1. Making Simple Decisions Copyright, 1996 © Dale Carnegie & Associates, Inc. Chapter 16

2. CS 471/598 by H. Liu2 Combining beliefs and desires We can make decision based on probabilistic reasoning (Belief Networks), but it does not include what an gent wants. An agent’s preferences between world states are captured by a utility function - it assigns a single number to express the desirability of a state. Utilities are combined with the outcome probabilities for actions to give an expected utility for each action.

3. CS 471/598 by H. Liu3 Expected utility EU(A|E) =  P(result i (A)|E,Do(A))U (result i (A)) Maximum expected utility - a rational agent should choose an action that maximizes the agent’s EU. Simple decisions are one-shot decisions.

4. CS 471/598 by H. Liu4 The basis of utility theory Why should maximizing the average utility be so special? Constraints on rational preferences are orderability, transitivity, continuity, substitutability, monotonicity, decomposability. The six constrts form the axioms of utility theory. The axioms of utility: Utility principle Maximum Expected Utility principle

5. CS 471/598 by H. Liu5 Utility functions Utility functions map states to real numbers. Utility theory has its roots in economics -> the utility of money Risk averse Risk seeking Certainty equivalent Risk neutral Utility scales and utility assessment Normalization

6. CS 471/598 by H. Liu6 Decision networks Types of nodes Chance nodes Decision nodes Utility nodes Action-utility tables (Eq. 16.1) Evaluating decision networks An algorithm (P 486)

7. CS 471/598 by H. Liu7 The value of information One of the most important parts of decision making is knowing what questions to ask. To conduct expensive and critical tests or not depends on two factors: Whether the different possible outcomes would make a significant difference to the optimal course of action The likelihood of the various outcomes Information value theory enables an agent to choose what information to acquire.

8. CS 471/598 by H. Liu8 Decision-theoretic expert systems The decision maker states preferences between outcomes. The decision analyst enumerates the possible actions and outcomes and elicits preferences from the decision maker to determine the best course of action. The addition of decision networks means that expert systems can be developed that recommend optimal decisions, reflecting the preferences of the user as well as the available evidence.

9. CS 471/598 by H. Liu9 Summary Probability theory describes what an agent should believe based on evidence Utility theory describes what an agent wants Decision theory puts the two together to describe what an agent should do A rational agent should select actions that maximize its expected utility. Decision networks provide a simple formalism for expressing and solving decision problems.