Let's try searching with partial observations (AIMA ch. 4).

So far, we've been dealing with deterministic, fully observable problems; it turns out that a lot of interesting problems are non-deterministic and/or partially observable.

Last week, we relaxed the determinism constraint; this time, we're going to relax the fully observable one.

See exercise 4.11:

We can turn the navigation problem in Exercise 3.7 into an environment as follows:

  • The percept will be a list of the positions, relative to the agent, of the visible vertices.
  • The percept does not include the position of the robot! The robot must learn its own position from the map; for now, you can assume that each location has a different 'view."
  • Each action will be a vector describing a straight-fine path to follow. If the path is unobstructed, the action succeeds; otherwise, the robot stops at the point where its path first intersects an obstacle. If the agent returns a zero motion vector and is at the goal (which is fixed and known), then the environment teleports the agent to a random location (not inside an obstacle).
  • The performance measure charges the agent l point for each unit of distance traversed and awards 1000 points each time the goal is reached.
  1. Implement this environment and a problem-solving agent for it. After each teleportation, the agent will need to formulate a new problem, which will involve discovering its current location.
  2. Document your agent's perfornumce (by having the agent generate suitable commentary as it moves around) and report its performance over 100 episodes.
  3. Modify the environment so that 30% of the time the agent ends up at an unintended destination (chosen randomly from the other visible vertices if any: otherwise, no move at all). This is a crude model of the motion errors of a real robot. Modify the agent so that when such an error is detected, it finds out where it is and then constructs a plan to get back to where it was and resume the old plan. Remember that sometimes getting back to where it was might also fail! Show an example of the agent successfully overcoming two successive motion errors and still reaching the goal.

Sayat mentioned he was interested in 4.12; that's not bad, either.

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  • Peter D.

    We had some great discussion vis-à-vis the privatization of space, the inevitable obsolescence of jobs, and disruption of the electronic medical records space.

    Oh, and we recapped a little non-determinism; talked about some of the research with POMDPs and cyclic AND/OR graph-search.

    January 22, 2013

  • Prasan S.

    ETA 10 mins

    January 21, 2013

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