Given a decision tree and two alternatives Ai and Aj, Ai is at least as good as Aj iff E(Ai)?– E(Aj)?>?0, where E(Ai) is the expected value of Ai, for all consistent variable assignments for the probabilities and values. Ai is better than Aj iff Ai is at least as good as Aj and E(Ai)?–?E(Aj)?>?0 for some consistent variable assignments for the probabilities and values. Ai is admissible iff no other Aj is better.
Published in Chapter:
Decision Making in Intelligent Agents
Mats Danielson (Stockholm University, Sweden & Royal Institute of Technology, Sweden) and Love Ekenberg (Stockholm University, Sweden & Royal Institute of Technology, Sweden)
Copyright: © 2009
|Pages: 6
DOI: 10.4018/978-1-59904-849-9.ch066
Abstract
There are several ways of building complex distributed software systems, for example in the form of software agents. But regardless of the form, there are some common problems having to do with specification contra execution. One of the problems is the inherent dynamics in the environment many systems are exposed to. The properties of the environment are not known with any precision at the time of construction. This renders a specification of the system incomplete by definition. A traditional software agent is only prepared to handle situations conceived of and implemented at compile-time. Even though it can operate in varying contexts, its decision making abilities are static. One remedy is to prepare the distributed components for a truly dynamic environment, i.e. an environment with changing and somewhat unpredictable conditions. A rational software agent needs both a representation of a decision problem at hand and means for evaluation. AI has traditionally addressed some parts of this problem such as representation and reasoning, but has hitherto to a lesser degree addressed the decision making abilities of independent distributed software components (Ekenberg, 2000a, 2000b). Such decision making often has to be carried out under severe uncertainty regarding several parameters. Thus, methods for independent decision making components should be able to handle uncertainties on the probabilities and utilities involved. They have mostly been studied as means of representation, but are now being developed into functional theories of decision making suitable for dynamic use by software agents and other dynamic distributed components. Such a functional theory will also benefit analytical decision support systems intended to aid humans in their decision making. Thus, the generic term agent below stands for a dynamic software component as well as a human or a group of humans assisted by intelligent software.