text: In some problems, the agent's preferences may be uncertain, especially if there are other agents or 
  humans involved. These can be learned (e.g., with inverse reinforcement learning), or the agent can seek 
  information to improve its preferences.[37] Information value theory can be used to weigh the value of 
  exploratory or experimental actions.[38] The space of possible future actions and situations is typically 
  intractably large, so the agents must take actions and evaluate situations while being uncertain of what the 
  outcome will be. 
  
  A Markov decision process has a transition model that describes the probability that a particular action 
  will change the state in a particular way and a reward function that supplies the utility of each state and 
  the cost of each action. A policy associates a decision with each possible state. The policy could be 
  calculated (e.g., by iteration), be heuristic, or it can be learned.[39] 
  
  Game theory describes the rational behavior of multiple interacting agents and is used in AI programs that 
  make decisions that involve other agents.[40] 