The challenge of complexity for cognitive systems

Complex cognition addresses research on (a) high-level cognitive processes – mainly problem solving, reasoning, and decision making – and their interaction with more basic processes such as perception, learning, motivation and emotion and (b) cognitive processes which take place in a complex, typically dynamic, environment. Our focus is on AI systems and cognitive models dealing with complexity and on psychological findings which can inspire or challenge cognitive systems research. In this overview we first motivate why we have to go beyond models for rather simple cognitive processes and reductionist experiments. Afterwards, we give a characterization of complexity from our perspective. We introduce the triad of cognitive science methods – analytical, empirical, and engineering methods – which in our opinion have all to be utilized to tackle complex cognition. Afterwards we highlight three aspects of complex cognition – complex problem solving, dynamic decision making, and learning of concepts, skills and strategies. We conclude with some reflections about and challenges for future research

The BB-SR system

This paper briefly describes the BB-SR system. BB-SR integrates the blackboard model of problem solving with a powerful object-oriented knowledge represenation system. The combined blackboard and knowledge base system provides a sophisticated and powerful environment within which to examine self-reflection in problem solving and within which to develop systems to solve complex problems

A systems and cybernetic perspective on complex problem solving

This thesis is concerned with Organisational Problem Solving. The work reflects the complexities of organisational problem situations and the eclectic approach that has been necessary to gain an understanding of the processes involved. The thesis is structured into three main parts. Part I describes the author's understanding of problems and suitable approaches. Chapter 2 identifies the Transcendental Realist (TR) view of science (Harre 1970, Bhaskar 1975) as the best general framework for identifying suitable approaches to complex organisational problems. Chapter 3 discusses the relationship between Checkland's methodology (1972) and TR. The need to generate iconic (explanatory) models of the problem situation is identified and the ability of viable system modelling to supplement the modelling stage of the methodology is explored in Chapter 4. Chapter 5 builds further on the methodology to produce an original iconic model of the methodological process. The model characterises the mechanisms of organisational problem situations as well as desirable procedural steps. The Weltanschauungen (W's) or "world views" of key actors is recognised as central to the mechanisms involved. Part II describes the experience which prompted the theoretical investigation. Chapter 6 describes the first year of the project. The success of this stage is attributed to the predominance of a single W. Chapter 7 describes the changes in the organisation which made the remaining phase of the project difficult. These difficulties are attributed to a failure to recognise the importance of differing W's. Part III revisits the theoretical and organisational issues. Chapter 8 identifies a range of techniques embodying W's which are compatible with .the framework of Part I and which might usefully supplement it. Chapter 9 characterises possible W's in the sponsoring organisation. Throughout the work, an attempt is made to reflect the process as well as the product of the author's learning

The Value and Costs of Modularity: A Cognitive Perspective

This paper discusses the issue of modularity from a problem-solving perspective. Modularity is in fact a decomposition heuristic, through which a complex problem is decomposed into independent or quasi-independent sub-problems. By means of a model of problem decomposition, this paper studies the trade-offs of modularity: on the one hand finer modules increase the speed of search, but on the other hand they usually determine lock-in into sub-optimal solutions. How effectively to balance this trade-off depends upon the problem environment and its complexity and volatility: we show that in stationary and complex environments there exists an evolutionary advantage to over-modularization, while in highly volatile – though “simple” – en- vironments, contrary to usual wisdom, modular search is inefficient. The empirical relevance of our findings is discussed, especially with reference to the literature on system integration.modularity, problem solving, complex systems

Metasynthetic computing for solving open complex problems

Complex systems, in particular, open complex giant systems have become one of major challenges to many current disciplines such as system sciences, cognitive sciences, intelligence sciences, computer sciences, and information sciences. An appropriate methodology for dealing with them is the theory of qualitative-to-quantitative metasynthesis. From the perspective of engineering, we propose the concept of metasynthetic computing. This paper discusses the theoretical frame-work, problem-solving process and intelligence emergence of metasynthetic computing from both engineering and cognition perspectives. These efforts can help one understand complex systems and design effective problem-solving systems. © 2008 IEEE

Propagators and Solvers for the Algebra of Modular Systems

To appear in the proceedings of LPAR 21. Solving complex problems can involve non-trivial combinations of distinct knowledge bases and problem solvers. The Algebra of Modular Systems is a knowledge representation framework that provides a method for formally specifying such systems in purely semantic terms. Formally, an expression of the algebra defines a class of structures. Many expressive formalism used in practice solve the model expansion task, where a structure is given on the input and an expansion of this structure in the defined class of structures is searched (this practice overcomes the common undecidability problem for expressive logics). In this paper, we construct a solver for the model expansion task for a complex modular systems from an expression in the algebra and black-box propagators or solvers for the primitive modules. To this end, we define a general notion of propagators equipped with an explanation mechanism, an extension of the alge- bra to propagators, and a lazy conflict-driven learning algorithm. The result is a framework for seamlessly combining solving technology from different domains to produce a solver for a combined system.Comment: To appear in the proceedings of LPAR 2