Design issues of mobile haptic interfaces

As a novel approach to force‐reflecting telepresence, the concept of mobile haptic interfaces (MHI) is presented. An MHI actively follows the locomotion of an operator, who is no longer bound to be stationary during teleoperation. Thus, operator locomotion can be used as an input for locomotion control of a real teleoperator or control of locomotion in a virtual environment while keeping the advantage of force‐reflection. The article focuses on basic design issues and presents a prototype MHI for haptic exploration of extended virtual environments. © 2003 Wiley Periodicals, Inc.     

Using argument schemes for hypothetical reasoning in law

This paper studies the use of hypothetical and value-based reasoning in US Supreme-Court cases concerning the United States Fourth Amendment. Drawing upon formal AI & Law models of legal argument a semi-formal reconstruction is given of parts of the Carney case, which has been studied previously in AI & law research on case-based reasoning. As part of the reconstruction, a semi-formal proposal is made for extending the formal AI & Law models with forms of metalevel reasoning in several argument schemes. The result is compared with Rissland’s (1989) analysis in terms of dimensions and Ashley’s (2008) analysis in terms of his process model of legal argument with hypotheticals.     

Experiencing Nature: Embodying Animals in Immersive Virtual Environments Increases Inclusion of Nature in Self and Involvement With Nature

Immersive virtual environments (IVEs) produce simulations that mimic unmediated sensory experiences. 3 experiments (N = 228) tested how different modalities increase environmental involvement by allowing users to inhabit the body of animals in IVEs or watch the experience on video. Embodying sensory‐rich experiences of animals in IVEs led to greater feeling of embodiment, perception of being present in the virtual world, and interconnection between the self and nature compared to video. Heightened interconnection with nature elicited greater perceptions of imminence of the environmental risk and involvement with nature, which persisted for 1 week. Although the effect sizes were small to moderate, findings suggest that embodied experiences in IVEs may be an effective tool to promote involvement with environmental issues.     

Distributed, multi-sensor tracking of multiple participants within immersive environments using a 2-cohort camera setup

Vision-based human motion tracking has gained significant interest in recent years, as the need for more intuitive human–computer interaction paradigms are sought after. Immersive environments, such as large-scale displays and virtual reality systems are particularly suitable for such interaction mechanisms, as they provide a controlled basis for experimentation and evaluation. However, despite the growing interest, human-motion tracking within immersive environments is still in its infancy. The lack of visible light and the presence of multiple people within such environments pose several challenges to the problem. In this research a robust framework for real-time, multi-camera human-motion tracking within an immersive environment is presented. The static nature of the environment is exploited to utilise a novel 2-cohort camera layout. This layout proves to be highly useful for tracking, providing several benefits over existing techniques, which treat all cameras as the same.     

A note on the alternating group network

The class of alternating group networks was introduced in the late 1990’s as an alternative to the alternating group graphs as interconnection networks. Recently, additional properties for the alternating group networks have been published. In particular, Zhou et al., J. Supercomput (2009), doi:, was published very recently in this journal. We show that this so-called new interconnection topology is in fact isomorphic to the (n,n−2)-star, a member of the well-known (n,k)-stars, 1≤k≤n−1, a class of popular networks proposed earlier for which a large amount of work have already been done. Specifically, the problem in Zhou et al., J. Supercomput (2009), doi:, was addressed in Lin and Duh, Inf. Sci. 178(3), 788–801, 2008, when k = n−2.     

3D simulation as training tool in container terminals: The TRAINPORTS simulator

Modeling & Simulation (M&S) provides one of the best solutions for personnel and managers training in complex environments. In this article, the authors present an advanced High Level Architecture (HLA) federation of simulators (TRAINPORTS, TRAINing in marine PORTs by using Simulation), that recreates in a three-dimensional virtual environment the most important transshipment terminal of the South Mediterranean area, the Gioia Tauro container terminal. The TRAINPORTS federation includes four different federates (Straddle Carrier, Quay Crane, Forklift and Truck) and offers an advanced tool for marine port operators’ training providing the sensation of being in a real container terminal environment. The TRAINPORTS federation is part of an innovative family of simulators (called ST_VP, Simulation Team Virtual Port). This article presents the TRAINPORTS architecture, describes the federates/federation development process and discusses the simulators operation modes and verification and validation issues.     

How to address group dynamics in virtual worlds

The study of group dynamics highlights the activity in the group in terms of its performance and communication. The experience of facilitating virtual communities and teams (Eunice and Kimball in , 1997) suggests that groups go through the same stages either in face-to-face or in online mode. The paper brings together a theoretical framework based on the literature on virtual communities, Gestalt systems and online facilitation in order to address the issue of electronic togetherness, in particular from a group dynamics perspective. The empirical work on which the paper is based is an observation of a group of students in a training set playing a decision-making game. The model of Tuckman (Tuckman in Psychol Bull 63:384–399, 1965; Tuckman and Jensen in Group Organ Stud 2:419–427, 1977) is used as a framework within which to discuss the findings of the case. The paper finishes with concrete recommendations for facilitators of online communities and designers of the electronic spaces where these communities operate.     

Learning to search: Functional gradient techniques for imitation learning

Programming robot behavior remains a challenging task. While it is often easy to abstractly define or even demonstrate a desired behavior, designing a controller that embodies the same behavior is difficult, time consuming, and ultimately expensive. The machine learning paradigm offers the promise of enabling “programming by demonstration” for developing high-performance robotic systems. Unfortunately, many “behavioral cloning” (Bain and Sammut in Machine intelligence agents. London: Oxford University Press, 1995; Pomerleau in Advances in neural information processing systems 1, 1989; LeCun et al. in Advances in neural information processing systems 18, 2006) approaches that utilize classical tools of supervised learning (e.g. decision trees, neural networks, or support vector machines) do not fit the needs of modern robotic systems. These systems are often built atop sophisticated planning algorithms that efficiently reason far into the future; consequently, ignoring these planning algorithms in lieu of a supervised learning approach often leads to myopic and poor-quality robot performance. While planning algorithms have shown success in many real-world applications ranging from legged locomotion (Chestnutt et al. in Proceedings of the IEEE-RAS international conference on humanoid robots, 2003) to outdoor unstructured navigation (Kelly et al. in Proceedings of the international symposium on experimental robotics (ISER), 2004; Stentz et al. in AUVSI’s unmanned systems, 2007), such algorithms rely on fully specified cost functions that map sensor readings and environment models to quantifiable costs. Such cost functions are usually manually designed and programmed. Recently, a set of techniques has been developed that explore learning these functions from expert human demonstration. These algorithms apply an inverse optimal control approach to find a cost function for which planned behavior mimics an expert’s demonstration. The work we present extends the Maximum Margin Planning (MMP) (Ratliff et al. in Twenty second international conference on machine learning (ICML06), 2006a) framework to admit learning of more powerful, non-linear cost functions. These algorithms, known collectively as LEARCH (LEArning to seaRCH), are simpler to implement than most existing methods, more efficient than previous attempts at non-linearization (Ratliff et al. in NIPS, 2006b), more naturally satisfy common constraints on the cost function, and better represent our prior beliefs about the function’s form. We derive and discuss the framework both mathematically and intuitively, and demonstrate practical real-world performance with three applied case-studies including legged locomotion, grasp planning, and autonomous outdoor unstructured navigation. The latter study includes hundreds of kilometers of autonomous traversal through complex natural environments. These case-studies address key challenges in applying the algorithm in practical settings that utilize state-of-the-art planners, and which may be constrained by efficiency requirements and imperfect expert demonstration.

The relationship between individual characteristics and experienced presence

People experience different levels of presence (e.g.  and ), and different levels of cybersickness (Sharples, Cobb, Moody, & Wilson, 2008) even though they are immersed in the same virtual environment setting. In the current study, we raised the question how differences in individual characteristics might relate to differences in sensed presence for a virtual environment related to public speaking. The individual characteristics included in the experiment were related to visual abilities, personality traits, cognitive styles, and demographic factors. We recruited 88 participants, who were first immersed in a non-stereoscopic neutral environment, and then in a public speaking world: once with stereoscopic rendering and once without stereoscopic rendering, in a counter-balanced order. The results showed that immersive tendency and monocular visual ability were significantly correlated with presence and these correlations were consistent among the three virtual environments. Immersive tendencies and its subscale “involvement” were also found to be significantly correlated with cybersickness in all three worlds. Screening people on these variables may help to recognize the users who are more likely to benefit from virtual reality applications and may help to reduce the number of dropouts during virtual reality exposure therapy.     

The effects of scent and game play experience on memory of a virtual environment

Scent has been well documented as having significant effects on emotion (Alaoui-Ismaili in Physiol Behav 62(4):713–720, 1997; Herz et al. in Motiv Emot 28(4):363–383, 2004), learning (Smith et al. in Percept Mot Skills 74(2):339–343, 1992; Morgan in Percept Mot Skills 83(3)(2):1227–1234, 1996), memory (Herz in Am J Psychol 110(4):489–505, 1997) and task performance (Barker et al. in Percept Mot Skills 97(3)(1):1007–1010, 2003). This paper describes an experiment in which environmentally appropriate scent was presented as an additional sensory modality consistent with other aspects of a virtual environment called DarkCon. Subjects’ game play habits were recorded as an additional factor for analysis. Subjects were randomly assigned to receive scent during the VE, and/or afterward during a task of recall of the environment. It was hypothesized that scent presentation during the VE would significantly improve recall, and that subjects who were presented with scent during the recall task, in addition to experiencing the scented VE, would perform the best on the recall task. Skin-conductance was a significant predictor of recall, over and above experimental groups. Finally, it was hypothesized that subjects’ game play habits would affect both their behavior in and recall of the environment. Results are encouraging to the use of scent in virtual environments, and directions for future research are discussed. The project described herein has been sponsored by the US Army Research, Development, and Engineering Command (RDECOM). Statements and opinions expressed do not necessarily reflect the position or the policy of the US Government; no official endorsement should be inferred.     

TextonBoost for Image Understanding: Multi-Class Object Recognition and Segmentation by Jointly Modeling Texture,Layout, and Context

This paper details a new approach for learning a discriminative model of object classes, incorporating texture, layout, and context information efficiently. The learned model is used for automatic visual understanding and semantic segmentation of photographs. Our discriminative model exploits texture-layout filters, novel features based on textons, which jointly model patterns of texture and their spatial layout. Unary classification and feature selection is achieved using shared boosting to give an efficient classifier which can be applied to a large number of classes. Accurate image segmentation is achieved by incorporating the unary classifier in a conditional random field, which (i) captures the spatial interactions between class labels of neighboring pixels, and (ii) improves the segmentation of specific object instances. Efficient training of the model on large datasets is achieved by exploiting both random feature selection and piecewise training methods. High classification and segmentation accuracy is demonstrated on four varied databases: (i) the MSRC 21-class database containing photographs of real objects viewed under general lighting conditions, poses and viewpoints, (ii) the 7-class Corel subset and (iii) the 7-class Sowerby database used in He et al. (Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 695–702, June 2004), and (iv) a set of video sequences of television shows. The proposed algorithm gives competitive and visually pleasing results for objects that are highly textured (grass, trees, etc.), highly structured (cars, faces, bicycles, airplanes, etc.), and even articulated (body, cow, etc.). J. Shotton is now working at Toshiba Corporate Research & Development Center, Kawasaki, Japan.     

The <Emphasis Type="Italic">USP Farmers</Emphasis> herding team

This paper describes the development steps and core ideas used by the USP Farmers herding team, that has participated in the 2010 edition of the Multi-Agent Programming Contest (MAPC 2010). This is the third year that the competitors must design a team of herding agents, whose global goal is to lead a maximum number of cows to their own corral. As this is a very complex task and requires coordination of the team, we have developed the individual agents using the Jason (Bordini et al. 2007) interpreter for AgentSpeak(L) (Rao 1996). Moreover, the coordination strategy was defined using the M\mathcal{M} OISE ⁺ (Hübner et al. 2002, 2007) organizational model. We have also used the idea of artifact (Ricci et al. 2007) to develop global services, available to all the agents. Moreover, it is clear that for this contest some pure procedural processing should be developed in a lower abstraction level (Hübner et al. 2008); therefore some calculation and pre-defined global decisions were implemented by Java classes.     

Crowd of Virtual Humans: a New Approach for Real Time Navigation in Complex and Structured Environments

The navigation activity is an every day practice for any human being capable of locomotion. Our objective in this work is to reproduce this crucial human activity inside virtual environments. Putting together the high complexity of a realistic environment such as a city, a big amount of virtual humans and the real‐time constraint requires to optimize each aspect of the animation process. In this paper, we present a suitable topological structuring of the geometric environment to allow fast path finding as well as an efficient reactive navigation algorithm for virtual humans evolving inside a crowd.     

Improved multi-processor scheduling for?flow time and?energy

Energy usage has been an important concern in recent research on online scheduling. In this paper, we study the tradeoff between flow time and energy (Albers and Fujiwara in ACM Trans. Algorithms 3(4), 2007; Bansal et al. in Proceedings of ACM-SIAM Symposium on Discrete Algorithms, pp. 805–813, 2007b, Bansal et al. in Proceedings of International Colloquium on Automata, Languages and Programming, pp. 409–420, 2008; Lam et al. in Proceedings of European Symposium on Algorithms, pp. 647–659, 2008b) in the multi-processor setting. Our main result is an enhanced analysis of a simple non-migratory online algorithm called CRR (classified round robin) on m≥2 processors, showing that its flow time plus energy is within O(1) times of the optimal non-migratory offline algorithm, when the maximum allowable speed is slightly relaxed. The result still holds even if the comparison is made against the optimal migratory offline algorithm. This improves previous analysis that CRR is O(log P)-competitive where P is the ratio of the maximum job size to the minimum job size.     

Reduced Basis Techniques for Stochastic Problems

We report here on the recent application of a now classical general reduction technique, the Reduced-Basis (RB) approach initiated by C. Prud’homme et al. in J. Fluids Eng. 124(1), 70–80, 2002, to the specific context of differential equations with random coefficients. After an elementary presentation of the approach, we review two contributions of the authors: in Comput. Methods Appl. Mech. Eng. 198(41–44), 3187–3206, 2009, which presents the application of the RB approach for the discretization of a simple second order elliptic equation supplied with a random boundary condition, and in Commun. Math. Sci., 2009, which uses a RB type approach to reduce the variance in the Monte-Carlo simulation of a stochastic differential equation. We conclude the review with some general comments and also discuss possible tracks for further research in the direction.     

Optimality conditions for multiple objective fractional subset programming with (ρ,σ,θ)-type-I and related non-convex functions

In this paper, we introduce a class of generalized invex n-set functions, called (ρ,σ,θ)-type-I and related non-convex functions, and then establish a number of parametric and semi-parametric sufficient optimality conditions for the primal problem under the aforesaid assumptions. This work partially extends an earlier work of Mishra et al. (Math. Methods Oper. Res. 67, 493–504, 2008) to a wider class of functions.     

Why Computers Can’t Feel Pain

The most cursory examination of the history of artificial intelligence highlights numerous egregious claims of its researchers, especially in relation to a populist form of ‘strong’ computationalism which holds that any suitably programmed computer instantiates genuine conscious mental states purely in virtue of carrying out a specific series of computations. The argument presented herein is a simple development of that originally presented in Putnam’s (Representation & Reality, Bradford Books, Cambridge in 1988) monograph, “Representation & Reality”, which if correct, has important implications for turing machine functionalism and the prospect of ‘conscious’ machines. In the paper, instead of seeking to develop Putnam’s claim that, “everything implements every finite state automata”, I will try to establish the weaker result that, “everything implements the specific machine Q on a particular input set (x)”. Then, equating Q (x) to any putative AI program, I will show that conceding the ‘strong AI’ thesis for Q (crediting it with mental states and consciousness) opens the door to a vicious form of panpsychism whereby all open systems, (e.g. grass, rocks etc.), must instantiate conscious experience and hence that disembodied minds lurk everywhere.     

A Unified Primal-Dual Algorithm Framework Based on Bregman Iteration

In this paper, we propose a unified primal-dual algorithm framework for two classes of problems that arise from various signal and image processing applications. We also show the connections to existing methods, in particular Bregman iteration (Osher et al., Multiscale Model. Simul. 4(2):460–489, 2005) based methods, such as linearized Bregman (Osher et al., Commun. Math. Sci. 8(1):93–111, 2010; Cai et al., SIAM J. Imag. Sci. 2(1):226–252, 2009, CAM Report 09-28, UCLA, March 2009; Yin, CAAM Report, Rice University, 2009) and split Bregman (Goldstein and Osher, SIAM J. Imag. Sci., 2, 2009). The convergence of the general algorithm framework is proved under mild assumptions. The applications to ℓ ¹ basis pursuit, TV−L ² minimization and matrix completion are demonstrated. Finally, the numerical examples show the algorithms proposed are easy to implement, efficient, stable and flexible enough to cover a wide variety of applications.     

Fast Evaluation of Interlace Polynomials on Graphs of Bounded Treewidth

We consider the multivariate interlace polynomial introduced by Courcelle (Electron. J. Comb. 15(1), 2008), which generalizes several interlace polynomials defined by Arratia, Bollobás, and Sorkin (J. Comb. Theory Ser. B 92(2):199–233, 2004) and by Aigner and van der Holst (Linear Algebra Appl., 2004). We present an algorithm to evaluate the multivariate interlace polynomial of a graph with n vertices given a tree decomposition of the graph of width k. The best previously known result (Courcelle, Electron. J. Comb. 15(1), 2008) employs a general logical framework and leads to an algorithm with running time f(k)⋅n, where f(k) is doubly exponential in k. Analyzing the GF(2)-rank of adjacency matrices in the context of tree decompositions, we give a faster and more direct algorithm. Our algorithm uses 2^3k2+O(k)·n2^{3k^{2}+O(k)}\cdot n arithmetic operations and can be efficiently implemented in parallel.     

On some applications of ant colony optimization metaheuristic to plane truss optimization

Ant colony optimization metaheuristic (ACO) represents a new class of algorithms particularly suited to solve real-world combinatorial optimization problems. ACO algorithms, published for the first time in 1991 by M. Dorigo [Optimization, learning and natural algorithms (in Italian). Ph.D. Thesis, Dipartimento di Elettronica, Politecnico di Milano, Milan, 1992] and his coworkers, have been applied, particularly starting from 1999 (Bonabeau et al., Swarm intelligence: from natural to artificial systems, Oxford University Press, New York, 1999; Dorigo et al., Artificial life 5(2):137–172, 1999; Dorigo and Di Caro, Ant colony optimization: a new metaheuristic, IEEE Press, Piscataway, NJ, 1999; Dorigo et al., Ant colony optimization and swarm intelligence, Springer, Berlin Heidelberg New York, 2004; Dorigo and Stutzle, Ant colony optimization, MIT Press, Cambridge, MA, 2004), to several kinds of optimization problems such as the traveling salesman problem, quadratic assignment problem, vehicle routing, sequential ordering, scheduling, graph coloring, management of communications networks, and so on. The ant colony optimization metaheuristic takes inspiration from the studies of real ant colonies’ foraging behavior. The main characteristic of such colonies is that individuals have no global knowledge of problem solving but communicate indirectly among themselves, depositing on the ground a chemical substance called pheromone, which influences probabilistically the choice of subsequent ants, which tend to follow paths where the pheromone concentration is higher. Such behavior, called stigmergy, is the basic mechanism that controls ant activity and permits them to take the shortest path connecting their nest to a food source. In this paper, it is shown how to convert natural ant behavior to algorithms able to escape from local minima and find global minimum solutions to constrained combinatorial problems. Some examples on plane trusses are also presented.