"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 make this concrete by repeatedly illustrating the ideas with three different agent tasks: a delivery robot, a diagnostic assistant, and an information slave (the infobot). " - from the Preface.
"Our theory is based on logic. Logic has been developed over the centuries as a formal (that is, precise not obtuse) way of representing assumptions about a world and the process of deriving the consequences of those assumptions. For simple agents in simple worlds we start with a highly restricted simple logic. Then as our agent/environment requires, we increase the logical power of the formalism. Since a computer is simply a symbol-manipulation engine, we can easily map our formal theories into computer programs that can control an agent or be used to reason about an agent. Everything we describe is implemented that way." - from the Preface.
- Chapter One is also available online and that's where you'll find lots of useful information including an answer to the question "What Is Computational Intelligence?" and this summary of the common features of the aforementioned three agent tasks: "At one level of abstraction, they each have four tasks: Modeling the environment ... Evidential reasoning or perception ... Action ... Learning from past experience .... These tasks cut across all application domains."
"Computational intelligence is intimately linked with the discipline of computer science. While there are many non-computer scientists who are researching CI, much, if not most, CI (or AI) research is done within computer science departments. We believe this is appropriate, as the study of computation is central to CI. It is essential to understand algorithms, data structures, and combinatorial complexity in order to build intelligent machines. It is also surprising how much of computer science started as a spin off from AI, from timesharing to computer algebra systems." - Page 6.
- "In order to use knowledge and reason with it, you need what we call a representation and reasoning system (RRS). A representation and reasoning system is composed of a language to communicate with a computer, a way to assign meaning to the language, and procedures to compute answers given input in the language. Intuitively, an RRS lets you tell the computer something in a language where you have some meaning associated with the sentences in the language, you can ask the computer questions, and the computer will produce answers that you can interpret according to the meaning associated with the language. ... One simple example of a representation and reasoning system ... is a database system. In a database system, you can tell the computer facts about a domain and then ask queries to retrieve these facts. What makes a database system into a representation and reasoning system is the notion of semantics. Semantics allows us to debate the truth of information in a knowledge base and makes such information knowledge rather than just data." - Pages 9 - 10.
Design and Assumption-Based Reasoning. Slides for Lecture #1 covering Section 1 of Chapter 9 [Assumption-Based Reasoning]. As per slide one: "Often we want our agents to make assumptions rather than doing deduction from their knowledge. For example . . . In design you hypothesize components that provably fulfill some design goals and are feasible."
- And be sure to check out their AIspace -Tools for Learning Computational Intelligence: applets designed as tools for teaching or learning about AI.