# Informatics 2D Reasoning and Agents Semester 2,

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1 Informatics 2D Reasoning and Agents Semester 2, Alex Lascarides Lecture 29 Decision Making Under Uncertainty 24th March 2016 Informatics UoE Informatics 2D 1

2 Where are we? Last time... Looked at Dynamic Bayesian Networks General, powerful method for describing temporal probabilistic problems Unfortunately exact inference computationally too hard Methods for approximate inference (particle filtering) Today... Decision Making under Uncertainty Informatics UoE Informatics 2D 197

3 Combining beliefs and desires Rational agents do things that are an optimal tradeoff between: the likelihood of reaching a particular resultant state (given one s actions) and The desirability of that state So far we have done the likelihood bit: we know how to evaluate the probability of being in a particular state at a particular time. But we ve not looked at an agent s preferences or desires Now we will discuss utility theory in more detail to obtain a full picture of decision-theoretic agent design Informatics UoE Informatics 2D 198

4 Agent s preferences between world states are described using a utility function UF assigns some numerical value U(S) to each state S to express its desirability for the agent Nondeterministic action a has results Result(a) and probabilities P(Result(a) = s a,e) summarise agent s knowledge about its effects given evidence observations e. Can be combined with probabilities for outcomes to obtain expected utility of action: EU(A E) = s P(Result(a) = s a,e)u(s ) Informatics UoE Informatics 2D 199

5 Principle of maximum expected utility (MEU) says agent should use action that maximises expected utility In a sense, this summarises the whole endeavour of AI: If agent maximises utility function that correctly reflects the performance measure applied to it, then optimal performance will be achieved by averaging over all environments in which agent could be placed Of course, this doesn t tell us how to define utility function or how to determine probabilities for any sequence of actions in a complex environment For now we will only look at one-shot decisions, not sequential decisions (next lecture) Informatics UoE Informatics 2D 200

6 MEU sounds reasonable, but why should this be the best quantity to maximise? Why are numerical utilities sensible? Why single number? Questions can be answered by looking at constraints on preferences Notation: A B A B A B A is preferred to B the agent is indifferent between A and B the agent prefers A to B or is indifferent between them But what are A and B? Introduce lotteries with outcomes C 1...C n and accompanying probabilities L = [p 1, C 1 ; p 2, C 2 ;...;p n, C n ] Informatics UoE Informatics 2D 201

7 Outcome of a lottery can be state or another lottery Can be used to understand how preferences between complex lotteries are defined in terms of preferences among their (outcome) states The following are considered reasonable axioms of utility theory Orderability: (A B) (B A) (A B) Transitivity: If agent prefers A over B and B over C then he must prefer A over C: (A B) (B C) (A C) Example: Assume A B C A and A, B, C are goods Agent might trade A and some money for C if he has A We then offer B for C and some cash and then trade A for B Agent would lose all his money over time Informatics UoE Informatics 2D 202

8 Continuity: If B is between A and C in preference, then with some probability agent will be indifferent between getting B for sure and a lottery over A and C A B C p [p,a;1 p,c] B Substitutability: Indifference between lotteries leads to indifference between complex lotteries built from them A B [p,a;1 p,c] [p,b;1 p,c] Monotonicity: Preferring A to B implies preference for any lottery that assigns higher probability to A A B (p q [p,a;1 p,b] [q,a;1 q,b] Informatics UoE Informatics 2D 203

9 Decomposability example Decomposability: Compound lotteries can be reduced to simpler one [p,a;1 p,[q,b;1 q,c]] [p,a;(1 p)q,b;(1 p)(1 q),c] p A q B (1 p) is equivalent to (1 q) A C p B (1 p)(1 q) C Informatics UoE Informatics 2D 204

10 From preferences to utility The following axioms of utility ensure that utility functions follow the above axioms on preference: Utility principle: there exists a function such that U(A) > U(B) A B U(A) = U(B) A B MEU principle: utility of lottery is sum of probability of outcomes times their utilities U([p 1,S 1 ;...;p n,s n ]) = i p i U(S i ) But an agent might not know even his own utilities! But you can work out his (or even your own!) utilities by observing his (your) behaviour and assuming that he (you) chooses to MEU. Informatics UoE Informatics 2D 205

11 According to the above axioms, arbitrary preferences can be expressed by utility functions I prefer to have a prime number of in my bank account; when I have 10 I will give away 3. But usually preferences are more systematic, a typical example being money (roughly, we like to maximise our money) Agents exhibit monotonic preference toward money, but how about lotteries involving money? Who wants to be a millionaire -type problem, is pocketing a smaller amount irrational? Expected monetary value (EMV) is actual expectation of outcome Informatics UoE Informatics 2D 206

12 Utility of money Assume you can keep 1 million or risk it with the prospect of getting three millions at the toss of a (fair) coin EMV of accepting gamble is , 000, 000 which is greater than 1, 000, 000 Use S n to denote state of possessing wealth n dollars, current wealth S k Expected utilities become: EU(Accept) = 1 2 U(S k) U(S k+3,000,000) EU(Decline) = U(Sk+1,000,000 ) But it all depends on utility values you assign to levels of monetary wealth (is first million more valuable than second?) Informatics UoE Informatics 2D 207

13 Utility of money (empirical study) It turns out that for most people this is usually concave (curve (a)), showing that going into debt is considered disastrous relative to small gains in money risk averse. U U o o o o o o o o o o o o 150,000 o 800,000 o \$ \$ o (a) But if you re already \$10M in debt, your utility curve is more like (b) risk seeking when desperate! (b) Informatics UoE Informatics 2D 208

14 Utility scales Axioms don t say anything about scales For example transformation of U(S) into U (S) = k 1 + k 2 U(S) (k 2 positive) doesn t affect behaviour In deterministic contexts behaviour is unchanged by any monotonic transformation (utility function is value function/ordinal function) One procedure for assessing utilities is to use normalised utility between best possible prize (u = 1) and worst possible catastrophe (u = 0) Ask agent to indicate preference between S and the standard lottery [p, u : (1 p), u ], adjust p until agent is indifferent between S and standard lottery, set U(S) = p Informatics UoE Informatics 2D 209

15 Representing problems with DNs Evaluating decision networks What we now need is a way of integrating utilities into our view of probabilistic reasoning (influence diagrams) combine BNs with additional node types for actions and utilities Illustrate with airport siting problem: Airport Site Air Traffic Deaths Litigation Noise U Construction Cost Informatics UoE Informatics 2D 210

16 Representing problems with DNs Evaluating decision networks Representing decision problems with DNs Chance nodes (ovals) represent random variables with CPTs, parents can be decision nodes Decision nodes represent decision-making points at which actions are available Utility nodes represent utility function connected to all nodes that affect utility directly Often nodes describing outcome states are omitted and expected utility associated with actions is expressed (rather than states) action-utility tables Informatics UoE Informatics 2D 211

17 Representing problems with DNs Evaluating decision networks Representing decision problems with DNs Simplified version with action-utility tables Less flexible but simpler (like pre-compiled version of general case) Airport Site Air Traffic Litigation U Construction Informatics UoE Informatics 2D 212

18 Representing problems with DNs Evaluating decision networks Evaluating decision networks Evaluation of a DN works by setting decision node to every possible value Algorithm : 1. Set evidence variables for current state 2. For each value of decision node: 2.1 Set decision node to that value 2.2 Calculate posterior probabilities for parents of utility node 2.3 Calculate resulting (expected) utility for action 3. Return action with highest (expected) utility Using any algorithm for BN inference, this yields a simple framework for building agents that make single-shot decisions Informatics UoE Informatics 2D 213

19 Foundations for rational decision making under uncertainty Utility theory and its axioms, utility functions Possible points of criticism? nicely blend with our BN framework Only looked at one-shot decisions so far Next time: Markov Decision Processes Informatics UoE Informatics 2D 214

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MASSACHUSETTS INSTITUTE OF TECHNOLOGY 6.436J/15.085J Fall 2008 Lecture 5 9/17/2008 RANDOM VARIABLES Contents 1. Random variables and measurable functions 2. Cumulative distribution functions 3. Discrete

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In many situations in life, we are presented with opportunities to evaluate probabilities of events occurring and make judgments and decisions from this information. In this paper, we will explore four