Resource Sharing and Coevolution in Evolving Cellular Automata

Evolving one-dimensional cellular automata (CAs) with genetic algorithms has provided insight into how improved performance on a task requiring global coordination emerges when only local interactions are possible. Two approaches that can affect the search efficiency of the genetic algorithm are coevolution, in which a population of problems---in our case, initial configurations of the CA lattice---evolves along with the population of CAs; and resource sharing, in which a greater proportion of a limited fitness resource is assigned to those CAs which correctly solve problems that fewer other CAs in the population can solve. Here we present evidence that, in contrast to what has been suggested elsewhere, the improvements observed when both techniques are used together depend largely on resource sharing alone.Comment: 8 pages, 1 figure; http://www.santafe.edu/~evca/rsc.ps.g

Revisiting the Edge of Chaos: Evolving Cellular Automata to Perform Computations

We present results from an experiment similar to one performed by Packard (1988), in which a genetic algorithm is used to evolve cellular automata (CA) to perform a particular computational task. Packard examined the frequency of evolved CA rules as a function of Langton's lambda parameter (Langton, 1990), and interpreted the results of his experiment as giving evidence for the following two hypotheses: (1) CA rules able to perform complex computations are most likely to be found near ``critical'' lambda values, which have been claimed to correlate with a phase transition between ordered and chaotic behavioral regimes for CA; (2) When CA rules are evolved to perform a complex computation, evolution will tend to select rules with lambda values close to the critical values. Our experiment produced very different results, and we suggest that the interpretation of the original results is not correct. We also review and discuss issues related to lambda, dynamical-behavior classes, and computation in CA. The main constructive results of our study are identifying the emergence and competition of computational strategies and analyzing the central role of symmetries in an evolutionary system. In particular, we demonstrate how symmetry breaking can impede the evolution toward higher computational capability.Comment: 38 pages, compressed .ps files (780Kb) available ONLY thru anonymous ftp. (Instructions available via `get 9303003' .

A Complex-Systems Perspective on the “Computation vs. Dynamics” Debate in Cognitive Science

I review the purported opposition between computational and dynamical approaches in cognitive science. I argue that both computational and dynamical notions will be necessary for a full explanatory account of cognition, and give a perspective on how recent research in complex systems can lead to a much needed rapprochement between computational and dynamical styles of explanation

Biological Computation

In this article, the term biological computation refers to the proposal that living organisms themselves perform computations, and, more specifically, that the abstract ideas of information and computation may be key to understanding biology in a more unified manner. It is important to point out that the study of biological computation is typically not the focus of the field of computational biology, which applies computing tools to the solution of specific biological problems. Likewise, biological computation is distinct from the field of biologically-inspired computing, which borrows ideas from biological systems such as the brain, insect colonies, and the immune system in order to develop new algorithms for specific computer science applications. While there is some overlap among these different meldings of biology and computer science, it is only the study of biological computation that asks, specifically, if, how, and why living systems can be viewed as fundamentally computational in nature

Active Object Localization in Visual Situations

We describe a method for performing active localization of objects in instances of visual situations. A visual situation is an abstract concept---e.g., "a boxing match", "a birthday party", "walking the dog", "waiting for a bus"---whose image instantiations are linked more by their common spatial and semantic structure than by low-level visual similarity. Our system combines given and learned knowledge of the structure of a particular situation, and adapts that knowledge to a new situation instance as it actively searches for objects. More specifically, the system learns a set of probability distributions describing spatial and other relationships among relevant objects. The system uses those distributions to iteratively sample object proposals on a test image, but also continually uses information from those object proposals to adaptively modify the distributions based on what the system has detected. We test our approach's ability to efficiently localize objects, using a situation-specific image dataset created by our group. We compare the results with several baselines and variations on our method, and demonstrate the strong benefit of using situation knowledge and active context-driven localization. Finally, we contrast our method with several other approaches that use context as well as active search for object localization in images.Comment: 14 page

Sparse Coding on Stereo Video for Object Detection

Deep Convolutional Neural Networks (DCNN) require millions of labeled training examples for image classification and object detection tasks, which restrict these models to domains where such datasets are available. In this paper, we explore the use of unsupervised sparse coding applied to stereo-video data to help alleviate the need for large amounts of labeled data. We show that replacing a typical supervised convolutional layer with an unsupervised sparse-coding layer within a DCNN allows for better performance on a car detection task when only a limited number of labeled training examples is available. Furthermore, the network that incorporates sparse coding allows for more consistent performance over varying initializations and ordering of training examples when compared to a fully supervised DCNN. Finally, we compare activations between the unsupervised sparse-coding layer and the supervised convolutional layer, and show that the sparse representation exhibits an encoding that is depth selective, whereas encodings from the convolutional layer do not exhibit such selectivity. These result indicates promise for using unsupervised sparse-coding approaches in real-world computer vision tasks in domains with limited labeled training data

Cortisol reactivity to psychosocial stress is greater in sexual risk takers

Several studies have reported an association between deviant behaviour and cortisol reactivity to stress. However relatively few studies have investigated the relationship between psychobiological stress reactivity and sexual risk taking behaviours. In the present study, cortisol reactivity to the Trier Social Stress Test (TSST) was measured in 26 healthy young adults prior to the administration of a sexual health and behaviour questionnaire. The cortisol response to the TSST was greater in those individuals who reported that at least one of their previous two sexual partners was someone whom they had just met. Results are discussed in context of a model which suggests that early life stress dysregulates the hypothalamic-pituitary-adrenal (HPA) axis and increases the likelihood of later life risk taking behaviour. The findings have implications in terms of improving our understanding of psychobiological factors which predispose individuals to engage in adverse sexual health behaviours

Gaussian Processes with Context-Supported Priors for Active Object Localization

We devise an algorithm using a Bayesian optimization framework in conjunction with contextual visual data for the efficient localization of objects in still images. Recent research has demonstrated substantial progress in object localization and related tasks for computer vision. However, many current state-of-the-art object localization procedures still suffer from inaccuracy and inefficiency, in addition to failing to provide a principled and interpretable system amenable to high-level vision tasks. We address these issues with the current research. Our method encompasses an active search procedure that uses contextual data to generate initial bounding-box proposals for a target object. We train a convolutional neural network to approximate an offset distance from the target object. Next, we use a Gaussian Process to model this offset response signal over the search space of the target. We then employ a Bayesian active search for accurate localization of the target. In experiments, we compare our approach to a state-of-theart bounding-box regression method for a challenging pedestrian localization task. Our method exhibits a substantial improvement over this baseline regression method.Comment: 10 pages, 4 figure

An extension of the use of biodata for managerial selection

The aim of the research was to investigate whether a construct-oriented approach to biodata modelling provides incremental validity over and above other instruments currently employed in the selection of managers. This aim was explored through the development of construct oriented biodata analogues of the constructs of critical thinking ability, extroversion and neuroticism. These models were developed on a pilot sample of 'potential managerial candidates'. The pilot analogue models demonstrated impressive levels of construct validity and the biodata instrument was then validated in a concurrent study based upon managerial job incumbents. Supervisor ratings of performance and a career progress variable provided subjective and objective indicators of managerial performance. Although the psychological constructs of critical thinking ability, extroversion, and neuroticism did not significantly predict either outcome, further analysis of supervisor ratings revealed that perceived conscientiousness and energy contribute much of the variance associated with overall performance ratings, suggesting the likelihood of halo error in the ratings and offering grounds for a social psychological explanation of the results relating to this criterion. Regression analyses revealed that biodata analogue models of critical thinking ability, extroversion and neuroticism demonstrate incremental validity of construct-oriented biodata analogue models over traditional psychometric measures of these constructs. Construct- oriented biographical life history analogues may add considerable utility when used in the pre-selection stage of managerial recruitment and selection