FLINS, originally an acronym for Fuzzy Logic and Intelligent Technologies in Nuclear Science, is extended to Applied Artificial Intelligence for Applied Research. This work covers state-of-the-art research and development in applied artificial intelligence for applied research in general and for power/nuclear engineering in particular.

Learning Techniques in Service Robotic Environment (Z Z Bien et al.); The Role of Soft Computing in Applied Sciences (P P Wang); New Operators for Context Adaptation of Mamdani Fuzzy Systems (A Botta et al.); Lukasiewicz Algebra Model of Linguistic Values of Truth and Their Reasoning (L Yi et al.); Annihilator and Alpha-Subset (X Q Long et al.); On PCA Error of Subject Classification (L H Feng et al.); Knowledge Discovery for Customer Classification on the Principle of Maximum Profit (C Zeng et al.); Fuzzy Multi-Objective Interactive Goal Programming Approach to Aggregate Production Planning (T Ertay); Analysing Success Criteria for ICT Projects (K Milis & K Vanhoof); Prioritization of Relational Capital Measurement Indicators Using Fuzzy AHP (A Beskese & F T Bozbura); Risk Analysis and Management of Urban Rainstorm Water Logging in Tianjin (S Han et al.); Obstacle Avoidance Learning for Biomimetic Robot Fish (Z Shen et al.); Urban Signal Control Using Intelligent Agents (M A Alipour & S Jalili); Parallel Evolutionary Methods Applied to a PWR Core Reload Pattern Optimization (R Schirru et al.); and other papers.