Probabilistic methods for uncertain reasoning Artificial intelligence

many problems in ai (in reasoning, planning, learning, perception , robotics) require agent operate incomplete or uncertain information. ai researchers have devised number of powerful tools solve these problems using methods probability theory , economics.

bayesian networks general tool can used large number of problems: reasoning (using bayesian inference algorithm), learning (using expectation-maximization algorithm), planning (using decision networks) , perception (using dynamic bayesian networks). probabilistic algorithms can used filtering, prediction, smoothing , finding explanations streams of data, helping perception systems analyze processes occur on time (e.g., hidden markov models or kalman filters).

a key concept science of economics utility : measure of how valuable intelligent agent. precise mathematical tools have been developed analyze how agent can make choices , plan, using decision theory, decision analysis, , information value theory. these tools include models such markov decision processes, dynamic decision networks, game theory , mechanism design.