By Lucia Sacchi, Carla Rognoni, Stefania Rubrichi, Silvia Panzarasa, Silvana Quaglini (auth.), Niels Peek, Roque Marín Morales, Mor Peleg (eds.)
This booklet constitutes the refereed complaints of the 14th convention on man made Intelligence in medication, AIME 2013, held in Murcia, Spain, in May/June 2013. The forty three revised complete and brief papers awarded have been conscientiously reviewed and chosen from eighty two submissions. The papers are geared up within the following topical sections: determination aid, guidance and protocols; semantic know-how; bioinformatics; computing device studying; probabilistic modeling and reasoning; photo and sign processing; temporal info visualization and research; and traditional language processing.
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Extra info for Artificial Intelligence in Medicine: 14th Conference on Artificial Intelligence in Medicine, AIME 2013, Murcia, Spain, May 29 – June 1, 2013. Proceedings
These features fulﬁll the main requirements of a cooperative distributed problem solving process. Moreover, the generation of a personalized treatment plan from multiple CPGs, for a comorbid patient, can be addressed as a multi-agent cooperative planning process. Concretely this work presents a multi-agent planning architecture in which each agent is capable of (1) planning a personalized treatment for a given patient, from a temporal Hierarchical Task Network (HTN) representation of a single-disease guideline, and (2) coordinating with other planning agents by both sharing their local, disease-speciﬁc recommendations, and resolving the eventual conﬂicts that may arise when conciliating their single-disease plans.
The extensions to handle iterative actions and numerical values, described below, aﬀect the preparation phase of the algorithm where logical representations of actionable graphs are constructed. Handling cycles requires a new step in the preparation phase, where a cyclic actionable graph is transformed into an acyclic one by expanding the cycle. The two key issues with cycles are: (1) they pose a problem when transforming an actionable graph into a logical model because a unique variable is needed for each action (and actions in cycles occur multiple times), and (2) often there is no clear deﬁnition of the number of iterations or a stopping condition.
PAFn where each PAFi expands the corresponding AFi with S by taking account the arguments, the attacks and the non-attacks from the other AFs in S. For this purpose, we focus on the consensual expansion proposed by . In order to expand an AFi on a PAFi, we consider all the arguments and only the consensual attacks. Definition 3 (Consensual Expansion). Let S = AF1 , . . , AFn be a profile of n argumentation frameworks AFi = Ai , Ri with 1 ≤ i ≤ n. Let con f (S) = ( i Ri ) ∩ ( i Ni ) be the set of nonconsensual attacks.