Adaptive AI Engine for RTS Games

Discussing the theory and practice

Case-Based Planning – A Framework for planning from experience (Part II)

Posted by Ogail on December 1, 2009

  • Object critics are used in plan modification to reduce effort of modifying a plan. Where you can use some of the unrelated components in the partially matched plan.
  • When plan fails the explanation function has two tasks:
    • Identifies the parts of the plan to be altered.
    • Identifies places where planner’s knowledge is faulty
  • Plan failure means:
    • Change the steps that leads to the current failed plan
    • Change your understanding to the world (as planner)
  • Explanation of Failure Contains:
    • Why the failure has occurred?
    • Definition of the state of the failure.
    • Step that resulted in the failure.
    • Conditions that has to be true for failure to come out.
    • What was the planner trying to do when the failure occurred?
    • Description of steps that achieves the plan that was being applied goals.
  • Explanation example is page 25 is very useful!
  • Thematic Organization Packets (TOP) are set of strategies used to resolve the failure in the plan.
  • Usually the planning failure occurs from the casual interaction between goals,
  • After detecting a failure try to generalize it as possible (as if you discover a problem in beef generalize it to meat).
  • Demons are features that cause the failure to arise.
  • In failure anticipation, when the planner predicts a problem that will arise it adds a goal that will avoid it.
  • Plan is a series of steps and a list of ingredients that has been built to satisfy some particular set of goals.    
  • CBP need not to generalize plans it stores, it does need to generalize the features that are used to index them.
  • It’s easier to alter a plan to satisfy a new goal than to make the changes required to deal with an interaction between goals (in other words problems to avoid)
  • CHEF was using semantic network as data structure that holds the plans
  • A CBP can use its own memories of past failure to anticipate new plans by associating failures with the surface features that predict them.
  • A CBP can generalize surface features of a problem by generalizing to the level of the rules. This means generalizing an object in an explanation up to the highest possible level of generality while still staying within the confines of the rules that explain the failure.
  • Intermediate states that serve as links in the casual chain that led to a failure are also linked to the memory of the failure and thus can be used to predict it if they arise in late situations.
  • Why not just index plans by the features that predict the problem that they solve directly?
    • Problems are described in few words and it’s inefficient to store all the features that describe a problem!
    • Ability re recovering from failure.
  • CBP stores names or repairs that it makes (critics) indexed by problems they solve.
  • A CBP can only save those repairs that can be transferred to any plan in which the problem that they repair arises.
  • Here we imply that: Learning is the organization of experience.
  • The most important problem here is deciding how to describe and store an experience so that it can be used again.
  • Learner job is to collect features that should be used to index the planner’s work and then store that work away for later reuse.
  • Planner’s Job:
    • Provide learner with content of what is learned.
    • Building explanations that is used to assign blame for a failure.
    • Building batch that is stored as a new ingredient critic.
  • Learning means storing the different results of the planner’s activity indexed by the features that determine their usefulness.
  • A planner knows when to learn.
  • Learning from planning is an improvement over other types of learning in that:
    • It uses the knowledge of the planner to determine:
      • What it learns.
      • How to index it.
      • When to learn it at all.
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