Rethinking emotion: New research in emotion and recent debates in cognitive science.

Cognitive science is currently the scene of a number of exciting debates. The so-called 'classical' approach, which has dominated the field since the 1950s, is increasingly being challenged on various fronts. Evolutionary psychologists and researchers in artificial life accuse classical cognitive scientists of ignoring the fact that natural cognition is not designed to solve abstract problems and prove theorems but to solve particular adaptive problems. Those working with a 'situated' view of the mind are challenging the classical commitment to internalism. Finally, proponents of dynamical approaches claim that the discrete models favoured by the classical approach are too coarse-grained and impute too much internal structure to the mind. In this thesis I argue that the 'non-classical' approaches are compatible with classical cognitive science, with the important proviso that compatibility comes in different kinds. In the final chapter I outline a vision of a comprehensive 'integrated non-classical cognitive science' that combines the three non-classical approaches into a single conceptual bundle. I illustrate these claims about cognitive science in general with reference to a particular field of research: the emotions. Emotions were ignored by most classical cognitive scientists, though some models of emotion were developed within the classical framework. These models, however, provided no way of distinguishing emotion from cognition. I argue that the non-classical approaches remedy this problem, and together provide a new way of thinking about the emotions which I dub 'the interruption theory'. Since the interruption theory borrows insights from all three of the non-classical forms of cognitive science, it serves as a good example of the integrated non-classical approach that I recommend for cognitive science in general

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

A complex systems approach to education in Switzerland

The insights gained from the study of complex systems in biological, social, and engineered systems enables us not only to observe and understand, but also to actively design systems which will be capable of successfully coping with complex and dynamically changing situations. The methods and mindset required for this approach have been applied to educational systems with their diverse levels of scale and complexity. Based on the general case made by Yaneer Bar-Yam, this paper applies the complex systems approach to the educational system in Switzerland. It confirms that the complex systems approach is valid. Indeed, many recommendations made for the general case have already been implemented in the Swiss education system. To address existing problems and difficulties, further steps are recommended. This paper contributes to the further establishment complex systems approach by shedding light on an area which concerns us all, which is a frequent topic of discussion and dispute among politicians and the public, where billions of dollars have been spent without achieving the desired results, and where it is difficult to directly derive consequences from actions taken. The analysis of the education system's different levels, their complexity and scale will clarify how such a dynamic system should be approached, and how it can be guided towards the desired performance

Accelerated cooperative co-evolution on multi-core architectures

The Cooperative Co-Evolution model has been used in Evolutionary Computation to optimize the training of artificial neural networks (ANNs). This architecture has proven to be a useful extension to domains such as Neuro-Evolution (NE), which is the training of ANNs using concepts of natural evolution. However, there is a need for real-time systems and the ability to solve more complex tasks which has prompted a further need to optimize these Cooperative Co-Evolution methods. Cooperative Co-Evolution methods consist of a number of phases, however the evaluation phase is still the most compute intensive phase, for some complex tasks taking as long as weeks to complete. This study uses NE as a test case study and we design a parallel Cooperative Co-Evolution processing framework and implement the optimized serial and parallel versions using the Golang (Go) programming language. Go is a multi-core programming language with first-class constructs, channels and goroutines, that make it well suited to parallel programming. Our study focuses on Enforced Subpopulations (ESP) for single-agent systems and Multi-Agent ESP for multi-agent systems. We evaluate the parallel versions in the benchmark tasks; double pole balancing and prey-capture, for single and multi-agent systems respectively, in tasks of increasing complexity. We observe a maximum speed-up of 20x for the parallel Multi-Agent ESP implementation over our single core optimized version in the prey-capture task and a maximum speedup of 16x for ESP in the harder version of double pole balancing task. We also observe linear speed-ups for the difficult versions of the tasks for a certain range of cores, indicating that the Go implementations are efficient and that the parallel speed-ups are better for more complex tasks. We find that in complex tasks, the Cooperative Co-Evolution Neuro-Evolution (CCNE) methods are amenable to multi-core acceleration, which provides a basis for the study of even more complex Cooperative Co-Evolution methods in a wider range of domains

Information-theoretic Reasoning in Distributed and Autonomous Systems

The increasing prevalence of distributed and autonomous systems is transforming decision making in industries as diverse as agriculture, environmental monitoring, and healthcare. Despite significant efforts, challenges remain in robustly planning under uncertainty. In this thesis, we present a number of information-theoretic decision rules for improving the analysis and control of complex adaptive systems. We begin with the problem of quantifying the data storage (memory) and transfer (communication) within information processing systems. We develop an information-theoretic framework to study nonlinear interactions within cooperative and adversarial scenarios, solely from observations of each agent's dynamics. This framework is applied to simulations of robotic soccer games, where the measures reveal insights into team performance, including correlations of the information dynamics to the scoreline. We then study the communication between processes with latent nonlinear dynamics that are observed only through a filter. By using methods from differential topology, we show that the information-theoretic measures commonly used to infer communication in observed systems can also be used in certain partially observed systems. For robotic environmental monitoring, the quality of data depends on the placement of sensors. These locations can be improved by either better estimating the quality of future viewpoints or by a team of robots operating concurrently. By robustly handling the uncertainty of sensor model measurements, we are able to present the first end-to-end robotic system for autonomously tracking small dynamic animals, with a performance comparable to human trackers. We then solve the issue of coordinating multi-robot systems through distributed optimisation techniques. These allow us to develop non-myopic robot trajectories for these tasks and, importantly, show that these algorithms provide guarantees for convergence rates to the optimal payoff sequence

On the definition of non-player character behaviour for real-time simulated virtual environments.

Computer games with complex virtual worlds, which are populated by artificial characters and creatures, are the most visible application of artificial intelligence techniques. In recent years game development has been fuelled by dramatic advances in computer graphics hardware which have led to a rise in the quality of real-time computer graphics and increased realism in computer games. As a result of these developments video games are gaining acceptance and cultural significance as a form of art and popular culture. An important factor for the attainment of realism in games is the artificially intelligent behaviour displayed by the virtual entities that populate the games' virtual worlds. It is our firm belief that to further improve the behaviour of virtual entities, game AI development will have to mirror the advances achieved in game graphics. A major contributing factor for these advancements has been the advent of programmable shaders for real-time graphics, which in turn has been significantly simplified by the introduction of higher level programming languages for the creation of shaders. This has demonstrated that a good system can be vastly improved by the addition of a programming language. This thesis presents a similar (syntactic) approach to the definition of the behaviour of virtual entities in computer games. We introduce the term behaviour definition language (BDL), describing a programming language for the definition of game entity behaviour. We specify the requirements for this type of programming language, which are applied to the development and implementation of several behaviour definition languages, culminating in the design of a new game-genre independent behaviour definition (scripting) language. This extension programming language includes several game AI techniques within a single unified system, allowing the use of different methods of behaviour definition. A subset of the language (itself a BDL) was implemented as a proof of concept of this design, providing a framework for the syntactic definition of the behaviour of virtual entities in computer games