Nathanael Hyafil

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We present a new algorithm for the conformant probabilistic planning problem. This is a planning problem in which we have probabilistic actions and we want to optimize the probability of achieving the goal, but we have no observations available to us during the course of the plan’s execution. Our algorithm is based on a CSP encoding of the problem, and a(More)
A CSP based algorithm for the conformant probabilistic planning problem (CPP) has been presented by Hyafil & Bacchus. Although their algorithm displayed some interesting potential when compared with traditional POMDP algorithms, it was developed using a “flat” representation. In this work we revisit this algorithm and develop a version that utilizes a(More)
Mechanism design has found considerable application to the construction of agent-interaction protocols. In the standard setting, the type (e.g., utility function) of an agent is not known by other agents, nor is it known by the mechanism designer. When this uncertainty is quantified probabilistically, a mechanism induces a game of incomplete information(More)
Classic direct mechanisms require full utility revelation from agents, which can be very difficult in practical multi-attribute settings. In this work, we study partial revelation within the framework of one-shot mechanisms. Each agent’s type space is partitioned into a finite set of partial types and agents (should) report the partial type within which(More)
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