Multi-agent Engineering. Lecture 4 Concrete Architectures for Intelligent Agents. Belief-Desire-Intention Architecture. Ivan Tanev.

Transcription

1 Multi-agent Engineering Lecture 4 Concrete Architectures for Intelligent Agents. Belief-Desire-Intention Architecture Ivan Tanev 1 Outline 1. Concrete architectures 2. Belief-Desire-Intention (BDI) Architecture. Definition 3. Key Problem (Dilemma) of BDI Architecture. 4. Main Functional Components of BDI Architecture 5. Realization of Main Functions in BDI Architecture 2 1

2 1. Concrete Architectures Abstract Architecture: Let P be a set of percepts. Then see is a function see: S P which maps the environmental states to perceps. Now is a function : P* A which maps sequences of percepts to s. see: S P see : P* A P* Agent Environment S Concrete Architecture: How function might be implemented BDI Architecture So far Reactive Architecture: Behavioral Architecture developing and combining individual behaviors, Situated Architecture agents are situated in some environment, Reactive Architecture agents are reacting to an environment, without reasoning about it. see: S P see : P* A P* Agent Environment S 4 2

3 2. BDI Architecture Roots in the philosophical traditions of understanding practical reasoning the process of deciding, moment by moment, which to perform in the furtherance of our goals. Practical reasoning involves two important processes: Deciding what goals we want to achieve (deliberation), and How we are going to achieve these goals (means-end reasoning). Intentions: Example: available => Intentions play a critical role in reasoning process. Properties of Intentions: Intentions tend to lead to Attempts to achieve the intention, and Expectations to try again if an attempt fails. Intentions constrain the future practical reasoning Intentions persist not for too long dropping the intention Intentions are closely related to the about future BDI Architecture Interpreter Beliefs Desires Intentions Environment 6 3

4 3. Dilemma Important roles of in practical reasoning: Intentions drive means-ends reasoning, Intentions constrain future deliberations, Intentions persist, Intentions influence upon which future practical reasoning is based. A key problem in the design of practical reasoning agents is that of achieving a good balance between these different concerns: Agent should at times drop some - agent should stop to reconsider its. Dilemma to reconsider the or to keep them intact them? The dilemma of balancing between the pro-active (keeping the same : goal directed) and reactive (reconsidering the : event driven) behaviors Dilemma Dilemma to reconsider the or to commit? Depends on the environment: In static (unchanging) environments purely proactive (keeping the same : goal-oriented) behavior is adequate. In dynamic environments the ability to react to changes by reconsidering the becomes more important. Information about the environment is stored in the belief component of the agent. 8 4

5 3. Dilemma: Strategies for Modifying Intentions Keep the intention Blind Commitment Until intention has been achieved Single minded Open minded Until intention achieved or no longer possible While intention is believed to be possible 9 4. Functional Components a revision function determines new sets of, Option generation function determines the available to the agent (its ), on the basis of current about its environment and its, Filter function represents the agent s deliberation process, and determines the agent s on the basis of its current,, and. The set of current represents the agent s current focus the states of affairs that it has committed to trying to achieve. selection process determines an to perform on the basis of current. 10 5

6 5. Functions Definitions: Bel set of all possible, Des set of all possible desired, and Int set of all possible. The state of the BDI agent is a triple (B, D, I), whereb Bel, D Des and I Int. Then the belief revision function is a mapping: : ρ(bel) P ρ(bel) Functions The generation function maps a set of and a set of to a set of : : ρ(bel) ρ(int) ρ(des) Roles: responsible for means-end reasoning the process of deciding how to achieve. Once agent has formed an intention x, it must subsequently consider to achieve x. It must be consistent any option d must be consistent with both the current and current. It must be opportunistic it should recognize when environment changes advantageously, to offer new ways of achieving, or the possibility of achieving that are otherwise unachievable. 12 6

7 5. Functions The agent deliberation process deciding what to do is represented in the function: : ρ(bel) ρ(des) ρ(int) ρ(int) Roles: It must drop any that are no longer achievable, or for which the expected cost of achieving them exceeds the expected gain associated with successfully achieving them, It should retain that are not achieved, and that are still expected to have a positive overall benefit Functions The execute function returns any executable one that corresponds to a directly executable : execute: ρ(int) A The agent decision function, of BDI is then a function : P A defined as: function (p:p): A begin B := (B,p); D := (B,I); I := (B,D,I); return execute(i); end function ; 14 7

8 Summary Advantages of BDI: Intuitive process of what to do, and the how to do it. All we have an understanding about our, desired, and. Functional decomposition we know what sorts of subsystems might be required to build an agent. Difficulty: How to efficiently implement the main funtions in BDI architecture. 15 8