One-switch utility functions and a measure of risk
Management Science
Planning under time constraints in stochastic domains
Artificial Intelligence - Special volume on planning and scheduling
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Management Science
ICTAI '07 Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence - Volume 01
Functional value iteration for decision-theoretic planning with general utility functions
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Risk-sensitive planning with one-switch utility functions: value iteration
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Solving time-dependent planning problems
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
State space search for risk-averse agents
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Brief paper: Risk-sensitivity conditions for stochastic uncertain model validation
Automatica (Journal of IFAC)
Combining rational and biological factors in virtual agent decision making
Applied Intelligence
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Risk attitude reflects an intelligent agent's preference for different uncertain rewards. Cost is the resource consumed; wealth is the total amount of the resource an agent holds. In a risk-aware system whose risk attitude is not independent of wealth, utility is a function of wealth. Given the utility function is one-switch, if we simply use cost based utility functions for the reasoning, unless the initial wealth is zero, we cannot precisely obtain the optimum preference in every decision step. A bridge algorithm between cost and wealth helps us solve this problem. We provide a framework of the bridge algorithm for risk-aware Markov decision processes. We present an example of the block-world problem to explain the algorithm.An effective bridge algorithm helps planners to make reasonable decisions according to their risk attitudes, without changing the structure of Markov domains. A bridge between cost and wealth enables us to deal with planning domains using the powerful backward induction approach instead of decision tree. This will have profound theoretical and realistic influence on artificial intelligence, economics, health care, as well as other areas concerning risk.