This book explores planning and control by reformulating the two areas in a common control framework, developing the corresponding techniques side-by-side, and identifying opportunities for integrating their ideas and methods.
The text is organized around the central roles of prediction, observation and computation. The first three chapters deal with predictive models of physical systems based on temporal logic and the differential calculus. Chapter 4 and 5 present basic concepts in planning and control, including controllability, observability, stability, feedback control, task reduction, conditional plans, and the relationship between goals and preferences. Chapters 6 and 7 consider issues of uncertainty, covering state estimation and the Kalman filter, stochastic dynamic programming, probabilistic modeling, and graph-based decision models. The remaining chapters investigate selected topics in time-critical decision making, adaptive control, and hybrid control architectures. Throughout, the reader is led to consider critical tradeoffs involving the accuracy of prediction, the availability of information from observation, and the costs and benefits of computation in dynamic environments.
The book is useful to researchers in artificial intelligence and control theory, and others concerned with the design of complex applications in robotics, automated manufacturing, and time-critical decision support.
The text is published by Morgan Kaufmann.