Andrea Tettamanzi

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In this paper we introduce ALCQF , a fuzzy description logic with extended qualified quantification. The proposed language allows for the definition of fuzzy quantifiers of the absolute and relative kind by means of piecewise linear functions on N and Q∩ [0, 1] respectively. These quantifiers extends the usual (qualified) ∃, ∀ and number restriction. The(More)
In this paper, we present quantitative models for the selection pressure of cellular evolutionary algorithms on regular oneand two-dimensional (2-D) lattices. We derive models based on probabilistic difference equations for synchronous and several asynchronous cell update policies. The models are validated using two customary selection methods: binary(More)
We present discrete stochastic mathematical models for the growth curves of synchronous and synchronous evolutionary algorithms with populations structured ccording to a random graph. We show that, to good approximation, randomly structured and panmictic populations have the some growth behavior. Furthermore, we show that global selection intensity depends(More)
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)
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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 are(More)
Protein design aims at designing new protein molecules of desired structure and functionality. One of the major obstacles to large-scale protein design are the extensive time and manpower requirements for experimental validation of designed sequences. Recent advances in protein structure prediction have provided potentials for an automated assessment of the(More)