Robots are increasingly becoming key players in human-robot teams. To become effective teammates, robots must possess profound understanding of an environment, be able to reason about the desired commands and goals within a specific context, and be able to communicate with human teammates in a clear and natural way. To address these challenges, we have developed an intelligence architecture that combines cognitive components to carry out high-level cognitive tasks, semantic perception to label regions in the world, and a natural language component to reason about the command and its relationship to the objects in the world. This paper describes recent developments using this architecture on a fielded mobile robot platform operating in unknown urban environments. We report a summary of extensive outdoor experiments; the results suggest that a multidisciplinary approach to robotics has the potential to create competent human-robot teams.

Motion-adaptive radiotherapy techniques are promising to deliver truly ablative radiation doses to tumors with minimal normal tissue exposure by accounting for real-time tumor movement. However, a major challenge of successful applications of these techniques is the real-time prediction of breathing-induced tumor motion to accommodate system delivery latencies. Predicting respiratory motion in real-time is challenging. The current respiratory motion prediction approaches are still not satisfactory in terms of accuracy and interpretability due to the complexity of breathing patterns and the high inter-individual variability across patients. In this paper, we propose a novel respiratory motion prediction framework which integrates four key components: a personalized monitoring window generator, an orthogonal polynomial approximation-based pattern library builder, a variant best neighbor pattern searcher, and a statistical prediction decision maker. The four functional components work together into a real-time prediction system and is capable of performing personalized tumor position prediction during radiotherapy. Based on a study of respiratory motion of 27 patients with lung cancer, the proposed prediction approach generated consistently better prediction performances than the current respiratory motion prediction approaches, particularly for long prediction horizons.

When two or more self-interested agents put their plans to execution in the same environment, conflicts may arise as a consequence, for instance, of a common utilization of resources. In this case, an agent can postpone the execution of a particular action, if this punctually solves the conflict, or it can resort to execute a different plan if the agent's payoff significantly diminishes due to the action deferral. In this paper, we present a game-theoretic approach to non-cooperative planning that helps predict before execution what plan schedules agents will adopt so that the set of strategies of all agents constitute a Nash equilibrium. We perform some experiments and discuss the solutions obtained with our game-theoretical approach, analyzing how the conflicts between the plans determine the strategic behavior of the agents.

Effective tutoring requires personalization of the interaction to each student.Continuous and efficient assessment of the student's skills are a prerequisite for such personalization.We developed a Bayesian active-learning algorithm that continuously and efficiently assesses a child's word-reading skills and implemented it in a social robot.We then developed an integrated experimental paradigm in which a child plays a novel story-creation tablet game with the robot.The robot is portrayed as a younger peer who wishes to learn to read, framing the assessment of the child's word-reading skills as well as empowering the child.We show that our algorithm results in an accurate representation of the child's word-reading skills for a large age range, 4-8 year old children, and large initial reading skill range.We also show that employing child-specific assessment-based tutoring results in an age- and initial reading skill-independent learning, compared to random tutoring.Finally, our integrated system enables us to show that implementing the same learning algorithm on the robot's reading skills results in knowledge that is comparable to what the child thinks the robot has learned.The child's perception of the robot's knowledge is age-dependent and may facilitate an indirect assessment of the development of theory-of-mind.

Emotion affects our understanding of the opinions and sentiments of others. Research has demonstrated that humans are able to recognize emotions in various domains, including speech and music, and that there are potential shared features that shape the emotion in both domains. In this paper, we investigate acoustic and visual features that are relevant to emotion perception in the domains of singing and speaking. We train regression models using two paradigms: (1) within-domain, in which models are trained and tested on the same domain and (2) cross-domain, in which models are trained on one domain and tested on the other domain. This strategy allows us to analyze the similarities and differences underlying the relationship between audio-visual feature expression and emotion perception and how this relationship is affected by domain of expression. We use kernel density estimation to model emotion as a probability distribution over the perception associated with multiple evaluators on the valence-activation space. This allows us to model the variation inherent in the reported perception. Results suggest that activation can be modeled more accurately across domains, compared to valence. Furthermore, visual features capture cross-domain emotion more accurately than acoustic features. The results provide additional evidence for a shared mechanism underlying spoken and sung emotion perception.

Argumentative discussion is a highly demanding task. In order to help people in such situations, this paper provides an innovative methodology for developing an agent that can support people in argumentative discussions by proposing possible arguments to them. By analyzing more than 130 human discussions and 140 questionnaires, answered by people, we show that the well-established Argumentation Theory is not a good predictor of people's choice of arguments. Then, we present a model that has 76% accuracy when predicting people’s top three argument choices given a partial deliberation. We present the Predictive and Relevance based Heuristic agent (PRH), which uses this model with a heuristic that estimates the relevance of possible arguments to the last argument given in order to propose possible arguments. Through extensive human studies with over 200 human subjects, we show that people’s satisfaction from the PRH agent is significantly higher than from other agents that propose arguments based on Argumentation Theory, predict arguments without the heuristics or only the heuristics. People also use the PRH agent's proposed arguments significantly more often than those proposed by the other agents.

Recent studies have suggested that current agent-based models are not sufficiently sophisticated to reproduce results achieved by human collaborative learning and reasoning. Such studies suggest that humans are diverse and dynamic when solving problems socially. However, despite their relevance to problem-solving, these two behavioral features have not yet been fully investigated. In this paper we analyse a recent social problem-solving model and attempt to address its shortcomings. Specifically, we investigate the effects of separating exploitation from exploration in agent behaviors and explore the concept of diversity in such models. We found out that diverse populations outperform homogeneous ones in both efficient and inefficient networks. Finally, we show that agent diversity is more relevant than the strategic behavioral dynamics. This work contributes towards understanding the role of diverse and dynamic behaviors in social problem-solving as well as the advancement of state-of-art social problem-solving models.

In a basic economic system, each participant receives a (financial) reward according to his own contribution to the system. In this work, we study an alternative approach — Incentive Networks — in which a participant's reward depends not only on his own contribution; but also in part on the contributions made by his social contacts or friends. We show that the key parameter effecting the efficiency of such an Incentive Network-based economic system depends on the participant's degree of directed altruism. Directed altruism is the extent to which someone is willing to work if his work results in a payment to his friend, rather than to himself. Specifically, we characterize the condition under which an Incentive Network-based economy is more efficient than the basic "pay-for-your-contribution" economy. We quantify by how much incentive networks can reduce the total reward that needs to be paid to the participants in order to achieve a certain overall contribution. Finally, we study the impact of the network topology and various exogenous parameters on the efficiency of incentive networks. Our results suggest that in many practical settings, Incentive Network-based reward systems or compensation structures could be more efficient than the ubiquitous 'pay-for-your-contribution' schemes.

Motivated by current day crowdsourcing platforms and emergence of online labor markets, this work addresses the problem of task allocation and payment decisions when unreliable and strategic workers arrive over time to work on tasks which must be completed within a deadline. We consider the following scenario: a requester has a set of tasks that must be completed before a deadline; agents (aka crowd workers) arrive over time and it is required to make sequential decisions regarding task allocation and pricing. Agents may have different costs for providing service and these costs are private information of the agents. We assume that agents are not strategic about their arrival times but could be strategic about their costs of service. In addition, agents could be unreliable in the sense of not being able to complete the assigned tasks within the allocated time; these tasks must then be reallocated to other agents to ensure ontime completion of the set of tasks by the deadline. For this setting, we propose two mechanisms: a DPM (DynamicPrice Mechanism) and an ABM (Auction Based Mechanism). Both mechanisms are dominant strategy incentive compatible, budget feasible, and also satisfy ex-post individual rationality for agents who complete the allocated tasks. These mechanisms can be implemented in current day crowdsourcing platforms with minimal changes to the current interaction model.

Temperature Discovery Search (TDS) is a forward search method for computing or approximating the temperature of a combinatorial game. Temperature and mean are important concepts in combinatorial game theory, which can be used to develop efficient algorithms for playing well in a sum of subgames. A new algorithm TDS+ with five enhancements of TDS is developed, which greatly speeds up both exact and approximate versions of TDS. Means and temperatures can be computed faster, and fixed-time approximations which are important for practical play can be computed with higher accuracy than before.