Explanations in cognitive science

Cognitive science may be loosely described as the activity of trying to model aspects of human behaviour upon a computer. It has emerged as a blending of the techniques of artificial intelligence and cognitive psychology but these two disciplines have different, and incompatible, philosophies. Searle has detected elements of behaviourism and operationism within artificial intelligence, whereas cognitive psychology is essentially anti-behaviourist. It is argued that cognitive science needs to distinguish between competence and performance in order to clarify this point, and that such a distinction has far-reaching effects on how we view computer simulations of behaviour.     

Problem solving through imitation

This paper presents an approach to problem solving through imitation. It introduces the Statistical and Temporal Percept Action Coupling (ST-PAC) System which statistically models the dependency between the perceptual state of the world and the resulting actions that this state should elicit. The ST-PAC system stores a sparse set of experiences provided by a teacher. These memories are stored to allow efficient recall and generalisation over novel systems states. Random exploration is also used as a fall-back “brute-force” mechanism should a recalled experience fail to solve a scenario. Statistical models are used to couple groups of percepts with similar actions and incremental learning used to incorporate new experiences into the system. The system is demonstrated within the problem domain of a children’s shape sorter puzzle. The ST-PAC system provides an emergent architecture where competence is implicitly encoded within the system. In order to train and evaluate such emergent architectures, the concept of the Complexity Chain is proposed. The Complexity Chain allows efficient structured learning in a similar fashion to that used in biological system and can also be used as a method for evaluating a cognitive system’s performance. Tests demonstrating the Complexity Chain in learning are shown in both simulated and live environments. Experimental results show that the proposed methods allowed for good generalisation and concept refinement from an initial set of sparse examples provided by a tutor.     

Neural constraints in cognitive science

The paper is an examination of the ways and extent to which neuroscience places constraints on cognitive science. In Part I, I clarify the issue, as well as the notion of levels in cognitive inquiry. I then present and address, in Part II, two arguments designed to show that facts from neuroscience are at a level too low to constrain cognitive theory in any important sense. I argue, to the contrary, that there are several respects in which facts from neurophysiology will constrain cognitive theory. Part III then turns to an examination of Connectionism and Classical Cognitivism to determine which, if either, is in a better position to accomodate neural constraints in the ways suggested in Part II.     

Problem solving with spreadsheets*

The recent availability of spreadsheets has provided new opportunities for pupil and student use of microcomputers. Whilst much has been written on the use of database and word processing packages, the educational uses of spreadsheets is relatively undocumented. In this paper reference is made to exploratory work aimed at providing opportunities for additional experience in problem solving.     

PROTRAN: Problem solving software

PROTRAN is the name of a family of problem-solving software systems designed to provide quick and convinient access to the computational power of the IMSL Library of mathematical and statistical Fortran subroutines. This paper discusses the structure and implementation of PROTRAN using as illustration MATH/PROTRAN which provides access to the mathematical areas of the Library. PROTRAN accepts problem specifications, checks them for consistency, selects the appropriate IMSL routines and generates a Fortran program. All programming requirements such as workspace assignment and error message handling are handled automatically by PROTRAN. The generated Fortran program goes through the usual steps of compilation, linking, and execution to produce a solution to the problem. PROTRAN adds to the problem-solving power of Fortran by adding very high level problem-solving statements and it adds to the reliability of Fortran by providing extensive error checking and automating the details of using the IMSL Library.     

Problem solving with the ATMS

An assumption-based truth maintenance system provides a very general facility for all types of default reasoning. However, the ATMS is only one component of an overall reasoning system. This paper presents a set of concerns for interfacing with the ATMS, an interface protocol, and an example of a constraint language based on the protocol. The paper concludes with a comparison of the ATMS and the view of problem solving it entails with other approaches.     

Theory and experiment in cognitive science

Margaret Boden's Mind as Machine: A History of Cognitive Science is a masterfully broad and informative history of computational theories of mental operations. However, its relative neglect of research in psychology and neuroscience yields a somewhat misleading picture of cognitive science, which ideally combines theory and experiment.     

Symbolic computational techniques for solving games

Games are useful in modular specification and analysis of systems where the distinction among the choices controlled by different components (for instance, the system and its environment) is made explicit. In this paper, we formulate and compare various symbolic computational techniques for deciding the existence of winning strategies. The game structure is given implicitly, and the winning condition is either a reachability game of the form p until q (for state predicates p and q) or a safety game of the form Always p.For reachability games, the first technique employs symbolic fixed-point computation using ordered binary decision diagrams (BDDs) [9]. The second technique checks for the existence of strategies that ensure winning within k steps, for a user-specified bound k, by reduction to the satisfiability of quantified boolean formulas. Finally, the bounded case can also be solved by reduction to satisfiability of ordinary boolean formulas, and we discuss two techniques, one based on encoding the strategy tree and one based on encoding a witness subgraph, for reduction to Sat. We also show how some of these techniques can be adopted to solve safety games. We compare the various approaches by evaluating them on two examples for reachability games, and on an interface synthesis example for a fragment of TinyOS [15] for safety games. We use existing tools such as Mocha [4], Mucke [7], Semprop [19], Qube [12], and Berkmin [13] and contrast the results.     

Thinking persons and cognitive science

Cognitive psychology and cognitive science are concerned with a domain of cognition that is much broader than the realm of judgement, belief, and inference. The idea of states with semantic content is extended far beyond the space of reasons and justification. Within this broad class of states we should, however, differentiate between the states distinctive of thinking persons — centrally, beliefs, desires, and intentions — and other states. The idea of consciousness does not furnish a principle of demarcation. But the distinction between states whose content is conceptualized by the person whose states they are and states for which this is not so is more promising. This principle of demarcation contains the seeds of a problem for distributed connectionism. The article ends with some more general reflections about cognitive science.     

Speed-up techniques for solving large-scale biobjective TSP

In this paper, we present the Two-Phase Pareto Local Search (2PPLS) method with speed-up techniques for the heuristic resolution of the biobjective traveling salesman problem. The 2PPLS method is a state-of-the-art method for this problem. However, because of its running time that strongly grows with the instances size, the method can be hardly applied to instances with more than 200 cities. We thus adapt some speed-up techniques used in single-objective optimization to the biobjective case. The proposed method is able to solve instances with up to 1000 cities in a reasonable time with no, or very small, reduction of the quality of the generated approximations.     

Network techniques for solving asset diversification problems in finance

In this paper, we study a problem of asset diversification or dynamic portfolio selection arising in the field of finance. This problem is first formulated as a nonlinear mathematical program and is solved via a special-purpose computer code that exploits the underlying network structure. A second model for asset diversification is presented which is linear in the decision variables and is therefore much easier to solve computationally than the first formulation. On the other hand, the linear model produces solutions that are consistently inferior to those generated by the nonlinear program in terms of expected profit. This trade-off of accuracy versus efficiency is discussed and assessed for several sample problems. Extensive computational results are presented.     

Solipsism,individualism and cognitive science

Abstract

‘Artificial Intelligence cannot ignore philosophy’ (McCarthy 1988) I shall challenge the claim that good old-fashioned artificial intelligence, or GOFAI (Haugeland 1985) is solipsistic while more recent neural or ‘brain-style’ approaches to AI are not (Rumelhart et al. 1986). After distinguishing GOFAI from connectionism, I will first show that GOFAI is not committed to solipsism but rather to what is more properly called individualism. I argue that the feature of GOFAI which entails individualism is shared by connectionism. Individualism is a metaphysical assumption of both types of AI, one which may indeed be pernicious. It is an assumption which must be located and understood.     

Levels of description and explanation in cognitive science

The notion of levels has been widely used in discussions of cognitive science, especially in discussions of the relation of connectionism to symbolic modeling of cognition. I argue that many of the notions of levels employed are problematic for this purpose, and develop an alternative notion grounded in the framework of mechanistic explanation. By considering the source of the analogies underlying both symbolic modeling and connectionist modeling, I argue that neither is likely to provide an adequate analysis of processes at the level at which cognitive theories attempt to function: One is drawn from too low a level, the other from too high a level. If there is a distinctly cognitive level, then we still need to determine what are the basic organizational principles at that level.     

求解单容器矩形装箱问题的混合算法

单容器矩形装箱问题是一个典型的组合优化问题,其在工业界有着广泛的应用。首先提出了一种新的启发式布局算法,即底部匹配算法,该算法在矩形的装入过程中根据五种启发式规则自动选择与装填区域相匹配的下一个矩形。然后将底部匹配算法与遗传算法相结合形成混合算法来求解单容器矩形装箱问题。实验结果表明,与已有算法相比,该文提出的算法更加有效。     

Problem solving and collaboration using mobile serious games

This paper presents the results obtained with the implementation of a series of learning activities based on Mobile Serious Games (MSGs) for the development of problem solving and collaborative skills in Chilean 8th grade students. Three MSGs were developed and played by teams of four students in order to solve problems collaboratively. A quasi-experimental design was used. The data shows that the experimental group achieved a higher perception of their own collaboration skills and a higher score in the plan execution dimension of the problem solving cycle than did the non-equivalent control group, revealing that MSG-based learning activities may contribute to such learning improvements. This challenges future research to identify under which conditions learning activities based on mobile serious games can promote the development of higher order skills.     

求解车辆路径安排问题的混合遗传算法

讨论了具有容量限制的车辆路径安排问题,设计了一个高效混合遗传算法。针对简单遗传算法易收敛于局部最优解的缺点,算法设计了交叉规则和选择策略。只有当两个个体的评价函数值满足一定条件时,才能进行交叉操作。采用优良个体保留策略执行选择操作,设计了保留函数。算法依据顶点间的位置关系,设计了优化策略,在每代进化中按概率选择一定数量的个体执行优化操作。数据实验表明,该算法是一个有效的求解车辆路径安排问题的混合遗传算法。