Methods for planning in stochastic domains often aim for finding plans
that minimize expected execution cost or maximize the probability of
goal achievement. This implies a neutral attitude towards risk.
People, however, are usually not risk-neutral. A gambler, for example,
is willing to accept a plan with a smaller expected reward if the
uncertainty is increased. Consequently, I have developed a mechanism
to incorporate risk-sensitive attitudes into existing probabilistic AI
planners, thus extending their functionality.
Check out my home page
and my other projects!
Last update: January 1 1996