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We address the issue, in cognitive agents, of possible loss of previous information, which later might turn out to be correct when new information becomes available. To this aim, we propose a framework for changing the agent's mind without erasing forever previous information, thus allowing its recovery in case the change turns out to be wrong. In this new(More)
We propose an integrated theoretical framework, grounded in possibility theory, to account for all the aspects involved in representing and changing beliefs, representing and generating justified desires , and selecting goals based on current and uncertain beliefs about the world, and the preferences of the agent. Beliefs and desires of a cognitive agent(More)
In brief, this license authorizes each and everybody to share (to copy, distribute and transmit) the work under the following conditions, without impairing or restricting the authors' moral rights: Attribution: The work must be attributed to its authors. The series Dagstuhl Follow-Ups is a publication format which offers a frame for the publication of(More)
This paper proposes a framework for planning under uncertainty given a partially known initial state and a set of actions having nondeterministic (disjunctive) eeects, some being more possible (normal) than the others. The problem, henceforth called possibilistic planning problem, is represented in an extension of the STRIPS formalism in which the initial(More)
A rational agent adopts (or changes) its goals when new information (beliefs) becomes available or its desires (e.g., tasks it is supposed to carry out) change. In conventional approaches to goal generation in which a goal is considered as a " particular " desire, a goal is adopted if and only if all conditions leading to its generation are satisfied. It is(More)
Two independent evolutionary modeling methods, based on fuzzy logic and neural networks respectively, are applied to predicting trend reversals in financial time series, and their performances are compared. Both methods are found to give essentially the same results, indicating that trend reversals are partially predictable.