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.

The construction of computer systems that are intelligent, collaborative problem-solving partners is an important goal for both the science of AI and its application. From the scientific perspective, the development of theories and mechanisms to enable building collaborative systems presents exciting research challenges across AI subfields. From the applications perspective, the capability to collaborate with users and other systems is essential if large-scale information systems of the future are to assist users in finding the information they need and solving the problems they have. In this address, it is argued that collaboration must be designed into systems from the start; it cannot be patched on. Key features of collaborative activity are described, the scientific base provided by recent AI research is discussed, and several of the research challenges posed by collaboration are presented.

In this article, I claim that research on cognitive architectures is an important path to the development of general intelligent systems. I contrast this paradigm with other approaches to constructing such systems, and I review the theoretical commitments associated with a cognitive architecture. These entities were intended to have the same intellectual capacity as humans and they were supposed to exhibit their intelligence in a general way across many different domains. I will refer to this research agenda as aimed at the creation of general intelligent systems. Unfortunately, modern artificial intelligence has largely abandoned this objective, having instead divided into many distinct subfields that care little about generality, intelligence, or even systems.

The simulator, acting as a server, accepts action commands from fully distributed clients (agents) throughout a 100-millisecond cycle and then updates the world state all at once at the end of the cycle. Agents receive sensory perceptions from the simulator asynchronously and at unpredictable intervals. We view robotic soccer as an example of a periodic team synchronization (PTS) domain. We define PTS domains as domains with the following characteristics: There is a team of autonomous agents A that collaborate toward the achievement of a joint long-term goal G. Periodically, the team can synchronize with no restrictions on communication: The agents can in effect inform each other of their entire internal states and decision-making mechanisms with no adverse effects on the achievement of G. These periods of full communication can be thought of as times at which the team is "offline."

Robotic soccer is a challenging research domain that involves multiple agents that need to collaborate in an adversarial environment to achieve specific objectives. Robotic soccer is an example of such complex tasks for which multiple agents need to collaborate in an adversarial environment to achieve specific objectives. Robotic soccer offers a challenging research domain to investigate a large spectrum of issues relevant to the development of complete autonomous agents (Asada et al. 1998; Kitano, Tambe, et al. 1997). The fast-paced nature of the domain necessitates real-time sensing coupled with quick behaving and decision making. The behaviors and decision-making processes can range from the most simple reactive behaviors, such as moving directly toward the ball, to arbitrarily complex reasoning procedures that take into account the actions and perceived strategies of teammates and opponents.

This page includes all the AAAI sponsored conferences, conferences presented by AAAI Affiliates, and conferences held in cooperation with AAAI. AI Magazine also maintains a calendar listing that also includes nonaffiliated conferences at www.aaai.org/Magazine/calendar.php. ICWSM-09 will be held May 17-20, 2009, in San Jose, California. IJCAI-09 will be held July 11-17, 2009, in Pasadena, California. IJCAI-09 is cosponsored by AAAI.