One-switch utility functions and a measure of risk
Management Science
SouthamptonTAC: An adaptive autonomous trading agent
ACM Transactions on Internet Technology (TOIT)
Dynamic Programming
Region-based incremental pruning for POMDPs
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Point-Based Value Iteration for Continuous POMDPs
The Journal of Machine Learning Research
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Functional value iteration for decision-theoretic planning with general utility functions
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Value-function approximations for partially observable Markov decision processes
Journal of Artificial Intelligence Research
Perseus: randomized point-based value iteration for POMDPs
Journal of Artificial Intelligence Research
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Planning under continuous time and resource uncertainty: a challenge for AI
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
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Partially Observable Markov Decision Process (POMDP) is a popular framework for planning under uncertainty in partially observable domains. Yet, the POMDP model is risk-neutral in that it assumes that the agent is maximizing the expected reward of its actions. In contrast, in domains like financial planning, it is often required that the agent decisions are risk-sensitive (maximize the utility of agent actions, for non-linear utility functions). Unfortunately, existing POMDP solvers cannot solve such planning problems exactly. By considering piecewise linear approximations of utility functions, this paper addresses this shortcoming in three contributions: (i) It defines the Risk-Sensitive POMDP model; (ii) It derives the fundamental properties of the underlying value functions and provides a functional value iteration technique to compute them exactly and (c) It proposes an efficient procedure to determine the dominated value functions, to speed up the algorithm. Our experiments show that the proposed approach is feasible and applicable to realistic financial planning domains.