Do the Right Thing: Studies in Limited RationalityLike Mooki, the hero of Spike Lee's film Do the Right Thing artificially, intelligent systems have a hard time knowing what to do in all circumstances. Classical theories of perfect rationality prescribe the right thing for any occasion, but no finite agent can compute their prescriptions fast enough. In Do the Right Thing, the authors argue that a new theoretical foundation for artificial intelligence can be constructed in which rationality is a property of programs within a finite architecture, and their behaviour over time in the task environment, rather than a property of individual decisions. |
Contents
1 | 14 |
2 | 31 |
Rational Metareasoning | 60 |
4 | 87 |
Application to ProblemSolving Search | 119 |
5 | 120 |
32 | 130 |
7 | 131 |
14 | 149 |
Learning the Value of Computation | 159 |
Towards Limited Rational Agents | 171 |
1 | 172 |
3 | 179 |
| 187 | |
34 | 189 |
| 195 | |
Other editions - View all
Do the Right Thing: Studies in Limited Rationality Stuart J. Russell,Eric H. Wefald No preview available - 2003 |
Common terms and phrases
admissible heuristic agent alpha-beta anytime algorithms approach approximate Artificial Intelligence assume assumption backed-up estimate behaviour best-first search best+10 bounded optimality branching factor chapter chess compilation complex computation steps computational actions condition-action rules contract algorithm cost estimate current best move cycle decision procedure decision-theoretic defined deliberation depth limit domain environment equation error evaluation function example execution architectures expanded expected net value expected utility expected value external action Figure game tree game-playing given goal hardest-first heuristic estimate heuristic function implemented knowledge Korf LDTA leaf node learning Lemma limited rationality lower bound manhattan distance metalevel metareasoning MGSS MGSS2 minimax Morgan Kaufmann node expansions number of nodes obtained outcome parameter possible probability distribution problem propagation function pruning random reasoning search algorithm search depth search graph search tree second-best move selection sequence solve SRTA successors tion update rule utility estimates value of computation
Popular passages
Page 200 - Crimson, 1990 Representing and Reasoning With Probabilistic Knowledge: A Logical Approach to Probabilities, Fahiem Bacchus, 1 990 3D Model Recognition from Stereoscopic Cues, edited by John EW Mayhew and John P.
