It involves choosing optimally between different lines of action in various information contexts that range from perfectly knowing all aspects of the decision problem to having just partial knowledge about it. The physical context often includes other interacting autonomous systems, typically called agents. In this article, I focus on decision making in a multiagent context with partial information about the problem. Relevant research in this complex but realistic setting has converged around two complementary, general frameworks and also introduced myriad specializations on its way. I put the two frameworks, decentralized partially observable Markov decision process (Dec-POMDP) and the interactive partially observable Markov decision process (I-POMDP), in context and review the foundational algorithms for these frameworks, while briefly discussing the advances in their specializations.

With the exception of plan repair, important topics related to the use of plans (robust execution, reactivity, monitoring, evaluation) have received significantly less consideration. In realistic domains, however, plan generation is only a small component of the overall package. In particular, plans must be updated in response to new information and requirements in a timely fashion to ensure that they remain viable and relevant. Plan execution involves more than blind adherence to previously generated plans. Rather, run-time decisions are made to adapt, initiate, or abandon plans and activities in response to current considerations within the operating environment.

The responsibility framework was devised specifically for coordinating behavior in complex, unpredictable, and dynamic environments, such as industrial control. In distributed AI (DAI) systems, problem-solving agents cooperate to achieve the goals of the individuals and of the system as a whole. Each individual is capable of a range of identifiable problem-solving activities, has its own aims and objectives, and can communicate with others. Typically, agents within a given system have problem-solving expertise that is related but distinct and that has to be coordinated when solving problems. Such interactions are needed because of the dependencies between agents' actions and the necessity to meet global constraints and because often, no one individual has sufficient competence to solve the entire problem.

The years of research on robotics and multiagent systems are coming together to provide just such a disruption to the material-handling industry. While autonomous guided vehicles (AGVs) have been used to move material within warehouses since the 1950s, they have been used primarily to transport very large, very heavy objects like rolls of uncut paper or engine blocks. The confluence of inexpensive wireless communications, computational power, and robotic components are making autonomous vehicles cheaper, smaller, and more capable. In recent years, we have seen an increase in the use of autonomous vehicles in the field. Examples include teleoperated military devices like iRobot's Packbot and the pilotless Predator aircraft, both of which have seen service in Iraq and Afghanistan.

Lifelike characters, or animated agents, provide a promising option for interface development because they allow us to draw on communication and interaction styles with which humans are already familiar. In this contribution, we revisit some of our past and ongoing projects to motivate an evolution of character-based presentation systems. This evolution starts from systems in which a character presents information content in the style of a TV presenter. It moves on with the introduction of presentation teams that convey information to the user by performing role plays. To explore new forms of active user involvement during a presentation, the next step can lead to systems that convey information in the style of interactive performances. From a technical point of view, this evaluation is mirrored in different approaches to determine the behavior of the employed characters. By means of concrete applications, we argue that a central planning component for automated agent scripting is not always a good choice, especially not in the case of interactive performances where the user might take on an active role as well. Work in this area is motivated by a number of supporting arguments, including the fact that such characters allow for communication styles common in human-human dialogue and thus can release users from the burden to learn and familiarize themselves with less native interaction techniques. Furthermore, well-designed characters show great potential for making interfacing with a computer system more enjoyable. One aspect when designing a character is to find a suitable visual and audible appearance. In fact, there is now a broad spectrum of characters that rely on either cartoon drawings, recorded (and possibly modified) video images of persons, or geometric three-dimensional (3D) body models for their visual realization with recorded voices or synthesized speech and sound to determine their audible appearance. Audiovisual attractiveness, however, is not everything. Rather, the success of an interface character in terms of user acceptance and interface efficiency very much depends on the character's communication skills and its overall behavior. On a very low level of abstraction, the behavior of an agent can be regarded as the execution of a script, that is, a temporally ordered sequence of actions including body gestures, facial expressions, verbal utterances, locomotion, and (quasi-) physical interactions with other entities of the character's immediate environment. It comes as no surprise then that behavior scripting, in one way or another, has been widely used in projects that deal with interface characters.

Research on constraints and agents is emerging at the intersection of the communities studying constraint computation and software agents. Constraint-based reasoning systems can be enhanced by using agents with multiple problem-solving approaches or diverse problem representations. The constraint computation paradigm can be used to model agent consultation, cooperation, and competition. An interesting theme in agent interaction, which is studied here in constraint-based terms, is confronting ignorance: the agent's own ignorance or its ignorance of other agents. On the one hand, agent behavior, for example, negotiation, can be modeled as constraint satisfaction and optimization. On the other hand, agents can be used to accomplish constraint satisfaction and optimization, for example, to solve distributed scheduling problems. Agents offer opportunities to apply the constraint computation paradigm and present challenges to extend the paradigm. Constraint computation provides a general ...

It features a unique combination of properties that distinguish it from other such games, including continuous action and state spaces, uncertainty in execution, a unique turntaking structure, and of course an adversarial nature. This article discusses some of the work done to date, focusing on the software side of the pool-playing problem. We discuss in some depth CueCard, the program that won the 2008 computational pool tournament. Research questions and ideas spawned by work on this problem are also discussed. We close by announcing the 2011 computational pool tournament, which will take place in conjunction with the 25th AAAI Conference.

We describe the goal of the overall Trading Agent Competition (TAC) and highlight particular competitions. We discuss its significance in the context of today's global market economy as well as AI research, the ways in which it breaks away from limiting assumptions made in prior work, and some of the advances it has engendered over the past 10 years. Since its introduction in 2000, TAC has attracted more than 350 entries and brought together researchers from AI and beyond. Chess, poker, stock trading, real-time strategy games, robot soccer, robot rescue or planning, and autonomous vehicles are among the most well known. Adaptability, proactiveness, and interoperability are essential characteristics of these games.

A recurring requirement for human-level artificial intelligence is the incorporation of vast amounts of knowledge into a software agent that can use the knowledge in an efficient and organized fashion. This article discusses representations and processes for agents and behavior models that integrate large, diverse knowledge stores, are long-lived, and exhibit high degrees of competence and flexibility while interacting with complex environments. There are many different approaches to building such agents, and understanding the important commonalities and differences between approaches is often difficult. We introduce a new approach to comparing frameworks based on the notions of commitment, reconsideration, and a categorization of representations and processes. We review four agent frameworks, concentrating on the major representations and processes each directly supports.

We describe an approach to intelligent user interfaces, based on the idea of making the computer a collaborator, and an application-independent technology for implementing such interfaces. For us, any interface that is called intelligent should at least be able to answer the six types of questions from users shown in figure 1. Being able to ask and answer these kinds of questions implies a flexible and adaptable division of labor between the human and the computer in the interaction process. Unlike most current interfaces, an intelligent user interface should be able to guide and support you when you make a mistake or if you don't know how to use the system well. What we are suggesting here is a paradigm shift. As an analogy, consider the introduction of the undo button.