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Default reasoning : causal and conditional theories

Defaults in commonsense reasoning permit the generation of useful predictions in the absence of complete information. However, attempts to represent and reason with defaults in AI run into the problem of spurious arguments, arguments that rely on acceptable defaults but which support unacceptable conclusions. Geffner addresses this problem by analyzing the causal and conditional aspects of default, establishing theoretical limits on the capabilities of probabilistic approaches. He provides new insights into the nature of defaults, and new methods of processing databases containing default expressions. The result is a default-handling system that yields an intuitive behaviour in several domains of interest in AI, including inheritance hierarchies, reasoning about change, general logic programs, and abductive reasoning.

A concise introduction to models and methods for automated planning

Planning is the model-based approach to autonomous behavior where the agent behavior is derived automatically from a model of the actions, sensors, and goals. The main challenges in planning are computational as all models, whether featuring uncertainty and feedback or not, are intractable in the worst case when represented in compact form. In this book, we look at a variety of models used in AI planning, and at the methods that have been developed for solving them. The goal is to provide a modern and coherent view of planning that is precise, concise, and mostly self-contained, without being shallow. For this, we make no attempt at covering the whole variety of planning approaches, ideas, and applications, and focus on the essentials. The target audience of the book are students and researchers interested in autonomous behavior and planning from an AI, engineering, or cognitive science perspective.

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A concise introduction to models and methods for automated planning [electronic resource]

Planning is the model-based approach to autonomous behavior where the agent behavior is derived automatically from a model of the actions, sensors, and goals. The main challenges in planning are computational as all models, whether featuring uncertainty and feedback or not, are intractable in the worst case when represented in compact form. In this book, we look at a variety of models used in AI planning, and at the methods that have been developed for solving them. The goal is to provide a modern and coherent view of planning that is precise, concise, and mostly self-contained, without being shallow. For this, we make no attempt at covering the whole variety of planning approaches, ideas, and applications, and focus on the essentials. The target audience of the book are students and researchers interested in autonomous behavior and planning from an AI, engineering, or cognitive science perspective.

Online Synthesis Digital Library