The Influence of Evolutionary Selection Schemes on the Iterated Prisoner's Dilemma

Many economic and social systems are essentially large multi-agent systems.By means of computational modeling, the complicated behavior of such systemscan be investigated. Modeling a multi-agent system as an evolutionary agentsystem, several important choices have to be made for evolutionary operators.Especially, it is to be expected that evolutionary dynamics substantiallydepend on the selection scheme. We therefore investigate the influence ofevolutionary selection mechanisms on a fundamental problem: the iteratedprisoner's dilemma (IPD), an elegant model for the emergence of cooperationin a multi-agent system. We observe various types of behavior, cooperationlevel, and stability, depending on the selection mechanism and the selectionintensity. Hence, our results are important for (1) the proper choice andapplication of selection schemes when modeling real economic situations and(2) assessing the validity of the conclusions drawn from computer experimentswith these models. We also conclude that the role of selection in theevolution of multi-agent systems should be investigated further, for instanceusing more detailed and complex agent interaction models.     

Shape Morphing-Based Control of Robotic Visual Servoing

We present an approach for controlling robotic interactions with objects, using synthetic images generated by morphing shapes. In particular, we attempt the problem of positioning an eye-in-hand robotic system with respect to objects in the workspace for grasping and manipulation. In our formulation, the grasp position (and consequently the approach trajectory of the manipulator), varies with each object. The proposed solution to the problem consists of two parts. First, based on a model-based object recognition framework, images of the objects taken at the desired grasp pose are stored in a database. The recognition and identification of the grasp position for an unknown input object (selected from the family of recognizable objects) occurs by morphing its contour to the templates in the database and using the virtual energy spent during the morph as a dissimilarity measure. In the second step, the images synthesized during the morph are used to guide the eye-in-hand system and execute the grasp. The proposed method requires minimal calibration of the system. Furthermore, it conjoins techniques from shape recognition, computer graphics, and vision-based robot control in a unified engineering amework. Potential applications range from recognition and positioning with respect to partially-occluded or deformable objects to planning robotic grasping based on human demonstration.     

Approximation algorithm for hotlink assignment in the greedy model

Link-based information structures such as the web can be enhanced through the addition of hotlinks. Assume that each node in the information structure is associated with a weight representing the access frequency of the node by users. In order to access a particular node, the user must follow a path leading to it from the root. By adding new hotlinks to the tree, it may be possible to reduce the access cost of the system, namely, the expected number of steps needed to reach a leaf from the root, assuming the user can decide which hotlinks to follow in each step. The hotlink assignment   problem involves finding a set of hotlinks (with at most K=O(1)$K=O\left(1\right)$ hotlinks emanating from every node) maximizing the gain in the expected cost. The paper addresses this problem in two user models, namely, the traditional clairvoyant user model employed in [P. Bose, J. Czyzowicz, L. Gasieniec, E. Kranakis, D. Krizanc, A. Pelc, M.V. Martin, Strategies for hotlink assignments, in: Proc. 11th Symp. on Algorithms and Computation, 2000, pp. 23–34; E. Kranakis, D. Krizanc, S. Shende, Approximating hotlink assignments, in: Proc. 12th Symp. on Algorithms and Computation, 2001, pp. 756–767; P. Bose, D. Krizanc, S. Langerman, P. Morin, Asymmetrical communication protocols via hotlink assignments, in: Proc. 9th Colloq. on Structural Information and Communication Complexity, 2002, pp. 33–39; R. Matichin, D. Peleg, Approximation algorithm for hotlink assignments in web directories, in: Proc. Workshop on Algorithms and Data Structures, 2003, pp. 271–280] and the more realistic greedy user model recently introduced in [O. Gerstel, S. Kutten, R. Matichin, D. Peleg, Hotlink enhancement algorithms for web directories, in: Proc. 14th Symp. on Algorithms and Computation, 2003, pp. 68–77], and presents a polynomial time 2-approximation algorithm for the hotlink assignment problem on rooted directed trees.     

Context learning in the rodent hippocampus

We present a Bayesian statistical theory of context learning in the rodent hippocampus. While context is often defined in an experimental setting in relation to specific background cues or task demands, we advance a single, more general notion of context that suffices for a variety of learning phenomena. Specifically, a context is defined as a statistically stationary distribution of experiences, and context learning is defined as the problem of how to form contexts out of groups of experiences that cluster together in time. The challenge of context learning is solving the model selection problem: How many contexts make up the rodent's world? Solving this problem requires balancing two opposing goals: minimize the variability of the distribution of experiences within a context and minimize the likelihood of transitioning between contexts. The theory provides an understanding of why hippocampal place cell remapping sometimes develops gradually over many days of experience and why even consistent landmark differences may need to be relearned after other environmental changes. The theory provides an explanation for progressive performance improvements in serial reversal learning, based on a clear dissociation between the incremental process of context learning and the relatively abrupt context selection process. The impact of partial reinforcement on reversal learning is also addressed. Finally, the theory explains why alternating sequence learning does not consistently result in unique context-dependent sequence representations in hippocampus.     

The relation between color discrimination and color constancy: when is optimal adaptation task dependent?

Color vision supports two distinct visual functions: discrimination and constancy. Discrimination requires that the visual response to distinct objects within a scene be different. Constancy requires that the visual response to any object be the same across scenes. Across changes in scene, adaptation can improve discrimination by optimizing the use of the available response range. Similarly, adaptation can improve constancy by stabilizing the visual response to any fixed object across changes in illumination. Can common mechanisms of adaptation achieve these two goals simultaneously? We develop a theoretical framework for answering this question and present several example calculations. In the examples studied, the answer is largely yes when the change of scene consists of a change in illumination and considerably less so when the change of scene consists of a change in the statistical ensemble of surface reflectances in the environment.     

Efficient computation based on stochastic spikes

The speed and reliability of mammalian perception indicate that cortical computations can rely on very few action potentials per involved neuron. Together with the stochasticity of single-spike events in cortex, this appears to imply that large populations of redundant neurons are needed for rapid computations with action potentials. Here we demonstrate that very fast and precise computations can be realized also in small networks of stochastically spiking neurons. We present a generative network model for which we derive biologically plausible algorithms that perform spike-by-spike updates of the neuron's internal states and adaptation of its synaptic weights from maximizing the likelihood of the observed spike patterns. Paradigmatic computational tasks demonstrate the online performance and learning efficiency of our framework. The potential relevance of our approach as a model for cortical computation is discussed.     

Scarecrow: If I only had AI

At the 1992 AAAI robot contest one of the top finishers was Scarecrow—a robot that had no computer in the traditional sense, was built out of less than $200 of parts, and was explained and operated by a five year old. The designers sought to demonstrate the capabilities and competence that can be accomplished by using a strictly reactive architecture for well defined tasks such as that contest. This paper reexamines the Scarecrow robot and puts it into historical context. With fifteen years of perspective, we can also see what Scarecrow has to say about the perception of intelligence.

Arthrodial joint markerless cross-parameterization and biomechanical visualization

Orthopedists invest significant amounts of effort and time trying to understand the biomechanics of arthrodial (gliding) joints. Although new image acquisition and processing methods currently generate richer-than-ever geometry and kinematic data sets that are individual specific, the computational and visualization tools needed to enable the comparative analysis and exploration of these data sets lag behind. In this paper, we present a framework that enables the cross-data-set visual exploration and analysis of arthrodial joint biomechanics. Central to our approach is a computer-vision-inspired markerless method for establishing pairwise correspondences between individual-specific geometry. Manifold models are subsequently defined and deformed from one individual-specific geometry to another such that the markerless correspondences are preserved while minimizing model distortion. The resulting mutually consistent parameterization and visualization allow the users to explore the similarities and differences between two data sets and to define meaningful quantitative measures. We present two applications of this framework to human-wrist data: articular cartilage transfer from cadaver data to in vivo data and cross-data-set kinematics analysis. The method allows our users to combine complementary geometries acquired through different modalities and thus overcome current imaging limitations. The results demonstrate that the technique is useful in the study of normal and injured anatomy and kinematics of arthrodial joints. In principle, the pairwise cross-parameterization method applies to all spherical topology data from the same class and should be particularly beneficial in instances where identifying salient object features is a nontrivial task.     

Generalization in the XCSF classifier system: analysis, improvement, and extension

We analyze generalization in XCSF and introduce three improvements. We begin by showing that the types of generalizations evolved by XCSF can be influenced by the input range. To explain these results we present a theoretical analysis of the convergence of classifier weights in XCSF which highlights a broader issue. In XCSF, because of the mathematical properties of the Widrow-Hoff update, the convergence of classifier weights in a given subspace can be slow when the spread of the eigenvalues of the autocorrelation matrix associated with each classifier is large. As a major consequence, the system's accuracy pressure may act before classifier weights are adequately updated, so that XCSF may evolve piecewise constant approximations, instead of the intended, and more efficient, piecewise linear ones. We propose three different ways to update classifier weights in XCSF so as to increase the generalization capabilities of XCSF: one based on a condition-based normalization of the inputs, one based on linear least squares, and one based on the recursive version of linear least squares. Through a series of experiments we show that while all three approaches significantly improve XCSF, least squares approaches appear to be best performing and most robust. Finally we show how XCSF can be extended to include polynomial approximations.     

A quantum chemical study of the mechanism of action of Vitamin K epoxide reductase (VKOR) II. Transition states

A reaction path including transition states is generated for the Silverman mechanism [R.B. Silverman, Chemical model studies for the mechanism of Vitamin K epoxide reductase, J. Am. Chem. Soc. 103 (1981) 5939-5941] of action for Vitamin K epoxide reductase (VKOR) using quantum mechanical methods (B3LYP/6-311G**). VKOR, an essential enzyme in mammalian systems, acts to convert Vitamin K epoxide, formed by Vitamin K carboxylase, to its (initial) quinone form for cellular reuse. This study elaborates on a prior work that focused on the thermodynamics of VKOR [D.W. Deerfield II, C.H. Davis, T. Wymore, D.W. Stafford, L.G. Pedersen, Int. J. Quant. Chem. 106 (2006) 2944-2952]. The geometries of proposed model intermediates and transition states in the mechanism are energy optimized. We find that once a key disulfide bond is broken, the reaction proceeds largely downhill. An important step in the conversion of the epoxide back to the quinone form involves initial protonation of the epoxide oxygen. We find that the source of this proton is likely a free mercapto group rather than a water molecule. The results are consistent with the current view that the widely used drug Warfarin likely acts by blocking binding of Vitamin K at the VKOR active site and thereby effectively blocking the initiating step. These results will be useful for designing more complete QM/MM studies of the enzymatic pathway once three-dimensional structural data is determined and available for VKOR.