Chapter 7 7.1 The agent maintains a knowledge base, KB, which may initially contain some background knowledge. Then we can expect it to cross the Golden Gate Bridge because it knows that that will achieve its goal. Notice that this analysis is independent of how the taxi works at the implementation level. This is called the declarative approach to system building. In contrast, the procedural approach encodes desired behaviors directly as program code. 7.2 The wumpus world is a cave consisting of rooms connected by passageways. Lurking somewhere in the cave is the terrible wumpus, a beast that eats anyone who enters its room. Chapter 8 Propositional logic has a third property that is desirable in representation languages, namely, compositionality. For nondeductive tasks such as learning from experience, outcomes are necessarily dependent on the form of the representations used. The language of first-order logic, whose syntax and semantics we define ...
Chapter 14 Agents almost never have access to the whole truth about their environment. The right thing to do, the rational decision, therefore, depends on both the relative importance of various goals and the likelihood that, and degree to which, they will be achieved. Probability provides a way of summarizing the uncertainty that comes from our laziness and ignorance. Probability theory makes the same ontological commitment as logic, namely, that facts either do or do not hold in the world. Degree of truth, as opposed to degree of belief, is the subject of fuzzy logic. Prior or unconditional probability; after the evidence is obtained, we talk about posterior or conditional probability. An agent is rational if and only if it chooses the action that yields the highest expected utility, averaged over all the possible outcomes of the action If Agent 1 expresses a set of degrees of belief that violate the axioms of probability theory then there is a betting strategy for Agent 2...
Chapter 20 This kind of feedback is called a reward, or reinforcement. In games like chess, the reinforcement is received only at the end of the game. We call this a terminal state in the state history sequence. The agent can be a passive learner or an active learner. A passive learner simply watches the world going by, and tries to learn the utility of being in various states; an active learner must also act using the learned information, and can use its problem generator to suggest explorations of unknown portions of the environment. The agent learns an action-value function giving the expected utility of taking a given action in a given state. This is called Q-learning. We define the reward-to-go of a state as the sum of the rewards from that state until a terminal state is reached. Given this definition, it is easy to see that the expected utility of a state is the expected reward-to-go of that state. A simple method for updating uti...
Comments
Post a Comment