In which we discuss what an intelligent agent does, how it is related to its environment, how it is evaluated, and how we might go about building one.
An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through effectors. A human agent has eyes, ears, and other organs for sensors, and hands, legs, mouth, and other body parts for effectors. A robotic agent substitutes cameras and infrared range ﬁnders for the sensors and various motors for the effectors. A software agent has encoded bit strings as its percepts and actions. A generic agent is diagrammed in Figure 2.1. Our aim in this book is to design agents that do a good job of acting on their environment. First, we will be a little more precise about what we mean by a good job. Then we will talk about different designs for successful agents—ﬁlling in the question mark in Figure 2.1. We discuss some of the general principles used in the design of agents throughout the book, chief among which is the principle that agents should know things. Finally, we show how to couple an agent to an environment and describe several kinds of environments.
2.2 HOW AGENTS SHOULD ACT
A rational agent is one that does the right thing. Obviously, this is better than doing the wrong thing, but what does it mean? As a ﬁrst approximation, we will say that the right action is the one that will cause the agent to be most successful. That leaves us with the problem of deciding how and when to evaluate the agent’s success. Artiﬁcial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig, c 1995 Prentice-Hall, Inc.
sensors percepts environment actions ? agent
Figure 2.1 Agents interact with environments through sensors and effectors.
We use the term performance measure for the how—the criteria that determine how successful an agent is. Obviously, there is not one ﬁxed measure suitable for all agents. We could ask the agent for a subjective opinion of how happy it is with its own performance, but some agents would be unable to answer, and others would delude themselves. (Human agents in particular are notorious for “sour grapes”—saying they did not really want something after they are unsuccessful at getting it.) Therefore, we will insist on an objective performance measure imposed by some authority. In other words, we as outside observers establish a standard of what it means to be successful in an environment and use it to measure the performance of agents. As an example, consider the case of an agent that is supposed to vacuum a dirty ﬂoor. A plausible performance measure would be the amount of dirt cleaned up in a single eight-hour shift. A more sophisticated performance measure would factor in the amount of electricity consumed and the amount of noise generated as well. A third performance measure might give highest marks to an agent that not only cleans the ﬂoor quietly and efﬁciently, but also ﬁnds time to go windsurﬁng at the weekend.1 The when of evaluating performance is also important. If we measured how much dirt the agent had cleaned up in the ﬁrst hour of the day, we would be rewarding those agents that start fast (even if they do little or no work later on), and punishing those that work consistently. Thus, we want to measure performance over the long run, be it an eight-hour shift or a lifetime. We need to be careful to distinguish between rationality and omniscience. An omniscient agent knows the actual outcome of its actions, and can act accordingly; but omniscience is impossible in reality. Consider the following example: I am walking along the Champs Elys´ es e one day and I see an old friend across the street. There is no trafﬁc nearby and I’m not otherwise engaged, so, being rational, I start to cross the street. Meanwhile, at...
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