this article introduces the notions of functional space and concept as a way of knowledge representation and abstraction for reinforcement learning agents. these definitions are used as a tool of knowledge transfer among agents. the agents are assumed to be heterogeneous; they have different state spaces but share a same dynamic, reward and action space. in other words, the agents are assumed t...

This article introduces the notions of functional space and concept as a way of knowledge representation and abstraction for Reinforcement Learning agents. These definitions are used as a tool of knowledge transfer among agents. The agents are assumed to be heterogeneous; they have different state spaces but share a same dynamic, reward and action space. In other words, the agents are assumed t...

Abstract Purpose: This research aims to structure a hierarchical model that integrates the industry 4.0 (I4.0) concepts and standardizes based on literature. Originality/value: Kamble et al. (2018) point out lack of architecture represent I4.0 concepts. paper brings an approach relationship between these I4.0. It expands studies by Ghobakhloo Liao (2017) homogenizes terms present in Design/meth...

Our recently introduced technique for multi-objective search of conceptual solutions, based on interactive evolutionary computation, is extended. The extension deals with concepts, which are represented by hierarchical trees of sub-concepts. The survivability of solutions is influenced by both model-based fitness and subjective human preferences. The concepts’ preferences are articulated via th...

The search engine needs relatedness to measure closeness between two concepts for determining optimal results in major applications like information retrieval, information integration and of many more in natural language processing tasks i.e. text classification, word sense disambiguation, matching problems in artificial intelligence etc,. The clustered hierarchical concept network helps to ove...

This paper presents an Ontology Learning From Text (OLFT) method follows the well-known OLFT cake layer framework. Based on the distributional similarity, the proposed method generates multi-level ontologies from comparatively small corpora with the aid of HITS algorithm. Currently, this method covers terms extraction, synonyms recognition, concepts discovery and concepts hierarchical clusterin...

Recently, hierarchical text classification has become an active research topic. The essential idea is that the descendant classes can share the information of the ancestor classes in a predefined taxonomy. In this paper, we claim that each class has several latent concepts and its subclasses share information with these different concepts respectively. Then, we propose a variant Passive-Aggress...