Belief-Desire-Intention Architectures

Agent theories that are based on mental categories originate from works in the Artifi-cial Intelligence domain that focus on the understanding of practical reasoning and the

essence of actions in human behavior so as it can be mapped to intelligent software. One of the most interesting results of such works lead to the introduction ofmental categories (ormental attitudes) to describe, understand and analyze the state of the agent and its past and future behavior [Bra87]. Notions such as beliefs, desires, intentions, knowledge, commitments, etc. were used for such purposes. A mental attitude is a position taken by a human or an agent towards a statement or an expression over the world. For example, an agent or a human canbelievea statement, ordesireto have that statement holding in the future.

Among the most popular agent architectures based on mental categories is the BDI (Belief, Desire, Intention) architecture [RG91, Bra87]. This architecture is based on the notions ofBelief, Desire andIntention. In fact, most of agent theories that are based on mental categories are called BDI-oriented or BDI-like theories. In general, BDI ap-proaches are based on a set of mental categories with defined semantics and a control architecture that defines the agent’s mental cycle. Themental cycle is the process that rationally selects its course of action based on these mental categories ( [RG95] cited in [Oli98]). We provide two simple examples showing how a mental cycle may be de-signed.

The first example is taken from [RG91]. The agent architecture is based on the be-lief, desire and intention mental categories. The mental cycle is composed of three pro-cesses. The option generation process waits for events perceived as beliefs, determines which are relevant according to the current desires of the agent and generates a set of options. The deliberation process selects the set of options that the agent believes to be pertinent for achievement. Finally, the selected options are considered as intentions and an execution process works out for their achievement.

The second example is Shoham’s agent-oriented programming language Agent0 [Sho93]. Agent0 is based on a set of mental categories composed of be-liefs, commitments and capabilities. Capabilities are what the agent can potentially carry out, i.e. the set of actions the agent can perform. Commitments can be seen as the agent’s obligations. The mental cycle is described using behavioral rules [Ret98] that map agent beliefs to directly executable commitments that invoke its capabilities.

Several mental attitudes were defined by people adopting the BDI approach.

M¨uller [M¨ul96] informally defined a set of these mental attitudes. We provide in the following a description of some pertinent mental categories based on the indicated ref-erence.

Belief

According to [M¨ul96], beliefs are “the agent’s expectations about the current state

of the world and about the likelihood of a course of action achieving certain effects”.

Therefore, the beliefs of the agent map its perception of the world. With a more general view, beliefs might also include the perception of the state of other agents and of the agent’s own state. Beliefs on the agent’s own state are essential for the agent to have control over itself, while the beliefs on the statuses of the other agents are required to to reason about the opportunity of cooperating with some of them.

Motivation

The motivation attitude is introduced as a source of stimulation that leads the agent to act on the world. In [TL94], the motivation is defined as a “driving force that arouses and directs [agent] action toward the achievement of goals”. It is important for an agent to be motivated, since motivations provide criteria to identify those situations to which the agent has to be responsive by acting on the environment. In other words, motivations allow the agent to have preferences over the status of the environment.

Goal

A goal denotes a state that the agent wants to achieve through the execution of a certain plan of actions [TL94].

Commitment/Intention

In general, the agent may have multiple possible options regarding which goal to achieve, or which course of action to adopt. In this case, the agent has to make a selection between these options, either because one, or a subset of them suffice, or because the agent is resource-bounded and cannot adopt all of them simulta-neously. Accordingly, an intention is a goal or a course of action that the agent has committed to adopt. Therefore, the commitment process can be viewed as an en-gagement of the agent to achieve the corresponding intentions. When the agent commits to an option, it decides how long it will persist in doing the correspond-ing action and under which circumstances it can be dropped [SRG98]. In general, the term ‘commitment’ is used for engagements to the other agents, while the term

‘intention’ is used for self-engagements.

Capability

The agent is an active software entity. The set of actions the agent is able to perform can be classified into action types. A capability denotes the knowledge about a cer-tain action that the agent can execute to act on the environment. This knowledge is used by the agent to decide how and when it is appropriate to execute a certain action. This definition adheres to and extends that adopted in [Ret98].

More than an architecture for building agents, the BDI paradigm is often used to model agents and their behaviors, even though these agents are not built using mental categories. For example, the database in which an agent stores the perceived environ-ment corresponds to the agent beliefs. During this thesis, we make extensive use of the BDI terminology to describe the agent architecture presented in Chapter 4. Furthermore, we develop in Chapter 7 an abstract BDI model for NM-oriented agent-based applica-tions, where a set of mental categories are selected and refined from those described above.