Posts tagged Alban
Virtue Signalling: Reassuring Observers of Machine Behaviour

We propose a constraint on machine behaviour: that partially observed machine systems ought to reassure observers that they understand the constraints that they are under and that they have and will abide by those constraints. Specifically, a system should not follow a course of action that, from the point of view of the observer, is not easily distinguishable from a course of action that is forbidden.

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A Planning Framework to Solve Conformant Planning Problems through a Counterexample Guided Refinement

In this paper, published in Artificial Intelligence, Alban Grastien and co-author address the problem of conformant planning, which consists in finding a sequence of actions in a well-specified environment that achieves a specified goal despite uncertainty on the initial configuration and without using observations.

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Computing Superior Counter-Examples for Conformant Planning

In a counterexample based approach to conformant planning, choosing the right counterexample can improve performance. We formalise this observation by introducing the notion of “superiority” of a counterexample over another one,that holds whenever the superior counterexample exhibits more tags than the latter. We provide a theoretical explanation that supports the strategy of searching for maximally superior counterexamples, and we show how this strategy can be implemented. The empirical experiments validate our approach.

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