The better the team mental model, the better the teamwork. An important aspect of what determines a good team model is the extent to which the model is shared by the team members. This paper presents suggestions for measuring the extent to which teams have a shared mental model and describes how these measures are related to team performance. The most promising measures of sharedness proposed so far rely on using a compositional approach for team modeling and on a situation-sensitive relevance relation that indicates to what extent components contribute to team performance. A case study illustrates the approach and initial results on measuring performance when teams use different levels of sharedness.

This paper is about generating questions in human-robot interaction. We survey existing work on the forms and meanings of questions in English and discuss the pragmatic effects resulting from an interplay between the choice of syntactic form and intonation. We propose an approach to formalization based on a notion of common ground and commitment, set in a model of situated dialogue as part of collaborative activity where we explicitly model the beliefs and intentions of both the robot and the human. Questions come about by abductively inferring an intentional structure grounded in the belief model and indicating commitments. Content planning and surface realization turn this into a question of the appropriate form.

In this paper we develop a model to simulate the evolution of a pollution-free resource gathering technology that is initially less efficient but ultimately reaches parity with polluting technology. We find that for low levels of pollution, pressure exerted by society can indeed encourage the development and use of non-polluting technology, with greater pressure being associated with faster achievement of efficiency parity and lower overall pollution. However, greater pressure is also associated with lower populations and at the highest levels of pressure there are significant risks of population crashes. We find that these results hold for both localized pollution and globalized pollution, with globalized pollution encouraging faster achievement of efficiency parity. For high levels of pollution we find that introducing societal pressure significantly increases the occurrence of population crashes, and thus the strategy is only effective under certain conditions.

In this paper we address the design of robots that can be successful partners to humans in joint activity. The paper outlines an approach to achieving adjustable autonomy during execution- and hence to achieve resilient multi-actor joint action - based on both temporal and epistemic situation projection. The approach is based on non-deterministic planning techniques based on the situations calculus.

In this paper, we consider the issue of guiding ethical behavior in human-robot teams from a systemic viewpoint. Considering a team as a sociotechnical complex, we look at how responsibility for actions can arise through the interaction between the different actors in the team while playing specific roles. We define the notions of role, discuss how they establish a social network, and then use logical notions of multi-agent trust to formalize responsibility as accountability against capabilities that are invoked during collaboration.

In this paper, we address the problem of computing the policy of an autonomous agent, taking human recommendations into account which could be appropriate for mixed initiative, or adjustable autonomy. For this purpose, we present Goal Biased Markov Decision Process (GBMDP) which assume two kinds of recommendation. The human recommends to the agent to avoid some situations (represented by undesirable states), or he recommends favorable situations represented by desirable states. The agent takes those recommendations into account by updating its policy (only updating the states concerned by the recommendations, not the whole policy). We show that GBMDP is efficient and it improves the human's intervention by reducing its time of attention paid to the agent. Moreover, GBMDP optimizes robot's computation time by updating only the necessary states. We also show how GBMDP can consider more than one recommendation. Finally, our experiments show how we update policies which are intractable by standard approaches.

Given the challenges of modeling multi-scale social phenomena, hybrids may hold the key to unlocking social complexity dynamics. We introduce hybrid system modeling from engineering, as a means to capture complex dynamics within interacting, multi-scale, and global social systems. Whereby hybrid modeling is used in industrial processes and automated control systems, this research uses world cup soccer tournament simulations to demonstrate successful applications. Agent-based modeling for soccer games and cellular automatons for crowd and bettor emotional reactions are modeled on each side of a playing field. A predator-prey theoretical approach is applied with self-organizing soccer teams represented as predators and the soccer ball as prey. Simulations of multiple soccer tournaments of thirty-two teams were conducted with pre-game betting and without betting as a pseudo-control measure. Tournaments conducted with pre-game betting resulted in the final tournament games having the wining team demonstrating strong defensive playing styles and scoring by a large margin. Divergence of playing styles did not develop in tournaments without pre-game betting. Hybrids offer a means to explore complexity with evolutionary learning by players, corresponding emotional reactions of spectators, and betting interacting, resulting in patterns of emergent behavior and unique evolutionary behavioral responses to complexity.

In this paper we describe a complex adaptive systems model of interactions between coupled human and natural system. We use learning classifier systems to create adaptive agents in a simulation of the Maine lobster fishery to explore the relationships among ecological, economic, and social characteristics. Our hypothesis is that the cost of information and learning drives agents' decisions to compete or co-operate and, consequently, the emergence of long-term relationships. Initial results provide tentative support for the hypothesis and the ability of this model to provide insight into the dynamics of individual interactions and the social relationships that emerge from those interactions.

This research aims to define a novel framework to employ engineering and mathematical models to study adaptive dynamics in heterarchial systems. This multi-profile descriptive platform and modeling approach is developed as a composite of conceptual behaviors and structural entity aspects of engineered complex adaptive systems (ECAS). While the US electric power system will be utilized for demonstration and validation, the framework has applicability to the general class of ECASs that are artificially created but highly interactive with natural and behavioral sciences. Conditioned on parameterization of the framework, a theorem will be presented to calibrate current structure and predict future dynamic behaviors of an ECAS. We analyze decentralized heterarchial ECASs to infer emergent behavior of the components, and evolution processes and adaptations of the whole system.

Beyond belief change and meme adoption, both genetics and infection have been spoken of in terms of information transfer. What we examine here, concentrating on the specific case of transfer between sub-networks, are the differences in network dynamics in these cases: the different network dynamics of germs, genes, and memes. Germs and memes, it turns out, exhibit a very different dynamics across networks. For infection, measured in terms of time to total infection, it is network type rather than degree of linkage between sub-networks that is of primary importance. For belief transfer, measured in terms of time to consensus, it is degree of linkage rather than network type that is crucial. Genes model each of these other dynamics in part, but match neither in full. For genetics, like belief transfer and unlike infection, network type makes little difference. Like infection and unlike belief, on the other hand, the dynamics of genetic information transfer within single and between linked networks are much the same. In ways both surprising and intriguing, transfer of genetic information seems to be robust across network differences crucial for the other two.