Although real-world solutions for robotic demining have not as yet been demonstrated, multirobot systems will clearly yield significant benefits over a single robot, given the extremely hazardous nature of the domain [JP1999]. Having multiple robots adds a desirable redundancy to the system, as well as potentially reducing the time required to clear an area if the robots are deployed effectively. In this paper, we describe a software implementation of a multirobot demining system that was deployed as part of a larger multiagent system, AgentStorm, to assist human commanders in command and control scenarios. Simulated robotic demining agents coordinate to breach a path through a minefield; demining is modeled as a distributed optimization problem in which the robots strive to minimize an abstract cost function. Although researchers have drawn comparisons between robot demining and foraging [Goldberg & Mataric1997], path breaching cannot be modeled as a simple foraging task because the goal of the exercise is not to optimize on number of mines removed but to assure coverage of a connected path.

In artificial intelligence research, agent-based systems technology has been hailed as a new paradigm for conceptualizing, designing, and implementing software systems. Agents are sophisticated computer programs that act autonomously on behalf of their users, across open and distributed environments, to solve a growing number of complex problems. Increasingly, however, applications require multiple agents that can work together. A multi-agent system (MAS) is a loosely coupled network of software agents that interact to solve problems that are beyond the individual capacities or knowledge of each problem solver.

Milind Tambe and class co-designer Emma Bowring, with some exercise materiels "Science fiction is the spice," says Tambe. Students in a new class offered by the USC Viterbi School of Engineering will be writing computer code for Isaac Asimov's disobedient robot Speedy, and for the sinister many-bodied Star Trek menace, the Borg. Milind Tambe, an associate professor of computer science, will be using science fiction as problem sets in a class on artificial intelligence for undergraduate programmers beginning in the fall, 2006 semester. "Computer science is catching up with the ideas in these stories," says Tambe. "We are using science fiction as the spice for the main dish of teaching an important new area of our discipline." While a number of universities use science fiction to introduce concepts in physics and other fields, Tambe believes his course is the first of its kind in computer science.

The Teamcore group's roots are in Artificial Intelligence (AI). Our research in the past decade and longer has focused on "AI for social good", covering topics such as but not limited to: (i) AI for protection of forests, fish and wildlife; (ii) AI for public safety and security; (iii) AI for assisting low-resource sections of society. We focus on fundamental research problems in computational game theory, machine learning, automated planning, intelligent agents and multiagent interactions that are driven by these topics, ensuring a virtuous cycle of research and real-world applications. Examples of research driven by real-world problems include challenges of learning and handling bounded rationality models of human (adversary) behavior from real-world data, solving massive scale games uncertainty, algorithms that enable influence maximization in social networks under dynamism and uncertainty. Our research is in real world use in the form of decision aids to assist Governmental and Non-Governmental Organizations in protecting wildlife, fisheries, and forests, as well as in assisting in public safety and security at airports or ports, or on trains and in university campuses.

Engineers love to do crazy things and when they involve robots, we love to tell you about them. We've reported on a lot of ping-pong playing robots over the years but usually they're based on conventional industrial robot arms or humanoid arm designs. What if, instead of a multi-jointed arm, you wanted to design a cartesian ping-pong playing robot? That is, a robot that can only move linearly on an X, Y, and Z axis. That's the question Hossein Jahandideh and his fellow engineers asked themselves.

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.