While current techniques for human-robot interaction modeling are typically limited to restrictive command-control style, traditional dialog modeling approaches are not directly applicable to robotics due to the lack of real-world integration. We present an approach that combines insights from dialog modeling with software-engineering demands that arise in robotics system research to provide a generalizable framework that can easily be applied to new scenarios. This goal is achieved by defining interaction patterns that combine abstract task states (such as task accepted or failed) with robot dialog acts (such as assertion or apology). An evaluation of the usability for robotic experts and novices showed that both groups were able to program 3 out of 5 dialog patterns in one hour while showing a steep learning curve. We argue that the proposed approach allows for less restricted and more informative human-robot interactions.

Inconsistency in behaviors of virtual agents and robots, like that between utterance contents, utterance forms, and postures, has a possibility of influences into human impression, cognition, and memory, and as a result, may lead to inhibition of dialogues between humans and these artifacts. In order to discuss about this possibility and its implications on dialogue design, this paper introduces some case studies using simple animated characters and a small-sized humanoid robot in Japan.

In order to design robots suited to engage in cooperative manipulation tasks with humans, we study human-human teams as they work together to move a heavy object across a room. We are interested in several questions. First, do two person, gross motion tasks follow the same sinusoidal pattern, one person fine motion tasks do? Does performance improve when audio or visual communication is permitted? How does performance correlate with an individual's perception of performance? Non-physiological, or performance based, studies of human-human cooperation can be divided into two categories: Haptic and Non-Haptic (audio, visual, etc). The first category, involves physical interaction through the object being manipulated via force, pressure, and tactile sensations (Jones and Sarter 2008), (Reed and Peshkin 2008). Most of the non-haptic experiments are virtual setups where individuals are moving an object together on a computer screen via two controllers (Basdogan, Ho, and Srinivasan 2000), (Sallnas and Zhai 2003). A survey on the role of communication between people appears in (Whitaker, 2003). The novelty of our work is to investigate non-haptic communication in haptic manipulation tasks.

Hutter (2007) recently introduced the loss rank principle (LoRP) as a generalpurpose principle for model selection. The LoRP enjoys many attractive properties and deserves further investigations. The LoRP has been well-studied for regression framework in Hutter and Tran (2010). In this paper, we study the LoRP for classification framework, and develop it further for model selection problems in unsupervised learning where the main interest is to describe the associations between input measurements, like cluster analysis or graphical modelling. Theoretical properties and simulation studies are presented.

Lasso and other regularization procedures are attractive methods for variable selection, subject to a proper choice of shrinkage parameter. Given a set of potential subsets produced by a regularization algorithm, a consistent model selection criterion is proposed to select the best one among this preselected set. The approach leads to a fast and efficient procedure for variable selection, especially in high-dimensional settings. Model selection consistency of the suggested criterion is proven when the number of covariates d is fixed. Simulation studies suggest that the criterion still enjoys model selection consistency when d is much larger than the sample size. The simulations also show that our approach for variable selection works surprisingly well in comparison with existing competitors. The method is also applied to a real data set.

But moreover, the implicit nature Metacognitive activity is a core aspect of many multifaceted of metacognitive activities makes the goal of supporting practices, but supporting such activity in educational contexts metacognition perhaps an even larger challenge. When we is a complex endeavor. One example of such a practice think about the two major learning goals described above includes the substantive inquiry practices that different in the science inquiry example and other learning goals educational policy groups (for example, National Research put forth in many educational policies, we can the central Council 2000) recommend for K-12 student curricula, including challenge that we want to address with metacognitive support: those practices that involve more authentic types of (1) supporting novice learners to mindfully engage in scientific inquiry along with online inquiry activities that incorporate the metacognitive activity necessary to successfully participate a growing number of digital libraries and other in complex, multifaceted practices, and (2) supporting information resources. There are many characterizations novice learners to learn good metacognitive practiceswhat of inquiry, but we can succinctly describe inquiry as a set metacognitive activities are, why they are important, and of activities that involve: (1) asking and developing questions how to engage in them. Supporting metacognition is vital to investigate; (2) searching for and gathering relevant to essentially help make these implicit activities more explicit data and information; (3) reading, evaluating, and analyzing to learners, yet we continue to see how difficult it is to the gathered data and information; and (4) synthesizing provide such support.

This paper is part of a larger project to determine how to build agent-based cognitive models capable of initial associative intelligence. Our method here is to take McClelland’s 1981 “Jets and Sharks” dataset and rebuild it using a nonlinear dynamic system with an eye toward determining which parameters are necessary to govern the interactivity of agents in a multi-agent cognitive system. A few number of parameters are suggested concerning diffusion and infusion values, which are basically elementary forms of information entropy, and multi-dimensional overlap from properties to objects and then from objects back to the properties that define them. While no agent-based model is presented, the success of the dynamic systems that are presented here suggest strong starting points for further research in building cognitive complex adaptive systems.

Links between licit and illicit economies fuel conflict in countries mired in irregular warfare. We argue that in Afghanistan, cultivating poppy and trading drugs bring stability to farmers who face the unintended consequences of haphazard development efforts while lacking alternative livelihoods and security necessary to access markets. Drug trafficking funds the crime-insurgency nexus and government corruption, in turn foiling attempts to establish a unified governance body. We show how individual rationality, market forces, corruption and opium stocks accumulated at different stages in the supply chain counteract the effects of poppy eradication. To that end, we use initial results from a multiagent model of the Afghan drug industry. We define physical, administrative, social and infrastructural environments in the simulation, and outline objectives and inputs for decision making and the structure of actor interactions.

The contribution of this paper is twofold. First, we present a new method for collective intention recognition based on mainstream philosophical accounts. Second, we extend our previous Elder Care system with collective intention recognition ability for assisting a couple of elderly people. The previous system was just capable of individual intention recognition, and so it has now been enabled to deal with situations where the elders intend to do things together.

We propose a simple model of network co-evolution in a game-dynamical system of interacting agents that play repeated games with their neighbors, and adapt their behaviors and network links based on the outcome of those games. The adaptation is achieved through a simple reinforcement learning scheme. We show that the collective evolution of such a system can be described by appropriately defined replicator dynamics equations. In particular, we suggest an appropriate factorization of the agents strategies thats results in a coupled system of equations characterizing the evolution of both strategies and network structure, and illustrate the framework on two simple examples.