1. Concrete architectures (Section 1.4) Part II: Shabbir Ssyed We will describe four classes of agents: 1.Logic based agents 2.Reactive agents 3.Belief-desire-intention agents 4.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 1.Var fired:f(R) 2.Var selected: A 3.Begin 4. fired:={(c,a)|(c,a)  R and p  c} 5. for each (c,a)  fired do 6. if ¬(  (c’,a’)  fired such that(c’,a’)<(c,a))then 7. return a 8.End-if 9.End-for 10.Function action(p:P):A 11.Return null 12.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: 1.How decision making can be done on non-local information. 2.How purely reactive agents can be designed that learn from experience. 3.Relationships between individual behaviors, environments, and overall behaviors are not understandable 4.It is harder to build agents that contain multiple layers.

8. Concrete architectures (Section 1.4) We will describe four classes of agents: 1.Logic based agents 2.Reactive agents 3.Belief-desire-intention agents 4.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 1.low bold agents outperform cautious agents. 2.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: 1.Logic based agents 2.Reactive agents 3.Belief-desire-intention agents 4.Layered architectures

15. Layered architecture Horizontal layering Vertical layering: 1.One pass control 2.Two pass control. Examples: –Touring machines (Horizotal arch.) –InteRRaP (Vertical layered two pass arch.)

16. Turing Machines Innes 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. IntRRaP Joerg 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) 1.Agent0 Agent-oriented programming [Yoav Shoham, 1990] 2.Concurrent METATEM Logic formulae [Michael Fisher, 1994]

23. Agent0: language components 1.set of initial capabilities, 2.Set of initial beliefs, 3.Set of initial commitments (intentions) 4.Set of commitment rules

24. Agent0: commitment rules A commitment rule has: 1.Message condition 2.Mental condition 3.Action Rule fires when: 1.Message condition matches against messages received by agent and 2.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 attention Let’s start the discussion!