.D.)--University of Melbourne, Dept. of Information Systems, 2009. / Typescript. Includes bibliographical references.

Hao, Jianye. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 216-228). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.