We use the methods of evolutionary game theory and computational modelling to examine the evolution of ethnocentrism. We show that ethnocentrism evolves in a spatially structured population not only under prisoner's dilemma interactions, but also hawk-dove, assurance, harmony, and leader games. In the case of harmony, ethnocentrism evolves even when defection is irrational. This suggests that the pressure of competing for a common resource (in our model: free space) can produce irrational hostility between groups. The minimal cognitive assumptions in our model also suggest that the ethnocentrism observed in humans and elsewhere in nature has an evolutionary basis that is robust over changes in interaction types.

Traditional dialogue management systems are tightly coupled with the sensing ability of a single computer. How to organize an interaction in pervasive environments to provide a friendly and integrated interface to users is an important issue. This requires a transition of the human-computer interaction (HCI) from tight coupling to loose coupling. This paper proposes a hierarchical multimodal framework for pervasive interactions. Our system is designed to remind the activities of daily living for individuals with cognitive impairments.The system is composed of Markov decision processes for activity planing, and multimodal partially observable Markov decision processes for action planning and executing. Empirical results demonstrate the hierarchical multimodal framework establishes a flexible mechanism for pervasive interaction systems.

This article introduces a "pseudo classical" notion of modelling non-separability. This form of non-separability can be viewed as lying between separability and quantum-like non-separability. Non-separability is formalized in terms of the non-factorizabilty of the underlying joint probability distribution. A decision criterium for determining the non-factorizability of the joint distribution is related to determining the rank of a matrix as well as another approach based on the chi-square-goodness-of-fit test. This pseudo-classical notion of non-separability is discussed in terms of quantum games and concept combinations in human cognition.

This position paper provides an overview of work conducted and an outlook of future directions within the field of Information Retrieval (IR) that aims to develop novel models, methods and frameworks inspired by Quantum Theory (QT).

STRIPS-style planning has proven to be a helpful methodology for narrative generation, but certain assumptions about the process remain in use which inhibit the creation of interesting stories. The sequence of actions is more important than the initial and goal state of the world, so a narrative planner should first build a plot and then adapt the world to that plot. This is possible by relaxing the closed world assumption to allow revision to the initial and goal states.

A peloton is a group of cyclists whose individual and collective energy expenditures are reduced when cyclists ride behind others in zones of reduced air pressure; this effect is known in cycling as ‘drafting’. Through drafting cyclists couple their energy expenditures. Coupling of cyclists’ energy expenditures when drafting is the basic peloton property from which self-organized collective behaviours emerge. Here we examine peloton hysteresis, applying the definition used in the context of vehicle traffic in which a rapid deceleration to a high density state (jam) is followed by a lag in vehicle acceleration. Applying a flow analysis of volume (number of cyclists) over time, peloton hysteresis is examined in three forms: one is similar to vehicle traffic hysteresis in which rapid decelerations and increased flow (or density) are followed by extended acceleration periods and reduced flow. In cycling this is known as the accordion effect. A second kind of hysteresis results from rapid accelerations followed by periods of decreasing speeds and decreasing flow. This form of hysteresis is essentially inverse to traffic hysteresis and the accordion effect. We show this form of hysteresis using data from a mass-start bicycle points-race. A third kind of peloton hysteresis occurs when the drafting benefit is minimized on hills and weaker cyclists lose positions in the peloton, while flow/density is retained. A computer simulation shows this hysteresis among two sets of cyclist agents, each with different output capacity and models hysteresis as a peloton transitions from flat topography to a steep incline on which drafting is negligible.

Studying how a robot can learn to communicate with a person provides insight into how communication might be learned in general. Deep models of dialog and communicative intent typically rely on modeling the internal state of the speakers—states that are unobservable by a learning robot. This paper considers how communication can be framed to be learnable from experience. In particular, we describe how an agent might learn to communicate by building on three foundational capabilities, namely 1) an observable signal of satisfied intent (a smile), 2) the ability to imitate perceived actions, and 3) perceptual referents for discourse items. Early simulation results show that an agent can learn some basic communication skills from these foundations.

Common sense collection has long been an important subfield of AI. This paper introduces a combined architecture for commonsense harvesting by text mining and a game with a purpose. The text miner module uses a seed set of known facts (sampled from ConceptNet) as training data and produces candidate commonsense facts mined from corpora. The game module taps humans' knowledge about the world by letting them play a simple slot-machine-like game. The proposed system allows us to collect significantly better commonsense facts than the state-of-the-art text miner alone, as shown experimentally for 5 rather different types of commonsense relations.

Successful learning with advanced learning technologies is based on the premise that learners adaptively regulate their cognitive and metacognitive behaviors during learning. However, there is abundant empirical evidence that suggests that learners typically do not adaptively modify their behavior, thus suggesting that they engage in what is called dysregulated behavior. Dysregulated learning is a new term that is used to describe a class of behaviors that learners use that lead to minimal learning. Examples of dysregulated learning include failures to: (1) encode contextual demands, (2) deploy effective learning strategies, (3) modify and update internal standards, (4) deal with the dynamic nature of the task, (5) metacognitive monitor the use of strategies and repeatedly make accurate metacognitive judgments, and (6) intelligently adapt behavior during learning so as to maximize learning and understanding of the instructional material. Understanding behaviors associated with dysregulated learning is critical since it has implications for determining what they are, when they occur, how often they occur, and how they can be corrected during learning.

A seminal study conducted by Greene, Bolick, and Robertson (2010) showed that learners do not always engage in appropriate metacognitive and self-regulatory processes while learning about history. However, little research exists to guide the design of technology-rich learning environments (TRLEs) as metacognitive tools in social sciences education. In order to address this issue, we designed a metacognitive tool using a bottom-up approach (Poitras, 2010; Poitras, Lajoie, & Hong, in prep). Thirty-two undergraduate students read an historical narrative text either with or without the benefit of the metacognitive tool. Results from process and product data suggest that learners had better recall because the metacognitive tool assisted learners to (a) notice that particular events are unexplained in the circumstances described in an historical narrative text, and (b) generate hypothetical causes to explain the occurrence of such events. We discuss the implications of these findings for the development of the MetaHistoReasoning Tool, a TRLE that assists learners’ historical reasoning while they accomplish authentic tasks of historical inquiry.