Ten years ago, at the first AAAI conference, Alan Newell (1982), in his presidential address, focused on understanding the then dominant paradigm for artificial intelligence: the writing of symbolic reasoning programs for an agent that would act rationally to achieve a goal. He distinguished the "knowledge level" from the "symbol level" description of a system. The former encompasses the knowledge and conditions necessary for an agent to solve a problem. It is only when the knowledge level is reduced to a symbol level description that issues of implementation get considered. Newell's analysis postulated a single agent, with fixed knowledge and a specified goal.

The term intelligence in the phrase artificial intelligence suggests that intelligence is the key characteristic to be analyzed and synthesized by the research discipline. However, for many researchers the objective of this discipline is the scientific understanding of all aspects of complex behavior. For some, this objective might be limited to the traditional goals of scientific psychology: understanding humans. For others, it might include other species and artificial systems. In either case, the enterprise is driven by psychological questions because humans are the extreme of the known range of possibility that drives our curiosity.

We pay particular attention to the design process, highlighting the objectives and properties that are important in the design of good prediction mechanisms. Whereas game theorists ask what outcome results from a game, mechanism designers ask what game produces a desired outcome. In this sense, game theorists act like scientists and mechanism designers like engineers. In this article, we survey a number of mechanisms created to elicit predictions, many newly proposed within the last decade. We focus on the engineering questions: How do they work and why?

Interacting with a computer requires adopting some metaphor to guide our actions and expectations. Most human-computer interfaces can be classified according to two dominant metaphors: (1) agent and (2) environment. Interactions based on an agent metaphor treat the computer as an intermediary that responds to user requests. In the environment metaphor, a model of the task domain is presented for the user to interact with directly. The term agent has come to refer to the automation of aspects of human-computer interaction (HCI), such as anticipating commands or autonomously performing actions.

As distributed systems of computers play an increasingly important role in society, it will be necessary to consider ways in which these machines can be made to interact effectively. We are concerned with heterogeneous, distributed systems made up of machines that have been programmed by different entities to pursue different goals. Adjusting the rules of public behavior (the rules of the game) by which the programs must interact can influence the private strategies that designers set up in their machines. These rules can shape the design choices of the machines' programmers and, thus, the run-time behavior of their creations. Certain kinds of desirable social behavior can thus be caused to emerge through the careful design of interaction rules.

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.