By Ioannis Hatzilygeroudis, Vasile Palade, Jim Prentzas
This quantity comprises prolonged and revised types of the papers offered on the 4th Workshop on “Combinations of clever tools and purposes” (CIMA 2014) which used to be meant to turn into a discussion board for replacing adventure and concepts between researchers and practitioners facing mixtures of alternative clever tools in synthetic Intelligence. the purpose is to create built-in or hybrid tools that take advantage of every one in their parts. many of the current offered efforts mix tender computing equipment (fuzzy good judgment, neural networks and genetic algorithms). one other flow of efforts integrates case-based reasoning or computer studying with soft-computing tools. many of the combos were extra extensively explored, like neuro-symbolic tools, neuro-fuzzy equipment and strategies combining rule-based and case-based reasoning. CIMA 2014 was once held together with the 26th IEEE overseas convention on instruments with man made Intelligence (ICTAI 2014).
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Extra resources for Combinations of Intelligent Methods and Applications: Proceedings of the 4th International Workshop, CIMA 2014, Limassol, Cyprus, November 2014 (at ICTAI 2014)
The application under discussion is an ant-based system, which was adapted to handle two new problem characteristics: uncertainty and dynamicity. For testing it, we used 24 instances with thousands of vertices of the Traveling Salesman Problem (TSP, a well-known Combinatorial Optimization Problem—COP). The results are compared with those obtained when the original software package solved the same 24 instances. Our work shows that the ant-based implementation can easily handle the dynamic uncertainty, being able to deliver results having the same quality as in the classical case.
2). 1 AgentSpeak We first define how an agent program can be written. We use S to denote a finite set of symbols for predicates, actions, and constants, and V to denote a set of variables. Following convention in logic programming, elements from S are written using lowercase letters and elements from V using uppercase letters. We use the standard 22 K. Bauters et al. first-order logic definition of a term1 and we use t as a compact notation for t1 , . . e. a vector of terms. We have : Definition 1 If b is a predicate symbol, and t are terms, then b(t) is a belief atom.
Nevertheless, future research showed that BDI and MDP are closely linked. Indeed, in  the relationship between the policies of MDPs and the intentions in the BDI architecture is discussed. In particular, it shows that intentions in the BDI architecture can be mapped to policies in MDPs. This in turn led to some work in the literature on hybrid BDI-POMDP approaches. In  an algorithm was proposed to build AgentSpeak plans from optimal POMDP policies. However, most characteristics of the original BDI framework are not retained in such hybrid approaches.