Описание
xiv Preface
The serious student will gain valuable skills at several levels ranging from
expertise in the specification and design of intelligent agents to skills for implementing, testing, and improving real software systems for several challenging
application domains. The thrill of participating in the emergence of a new science of intelligent agents is one of the attractions of this approach. The practical
skills of dealing with a world of ubiquitous, intelligent, embedded agents are
now in great demand in the marketplace.
The focus is on an intelligent agent acting in an environment. We start with
simple agents acting in simple, static environments and gradually increase the
power of the agents to cope with more challenging worlds. We explore nine
dimensions of complexity that allow us to introduce, gradually and with modularity, what makes building intelligent agents challenging. We have tried to
structure the book so that the reader can understand each of the dimensions
separately, and we make this concrete by repeatedly illustrating the ideas with
four different agent tasks: a delivery robot, a diagnostic assistant, a tutoring
system, and a trading agent.
The agent we want the student to envision is a hierarchically designed
agent that acts intelligently in a stochastic environment that it can only partially observe – one that reasons about individuals and the relationships among
them, has complex preferences, learns while acting, takes into account other
agents, and acts appropriately given its own computational limitations. Of
course, we can’t start with such an agent; it is still a research question to build
such agents. So we introduce the simplest agents and then show how to add
each of these complexities in a modular way.
We have made a number of design choices that distinguish this book from
competing books, including the earlier book by the same authors:
• We have tried to give a coherent framework in which to understand AI.
We have chosen not to present disconnected topics that do not fit together. For example, we do not present disconnected logical and probabilistic views of AI, but we have presented a multidimensional design
space in which the students can understand the big picture, in which
probabilistic and logical reasoning coexist.
• We decided that it is better to clearly explain the foundations on which
more sophisticated techniques can be built, rather than present these
more sophisticated techniques. This means that a larger gap exists between what is covered in this book and the frontier of science. It also
means that the student will have a better foundation to understand current and future research.
• One of the more difficult decisions we made was how to linearize the
design space. Our previous book (Poole, Mackworth, and Goebel, 1998)
presented a relational language early and built the foundations in terms
of this language. This approach made it difficult for the students to
appreciate work that was not relational, for example, in reinforcement
Детали
- Год издания
- 2011
- Format