A Theoretical Comparison of Models

  • Gerardo I. Simari
  • Simon D. Parsons
Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)


We have seen in the previous chapter that Markov Decision Processes can be consid- ered an “ideal” approach to the implementation of intelligent agents. Even though assigning utilities to states and probabilities to transitions between states might be regarded as a questionable way to solve the problem of preference, there are many situations in which this is acceptable. Once we have accepted that the problem is cor- rectly formulated in terms of the probabilities of actions having particular effects, and certain states having higher rewards than others, the MDP solution algorithms yield MEU-optimal policies. By this we mean mappings of states into actions that tell the agent what to do in each state, based on the probable outcomes of every possible action.


State Space Action Space Markov Decision Process Belief State Reward Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of OxfordOxfordUK
  2. 2.Department of Computer and Information Science Brooklyn CollegeCity University of New YorkNew YorkUSA

Personalised recommendations