Concrete architectures (Section 1.4) Part II: Shabbir Ssyed

1. Concrete architectures (Section 1.4)Part II: Shabbir Ssyed We will describe four classes of agents: • Logic based agents • Reactive agents • Belief-desire-intention agents • Layered architectures

2. Reactive architectures (Section 1.4) Subsumption architecture: Rodney Brooks • Task accomplishing behavior. Situation Action. • Many behaviors can fire simultaneously. Subsumption hierarchy: Lower layer has higher priority than higher layers.

3. Background • Emergent behavior • Ant colony • Artificial life • Intelligence without reason • Intelligence without representation

4. Simple algorithm • Var fired:f(R) • Var selected: A • Begin • fired:={(c,a)|(c,a)  R and p c} • for each (c,a) fired do • if ¬( (c’,a’)  fired such that(c’,a’)<(c,a))then • return a • End-if • End-for • Function action(p:P):A • Return null • End function action

5. Robot scenario • If detect an obstacle then change direction (1.6) • If carrying samples and at the base then drop samples (1.7) • If carrying samples and not at base then travel upgradient (1.8) • If detect a sample then pick sample up (1.9) • If true then move randomly (1.10) (1.6) < (1.7) < (1.8) < (1.9) < (1.10)

6. Modified sequence • If carrying samples and at the base then drop samples (1.11) • If carrying samples and not at the base then drop 2 crumbs and travel up gradient (1.12) • If sense crumbs then pick up 1 crumb and travel down gradient (1.13) (1.6) < (1.11) < (1.12) < (1.9) < (1.13) < (1.10)

7. Advantages & distadvantages Advantages: simplicity, economy, computational tractability, robustness against failure. Disadvantages: • How decision making can be done on non-local information. • How purely reactive agents can be designed that learn from experience. • Relationships between individual behaviors, environments, and overall behaviors are not understandable • It is harder to build agents that contain multiple layers.

8. Concrete architectures (Section 1.4) We will describe four classes of agents: • Logic based agents • Reactive agents • Belief-desire-intention agents • Layered architectures

9. Belief-Desire-Intention architecture • Deliberation: what goals we want to achieve. • Means-ends reasoning/analysis: how are we going to achieve these goals. If(conditions) Then{statements}; Else{statements};

10. Roles of Intentions • Intentions drive means-ends reasoning • Intentions constrain future deliberation. • Intentions persist. • Intentions influence beliefs upon which future practical reasoning is based.

11. Tradeoff between degree of commitment and reconsideration Rate of change of world:  If  is • low bold agents outperform cautious agents. • high cautious agents outperform bold agents. Different environments require different types of decision strategies.

12. BDI Architecture

13. Functions • Options: (Bel)* (Int) (Des) • Filter: (Bel)* (Int)* (Des) (Int) • Execute: (Int)A • Action:PA Current intentions are either previously held intentions or newly adopted options

14. Concrete architectures (Section 1.4) We will describe four classes of agents: • Logic based agents • Reactive agents • Belief-desire-intention agents • Layered architectures

15. Layered architecture • Horizontal layering • Vertical layering: • One pass control • Two pass control. • Examples: • Touring machines (Horizotal arch.) • InteRRaP (Vertical layered two pass arch.)

16. Turing MachinesInnes Ferguson

17. Layers Reactive : • Reactive layer provides more or less immediate response to changes that occur in environment. • Implemented as set of situation—action rules; like subsumption. • These rules map sensor I/p directly to effector o/p. • Makes reference to agents current state. • Cannot do explicit reasoning about the world. Planning: • Does not generate plans from scratch; employs library of plans called skeletons. Modelling: • Represents various entities in the worlds. • Predicts conflicts between agents and generates new goals to resolve the conflicts

18. IntRRaPJoerg Mueller

19. Properties of Layers • Situation recognition: maps KB and current goals to a new set of goals • Goal activation: selects which plans to execute, based on the current plans, goals, and KB of that layer • Bottom up activation • Top down execution

20. Difference between TM & InteRRaP • KB is in InteRRaP; not in TM. • In TM: each layer directly coupled with I/p and o/p; so a control layer is necessary. In InteRRaPP: layers interact with each other.

21. Layered vs. unlayered architecture • Layered architecture lacks the conceptual and semantic clarity of unlayered architecture (e.g., logic-based) • But remains the most popular; because layering represents decomposition of functionality

22. Agent Programming Languages (Section 1.5) • Agent0 Agent-oriented programming [Yoav Shoham, 1990] • Concurrent METATEM Logic formulae [Michael Fisher, 1994]

23. Agent0: language components • set of initial capabilities, • Set of initial beliefs, • Set of initial commitments (intentions) • Set of commitment rules

24. Agent0: commitment rules A commitment rule has: • Message condition • Mental condition • Action Rule fires when: • Message condition matches against messages received by agent and • Mental condition matches against beliefs held by agent Action can be private or communicative

25. Flow control in Agent0

26. Concurrent METATEM • Each agent is programmed by giving a temporal logic specification. • Agents specification is executed directly to generate its behaviour. • Pi Fi. Is a rule. Each rule is continuously matched against an internal recorded history, if matched rule fires. • If rule fires then commitment is updated to future time part. • Example: Agent X asks Resource Controller(RC) for resource; and RC gives X the resource, after mutual exclusion is performed.

27. Conclusions Goal of Introduction • What is an agent • Why this is an important area for building flexible autonomous systems Goal of research activities • Theory, design, construction and implementation of intelligent agents

28. THANK YOU for your attentionLet’s start the discussion!