Reasoning about agents that we observe in the world must integrate two disparate levels. Our observations are often limited to the agent's external behavior, which can frequently be summarized: numerically as a trajectory in space-time (perhaps punctuated by actions from a fairly limited vocabulary). However, this behavior is driven by the agent's internal state, which (in the case of a human) may involve high-level psychological and cognitive concepts such as intentions and emotions. A central challenge in many application domains is reasoning from external observations of agent behavior to an estimate of their internal state. Such reasoning is motivated by a desire to predict the agent's behavior. Work to date focuses almost entirely on recognizing the rational state (as opposed to the emotional state) of a single agent (as opposed to an interacting community), and frequently takes advantage of explicit communications between agents (as in managing conversational protocols).
It is increasingly common in agent theory to describe the cognitive state of an agent in terms of its beliefs, desires, and intentions (the so-called “BDI” model [4, 15]). An agent's beliefs are propositions about the state of the world that it considers true, based on its perceptions. Its desires are propositions about the world that it would like to be true. Desires are not necessarily consistent with one another: an agent might desire both to be rich and not to work at the same time. An agent's intentions, or goals, are a subset of its desires that it has selected, based on its beliefs, to guide its future actions. Unlike desires, goals must be consistent with one another (or at least believed to be consistent by the agent).
An agent's goals guide its actions. Thus one ought to be able to learn something about an agent's goals by observing its past actions, and knowledge of the agent's goals in turn enables conclusions about what the agent may do in the future.
There is a considerable body of work in the AI and multi-agent community on reasoning from an agent's actions to the goals that motivate them. This process is known as “plan recognition” or “plan inference.” A recent survey is available at [2]. This body of work is rich and varied. It covers both single-agent and multi-agent (e.g., robot soccer team) plans, intentional vs. non-intentional actions, speech vs. non-speech behavior, adversarial vs. cooperative intent, complete vs. incomplete world knowledge, and correct vs. faulty plans, among other dimensions.
Plan recognition is seldom pursued for its own sake. It usually supports a higher-level function. For example, in human-computer interfaces, recognizing a user's plan can enable the system to provide more appropriate information and options for user action. In a tutoring system, inferring the student's plan is a first step to identifying buggy plans and providing appropriate remediation. In many cases, the higher-level function is predicting likely future actions by the entity whose plan is being inferred.
Many realistic problems deviate from these conditions:                Increasing the number of agents leads to a combinatorial explosion of possibilities that can swamp conventional analysis.        The dynamics of the environment can frustrate the intentions of an agent.        The agents often are trying to hide their intentions (and even their presence), rather than intentionally sharing information.        An agent's emotional state may be at least as important as its rational state in determining its behavior.        
Domains that exhibit these constraints can often be characterized as adversarial, and include military combat, competitive business tactics, and multi-player computer games.