Boids Building autonomous agents to simulate group motion and obstacle avoidance such as activities of bird flocks and schools of fish. Excalibur This project develops a generic architecture for a group of agents to pursue their given goals, adapt their behavior to new environments, and communicate and perform coordinated group actions. Artificial Life Interactive Video Environment (MIT) The Artificial Life Interactive Video Environment (ALIVE) is virtual reality system where people can interact with virtual creatures without being constrained by headsets, goggles, or special sensing equipment. The system is based on a magic mirror metaphore: a person in the ALIVE space sees their own image in a large-screen TV as if in a mirror. Autonomous, animated characters join the user's own image in the reflected world.

The application of Evolutionary Computation (EC) techniques for the development of creative systems is a new, exciting and significant area of research. There is a growing interest in the application of these techniques in fields such as: art and music generation, analysis and interpretation; architecture; and design. EvoMUSART 2006 is the third workshop of the EvoNet working group on Evolutionary Music and Art. Following the success of previous events, the main goal of EvoMUSART 2006 is to bring together researchers who are using Evolutionary Computation in this context, providing the opportunity to promote, present and discuss ongoing work in the area. The workshop will include an open panel for the discussion of the most relevant questions of the field.

Hugo Liu and Push Singh.

This issue of AI Magazine brings together a collection of articles on challenges, mechanisms, and research progress in turn-taking and coordination between humans and machines.

This issue of AI Magazine brings together a collection of articles on challenges, mechanisms, and research progress in turn-taking and coordination between humans and machines. The contributing authors work in interrelated fields of spoken dialog systems, intelligent virtual agents, human-computer interaction, human-robot interaction, and semiautonomous collaborative systems and explore core concepts in coordinating speech and actions with virtual agents, robots, and other autonomous systems. Several of the contributors participated in the AAAI Spring Symposium on Turn-Taking and Coordination in Human-Machine Interaction, held in March 2015, and several articles in this issue are extensions of work presented at that symposium. The articles in the collection address key modeling, methodological, and computational challenges in achieving effective coordination with machines, propose solutions that overcome these challenges under sensory, cognitive, and resource restrictions, and illustrate how such solutions can facilitate coordination across diverse and challenging domains. The contributions highlight turn-taking and coordination in human-machine interaction as an emerging and evolving research area with important implications for future applications of AI.

Efficient Inspired Cognitive Architectures Society.

Embedding making, political analysis, and intelligence analysis; morality when handling preferences and dealing models of biomedical argumentation in research journals with the potential and risks of big data were identified and popular media; annotation of rhetorical figures; as challenging endeavors for the future.

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, presented the 2016 Spring Symposium Series on Monday through Wednesday, March 21-23, 2016 at Stanford University. The titles of the seven symposia were (1) AI and the Mitigation of Human Error: Anomalies, Team Metrics and Thermodynamics; (2) Challenges and Opportunities in Multiagent Learning for the Real World (3) Enabling Computing Research in Socially Intelligent Human-Robot Interaction: A Community-Driven Modular Research Platform; (4) Ethical and Moral Considerations in Non-Human Agents; (5) Intelligent Systems for Supporting Distributed Human Teamwork; (6) Observational Studies through Social Media and Other Human-Generated Content, and (7) Well-Being Computing: AI Meets Health and Happiness Science.

When the underlying assumptions of commonality of purpose and content break down, the interaction does as well. A great deal of the art of interaction design lies in minimizing what is, from the agent's point of view, out-of-task behavior, both by anticipating natural intask communication and by providing cues to lead participants down the predicted paths. Anticipation and cueing are particularly important in designing interactions for young children, a population that is limited in its ability to understand and adapt to the bounds of a system when things go awry. Most speech and natural language research that focuses on this population has pedagogy (Ogan et al. 2012; Gordon and Breazeal 2015) or therapy As explained briefly by Edith, there are two main game actions: effecting a change to the model by naming one of the clothing items or accessories on the board, and requesting a picture of the increasingly crazily clad model to be printed and taken home afterward. The majority of the interaction consists of 20 choice cycles during each of which a valid reference to a board item is made, the model changes, and a replacement item appears.

How should one aggregate ordinal preferences expressed by voters into a measurably superior social choice? A well-established approach -- which we refer to as implicit utilitarian voting -- assumes that voters have latent utility functions that induce the reported rankings, and seeks voting rules that approximately maximize utilitarian social welfare. We extend this approach to the design of rules that select a subset of alternatives. We derive analytical bounds on the performance of optimal (deterministic as well as randomized) rules in terms of two measures, distortion and regret. Empirical results show that regret-based rules are more compelling than distortion-based rules, leading us to focus on developing a scalable implementation for the optimal (deterministic) regret-based rule. Our methods underlie the design and implementation of RoboVote.org,