By Ana L.C. Bazzan, Karim Pichara
This ebook constitutes the refereed court cases of the 14th Ibero-American convention on man made Intelligence, IBERAMIA 2014, held in Santiago de Chile, Chile, in November 2014. The sixty four papers offered have been conscientiously reviewed and chosen from 136 submissions. The papers are equipped within the following topical sections: wisdom engineering, wisdom illustration and probabilistic reasoning making plans and scheduling usual language processing computer studying fuzzy structures wisdom discovery and knowledge mining bio-inspired computing robotics imaginative and prescient multi-agent structures agent-based modeling and simulation AI in schooling, affective computing, and human-computer interplay functions of AI and ambient intelligence.
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Extra resources for Advances in Artificial Intelligence -- IBERAMIA 2014: 14th Ibero-American Conference on AI, Santiago de Chile, Chile, November 24-27, 2014, Proceedings
The complexity of recognition of linguistically adequate dependency grammars. In: Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics, pp. 337–343. Association for Computational Linguistics, Madrid (1997) 12. : Parsing extended LR(k) grammars. Acta Informatica 15(2), 115–127 (1981) 13. : Computational complexity of current GPSG theory. In: Proc. of the 24th ACL, pp. 30–39, New York (1986) 14. : Recognition of Linear Context-Free Rewriting Systems. In: ACL, pp.
For this reason, in the following section we propose an argumentation-based procedure to answer queries in defeasible Datalog± ontologies. 4 Argumentation-Based Reasoning in Defeasible Datalog± Conflicts in defeasible Datalog± ontologies come, as in classical Datalog± , from the violation of negative constraints. Intuitively, two atoms are in conflict relative to a defeasible Datalog± ontology whenever they are both derived from the ontology (either strictly o defeasible) and together map to the body of a negative constraint.
Traverse the multi-stack list ms, until a node ni is found such that t ≤O ni . A new node nt is created and the adjacency list and the multi-stack ms are updated. If the list is exhausted (there was no ni such that t ≤O ni , then the new node nt is added to the multi-stack being the last element in the list. The output is returned with a single element (line 15 or 19-21). M. Casta˜no 3. A pop production (lines 23-31) μ is t¯. Check whether t matches n0 the first element in the multi-stack list, the least element in the input multi-stack (line 23).
Advances in Artificial Intelligence -- IBERAMIA 2014: 14th Ibero-American Conference on AI, Santiago de Chile, Chile, November 24-27, 2014, Proceedings by Ana L.C. Bazzan, Karim Pichara