|Norvig I 610
a) A goal-based agent has a binary distinction between good (goal) and bad (non-goal) states,
b) A decision-theoretic agent has a continuous measure of outcome quality.
Decision theory: basic principle: the maximization of expected utility (MEU).
Decision-making systems: (…) a formalism called a decision network (also known as an influence diagram) that extends Bayesian networks by incorporating actions and utilities.
Uncertainty: [When] we deal with nondeterministic partially observable environments (…) the agent may not know the current state, we omit it and define RESULT(a) as a random variable whose values are the possible outcome states.
Norvig I 611
The agent’s preferences are captured by a utility function, U(s), which assigns a single number to express the desirability of a state. The expected utility of an action given the evidence, EU(a|e), is just the average utility value of the outcomes, weighted by the probability that the outcome occurs (…). The principle of maximum expected utility (MEU) says that a rational agent should choose the action that maximizes the agent’s expected utility (…). >Utility/AI research, >Utility theory/Norvig, >Rationality/AI research, >Certainty effect/Kahneman/Tversky, >Ambiguity/Kahneman/Tversky._____________Explanation of symbols: Roman numerals indicate the source, arabic numerals indicate the page number. The corresponding books are indicated on the right hand side. ((s)…): Comment by the sender of the contribution. The note [Author1]Vs[Author2] or [Author]Vs[term] is an addition from the Dictionary of Arguments. If a German edition is specified, the page numbers refer to this edition.
Stuart J. Russell
Artificial Intelligence: A Modern Approach Upper Saddle River, NJ 2010