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.

Fast mapping is a phenomenon by which children learn the meanings of novel adjectives after a very small number of exposures when the new word is contrasted with a known word. The present study was a preliminary test of whether machine learners could use such contrasts in unconstrained speech to learn adjective meanings and categories. Six decision tree-based learning methods were evaluated that use contrasting examples in order to work toward an adjective fast-mapping system for machine learners. Subjects tended to compare objects using adjectives of the same category, implying that such contrasts may be a useful source of data about adjective meaning, though none of the learning algorithms showed strong advantages over any other.

We propose a computational model of joint attention consisting of three parts: responding to joint attention, initiating joint attention, and ensuring joint attention. This model is supported by psychological findings and matches the developmental timeline in humans. We present two experiments that test this model and investigate joint attention in human-robot interaction. The first experiment explored the effects of responding to joint attention on human-robot interaction. We show that robots responding to joint attention are more transparent to humans and are more competent and socially interactive. The second experiment studied the importance of ensuring joint attention in human-robot interaction. Data upheld our hypotheses that a robot's ensuring joint attention behavior yields better performance in human-robot interactive tasks and ensuring joint attention behaviors are perceived as natural behaviors.

The development of hand-crafted action and dialog generation models for a social robot is a time consuming process that yields a solution only for the relatively narrow range of interactions envisioned by the programmers. In this paper, we propose a data-driven solution for interactive behavior generation that leverages online games as a means of collecting large-scale data corpora for human-robot interaction research. We present a system in which action and dialog models for a collaborative human-robot task are learned based on a reproduction of the task in a two-player online game called Mars Escape.

The aim of this paper is to revisit the fundamental requirements for bulding computational models for Interactive Narrative. We express the need for broader computational models of narrative and underline the fundamental difference between models for story generation and models for Interactive Narrative. Research directions are finally sketched to move towards dedicated computational models for Interactive Narrative.

Many theories of discourse structure rely on the idea that the segments comprising the discourse are linked through inferred relations such as causality and temporal contiguity. These theories suggest that the resulting discourse is represented hierarchically. Two experiments examine some of the implications of these hierarchical structures on the perceived coherence of texts. Experiment 1 shows that texts with more levels to their hierarchical structure are judged to be more coherent. Experiment 2 demonstrates that these effects are sensitive to the genre of the text. Specifically, narratives seem to be more affected by manipulation of the discourse structure than procedural texts.

Computational Narrative has provided several examples of how to process narrations using semantical approaches. While many useful concepts for computational management of stories have been unveiled, a common barrier has hindered their development: semantic knowledge is still too complex to handle. In this paper, a focus shift based on narrative structure is proposed. Instead of digging deeper into the possibilities of semantic processing, analysing structural properties of stories and keeping the semantic load to a minimum can allow for a more efficient use of available narrative corpora, even without mimicking human behaviour.

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.

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.

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.