Citation: Position Papers of the 2017 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 12, pages 25–32 (2017)
Abstract. A heterogeneous computing model for ontology with preservation functions is presented for concept learning across domains with structural agent morphisms. A computing models based on a novel multi-agent competitive learning with multiplayer game tree plans are applied. Agents tree transformations for the models reach goal plans. Goals are satisfied based on competitive game tree learning. Agent tree computing models are example prototypes for modeling ontology algebras. Plan goals are satisfied based on competitive game tree learning. Novel Description algebras with concept description ontology algebras and description ontology preservation morphisms are presented. Cooperating agents, that have opened new avenues in modeling and implementing agent teams, are ingredients for specific application modeling. Applications to Formal Concept Description are developed with new description logic algebraic models.
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