The Belief-Desire-Intention (BDI) model of agents, developed by Australian Artificial Intelligence Institute, has become one of the best known and most studied models for agents. The model is based on folk psychology in that BDI agents are characterized by a mental state with three components: beliefs, desires, and intentions (Rao & Georgeff, 1995). Generally, beliefs are what is contained in the agent’s local knowledge base, and desires are what the agent is trying to achieve based on its beliefs. Intentions are currently adopted plans, which are predetermined sequences of actions (or subgoals) that can accomplish specified tasks. An agent’s practical reasoning involves repeatedly updating its beliefs from information in the environment, deciding what options are available, filtering these options to determine new intentions, and acting on the basis of these intentions. In some cases, this reasoning may also include an effort to coordinate with other agents in the system in order to synthesize beliefs and desires into an achievable consensual plan of action.
Agent-oriented programming (Shoham, 1993) is a multiagent programming model in which agents are explicitly programmed in terms of mentalistic notions. In a multiagent
system, agents can have different beliefs and goals, and there may be multiple instances of any given agent. Perspectives are implicitly embedded in a given agent’s worldview;
this worldview is similar to Churchman’s (1971) use of Hegel’s Weltanschauung and Singer’s natural image. In this model, beliefs can be defined as the informative component of the agent’s state. Agents acting under different perspectives each pay unique attention to various information items in the environment when collecting data according to that perspective; in other words, when perceiving the world.
These beliefs can be partitioned into two components. The first component contains the fundamentals of the particular perspective (worldview) on which the agent is to act; these are known as meta-beliefs. The other component contains the facts that the agent collects from the environment, which are interpreted through the lens of the meta-beliefs. Beliefs are parallel to the concept of a mental model. That is, they are continuously updated as the environment changes, yet the underlying foundations often remain undisturbed.
Desires are the goals or motivations of the agent. By using certain decision criteria and other methods, the agent adopts the best intention/plan for the context.
The relationship between these components is shown in Figure 1; this figure depicts the internal structure of an agent which is comprised of beliefs and plans (Kinny, Georgeff
& Rao, 1996). In order to make an agent more socially aware, a social component based on speech acts has been added (Dignum & van Linder, 1997). Contents of beliefs and/
or plans can be similar among agents but will be interpreted differently (for instance, prioritizing) according to the belief component. Social plans may include a social network of cooperative agents. This network may, of course, be different for each agent and under different contexts. Implicit in the social plan is the need for the agent to act under direction
Figure 1. Internal Structure of an Agent
of any supervisory agent—explicit social plans are more detailed plans of communication and coordination required for the agent to carry out its task. The sensor is the component that reacts to changes in the environment to which the agent reacts; the effector implements the chosen plan.
Consider, for example, an agent based on the Lockean inquirer. The essence of the Lockean inquirer is to reach consensus about the interpretation of new information.
Beginning only with a list of basic properties (categories), the inquirer views new information in relation to whether it does or does not fit existing categories defined earlier by the Lockean community. Learning about the new information requires that consensus is reached as to its classification or that a new classification is added. The agent’s meta- belief is the store of classified observations against which new information is interpreted.
Because of its social predisposition, a Lockean inquiring agent would have predefined plans of cooperation and explicit social plans that allow it to engage in compromise.
Contrast this with a Leibnizian inquiring agent, whose primary task is to analyze and react accordingly from within its own limited worldview and because of its closed nature does not have explicit social plans of negotiation, coordination, or cooperation. This type of agent is viable in structured problems, such as those requiring predefined plans based on known concrete variables. These two types of agents have different aptitudes and different worldviews. Table 1 shows the implementation of these agents in the traditional BDI beliefs and plans structure.
These two types of inquirers can be seen in the example of the agent-oriented air traffic management system (OASIS) (Kwok, Ma & Zhou, 2002) in which a given aircraft agent tries to fly along a certain flight path provided by the coordinates of a sequence of waypoints. This agent is best represented as Leibnizian because it pays attention only to facts related to its job — taking off, flying, and landing safely. These facts are information, such as wind and trajectories; it also follows rules regarding speed and altitude and defers to the sequencing agent when required.
On the other hand, a sequencer agent has a different goal of landing all aircraft safely in an optimal sequence; it is concerned with performance of aircraft, desired separation time,
Table 1. Differing BDI Beliefs and Plans Structure of Leibnizian and Lockean Agents
Meta-beliefs Predefined social plans Explicit social plans Leibnizian
Agent Axioms, fact net If candidate = axiom Accept
Else if candidate = fact net Accept
Else
Store as potential
None
Lockean
Agent Classified
observations If observation = classification Accept
Else
Enter mediation
Cooperation, consensus- seeking
wind field, runway situation, safety of crew, passengers, ground personnel, and so on.
This has a social perspective that is representative of a Lockean inquirer. The sequencer agent must find that point at which all variables are in consensus that an individual aircraft may land in safety without disrupting the patterns of landings of other aircraft or the patterns of ground crews. This may require negotiation and cooperation with other agents as well as the overall task of coordinating flights.
The sequencer agent and the aircraft agent have different intentions and desires, thus different perceptions of the world (beliefs). Regardless, they must work together to accomplish the goal of a safe landing and hence must be able to develop an understanding of each other’s perspectives, a process in an inquiring organization referred to as perspective synthesis.