Though the exact definition of the boundary between intelligent and non-intelligent artifacts has been a subject of much debate, one aspect of intelligence that many would deem essential is deliberation: Rather than reacting "instinctively" to its environment, an intelligent system should also be capable of reasoning about it, reasoning about the effects of actions performed by itself and others, and creating and executing plans, that is, determining which actions to perform in order to achieve certain goals. True deliberation is a complex topic, requiring support from several different sub-fields of artificial intelligence. The work presented in this thesis spans two of these partially overlapping fields, beginning with reasoning about action and change and eventually moving over towards planning.
The qualification problem relates to the difficulties inherent in providing, for each action available to an agent, an exhaustive list of all qualifications to the action, that is, all the conditions that may prevent the action from being executed in the intended manner. The first contribution of this thesis is a framework for modeling qualifications in Temporal Action Logic (TAL).
As research on reasoning about action and change proceeds, increasingly complex and interconnected domains are modeled in increasingly greater detail. Unless the resulting models are structured consistently and coherently, they will be prohibitively difficult to maintain. The second contribution is a framework for structuring TAL domains using object-oriented concepts.
Finally, the second half of the thesis is dedicated to the task of planning. TLplan pioneered the idea of using domain-specific control knowledge in a temporal logic to constrain the search space of a forward-chaining planner. We develop a new planner called TALplanner, based on the same idea but with fundamental differences in the way the planner verifies that a plan satisfies control formulas. T ALplanner generates concurrent plans and can take resource constraints into account. The planner also applies several new automated domain analysis techniques to control formulas, further increasing performance by orders of magnitude for many problem domains.