1. Fariba Sadri - ICCL 08 Agent Examples 1 Agents Some Examples Fariba Sadri Imperial College London ICCL Summer School Dresden August 2008

2. Fariba Sadri - ICCL 08 Agent Examples 2 Contents Teleo-Reactive agents Agent-0 BDI/AgentSpeak(L)

3. Fariba Sadri - ICCL 08 Agent Examples 3 Teleo-Reactive (TR) Programs Some references: Nilsson, TR Programs for agent control, Journal of AI Research, 1994, 139-158 Nilsson, Teleo-reactive programs and the triple-tower architecture, October 2001

4. Fariba Sadri - ICCL 08 Agent Examples 4 TR-Programs They are named sequences of condition- action rules : Program for Goal G: G => nil % i.e. do nothing C1 => A1 C2 => A2.. Cn => An.

5. Fariba Sadri - ICCL 08 Agent Examples 5 TR-Programs They are intended to direct the agent towards a goal, while continuously taking into account changing perceptions of the environment. No declarative semantics Only procedural semantics

6. Fariba Sadri - ICCL 08 Agent Examples 6 Demo http://www.robotics.stanford.edu/users/ nilsson/trweb/TRTower/TRTower_links. html http://www.robotics.stanford.edu/users/ nilsson/trweb/tr.html

7. Fariba Sadri - ICCL 08 Agent Examples 7 TR-Programs The C i are tests to be evaluated on the world model. The A i are actions the agent can do. At each cycle: – observations are made – the rules are checked from the top. –The first rule with a true test fires, i.e. determines the action to be done next. –The action is executed. Typically actions of later rules are intended to eventually result in the test of an earlier rule to become true (Regression Property). There is always a rule that will fire.

8. Fariba Sadri - ICCL 08 Agent Examples 8 TR-Program Examples Example (from Nilsson 2001): unpile(x); x is a block Clear(x) =>nil On(y,x) =>move-to-table(y) move-to-table(x); x is a block On(x,Ta)=>nil Holding(y)=>putdown(y,Ta) Clear(x)=>pickup(x) T=>unpile(x) Putdown and pickup are primitive actions.

9. Fariba Sadri - ICCL 08 Agent Examples 9 TR-Programs Example Example (from Nilsson 2001): move(x,y);x and y are blocks On(x,y)=>nil Holding(x)  Clear(y)=>putdown(x,y) Holding(z)=>putdown(z,Ta) Clear(x)  Clear(y)=>pickup(x) Clear(y)=> unpile(x) T=> unpile(y)

10. Fariba Sadri - ICCL 08 Agent Examples 10 TR Triple Tower Architecture Perception Tower (Rules) Model Tower (Predicates + TMS) Action Tower (Action Routines) Sensors Environment

11. Fariba Sadri - ICCL 08 Agent Examples 11 TR Triple Tower Architecture Example ¬  XOn(x,Y)  ¬Holding(Y)  Clear(Y) Clear(A) On(A,B) Holding(C) TR- Program Sensors Environment

12. Fariba Sadri - ICCL 08 Agent Examples 12 TR-Programs The actions A i may be: –primitive, –Sets of actions that can be executed simultaneously, or –refer to other TR programs. A TR program called will continue until the original condition leading to it being called remains the highest one in the original program that remains true.

13. Fariba Sadri - ICCL 08 Agent Examples 13 TR-Programs New Info from environment deletes old info (TMS). Also forward reasoning to derive all provable facts.

14. Fariba Sadri - ICCL 08 Agent Examples 14 TR-Programs Where do TR-programs fit in within the agent classification given in the introduction ?

15. Fariba Sadri - ICCL 08 Agent Examples 15 Agent-0 Reference: Yoav Shoham, Agent0: A simple agent language and its interpreter, Proceedings AAAI-91, 1991, 704- 709 One of the early multi-agent models and programming languages. Fairly simple Motivation: partly to gain experience from implementing an agent model

16. Fariba Sadri - ICCL 08 Agent Examples 16 Agent-0 Agents send messages to each other: Inform, Request, Unrequest A1 A2

17. Fariba Sadri - ICCL 08 Agent Examples 17 Agent-0 Mental State Mental state is made up of : Capabilities - fixed Commitment rules- fixed Beliefs- get updated Commitments- get updated

18. Fariba Sadri - ICCL 08 Agent Examples 18 Agent-0 Capabilities cap(time,private action, mental condition) e.g. cap(T, rotate(Degree1), not (cmtd(_,do(T,rotate(Degree2)) and Degree1\=Degree2 ) ) where Degree1, Degree2, T are variables. This says: The agent is able to rotate (something) by Degree1 degrees at some future time T if it does not already have a commitment to any agent to rotate (it) by a different number of degrees at the same time.

19. Fariba Sadri - ICCL 08 Agent Examples 19 Agent-0 Commitment Rules commit(messpattern, mentalcond, agent, action) The action can be a single or a sequence of actions. e.g. commit( (Ag, REQUEST(Act)), (_,myfriend(Ag), Ag, Act) This says: The agent can (perhaps) commit to do Act for agent Ag if Ag has just requested Act and agent believes Ag is a friend. No declarative semantics, Just operational semantics.

20. Fariba Sadri - ICCL 08 Agent Examples 20 Agent-0 Beliefs bel(Ag, F) where Ag is the agent who believes Fact F AGENT-0 agents trust one another. They believe anything they are told, incorporate it in their beliefs, and retract any older contradictory beliefs. Only atomic propositions or their negations are held as beliefs. This is to simplify knowledge assimilation. It makes consistency checking trivial.

21. Fariba Sadri - ICCL 08 Agent Examples 21 Agent-0 Commitments cmtd(agent, action) where the commitment is to agent. The set of commitments implicitly defines the future actions for the agent. Commitments are acted upon, by executing the action when its time comes.

22. Fariba Sadri - ICCL 08 Agent Examples 22 Agent-0 Time Agents measure time as cycle number (number of cycle executions) and synchronise their cycle executions using a global clock. So the time of a committed-to action comes when the agent’s cycle number equals the cycle time embedded in the action description.

23. Fariba Sadri - ICCL 08 Agent Examples 23 Agent0 Cycle

24. Fariba Sadri - ICCL 08 Agent Examples 24 Agent0 Cycle Initialisation: Initialises the Capabilities, Commitment rules, Beliefs, and Commitments After that the agent is continually involved in: –Updating its beliefs –Updating its commitments –Honouring commitments whose time has come

25. Fariba Sadri - ICCL 08 Agent Examples 25 Agent-0 Commitments Commitments are only to primitive actions. So the agent cannot commit to bringing about a state that requires any element of planning.

26. Fariba Sadri - ICCL 08 Agent Examples 26 Agent-0 Actions Private –Can be anything Communicative –Inform(t a fact) –Request(t, a, action) –Unrequest(t, a, action) –Refrain(action)

27. Fariba Sadri - ICCL 08 Agent Examples 27 Agent-0 Actions Actions can be Conditional If mntlcond then action If at time t you believe F holds at time t’ then at time t inform a that F holds at t’ Unconditional

28. Fariba Sadri - ICCL 08 Agent Examples 28 BDI/AgentSpeak(L) References A. Roa, AgentSpeak(L):BDI Agents speak out in a logical language, Springer LNCS 1038, 1996 A. Rao, M. Georgeff, An abstarct architecture for rational agents, Proceedings of the 3 rd International Conference on Principles of Knowledge Representation and Reasoning, KRR92, Boston, 1992 R. Bordini et al, Programming MAS in AgentSpeak using Jason, Wiley, 2007

29. Fariba Sadri - ICCL 08 Agent Examples 29 BDI/AgentSpeak(L) Motivations: BDI agents are “traditionally” specified in a modal logic with modal operators to represent BDI (Beliefs, Desires and Intentions). Their implementations (e.g. PRS, dMARS), however, have typically simplified their specifications and used non-logical procedural approaches. AgentSpeak is a programming language based on restricted FOL. AgentSpeak attempts to provide operational and proof theoretic semantics for PRS and dMARS ( and thus by a roundabout way for BDI agents)

30. Fariba Sadri - ICCL 08 Agent Examples 30 BDI/AgentSpeak(L) Further Motivations: To incorporate some practical reasoning: –Means ends reasoning, deciding how to achieve goals –Reaction to events, for example when something unexpected happens –Choice deliberation, deciding what we want to achieve (our intention) from amongst our desires

31. Fariba Sadri - ICCL 08 Agent Examples 31 BDI/AgentSpeak(L) Internal (Mental) State –A set of beliefs (similar to Agent0 beliefs) –A set of current desires (or goals) typically of the form !b where b is belief interpreted as desire for state of the world in which b holds. –A set of pending events typically perceptions of messages interpreted as belief updates: +b, -b or as goals to be achieved: +!b including request messages from other agents usually recorded as new belief events, perhaps as a new belief that the request has been made. –A set of intentions (similar to agent0 commitments) –A plan library. A plan has a triggering condition (an event), a mental state applicability condition, and a collection of sub- goals and actions (similar to ECA rules).

32. Fariba Sadri - ICCL 08 Agent Examples 32 AgentSpeak(L) Beliefs and Event Terms No modal opeartors Beliefs: a conjunction of ground literals adjacent(room1, room2) & loc(room1) & ¬empty(room1) Events: If b is an atomic belief then the following are event terms: –!b represents an achievement goal, e.g. !loc(room2) –?b represents a test goal, e.g. ?empty(room1) –+b, -b representing events of adding or deleting beliefs (events generated by messages) –+!b, -!b –+?b, -?b Agent can have explicit goals, given by events

33. Fariba Sadri - ICCL 08 Agent Examples 33 AgentSpeak Agent Cycle see environment beliefs desires plans generate new intentions intentions action execute next step of some intention events

34. Fariba Sadri - ICCL 08 Agent Examples 34 AgentSpeak Agent Cycle Notice external/internal changes Update belief and record as events in event stores e.g. +!location(robot, b), +location(waste, a) Choose event (from event store) or desire (from desire store for which there is at least one plan) Select plan – this becomes new intention Drop intentions no longer believed viable Resume intention –Execute an action, or –Post subgoal as a new goal event Repeat cycle

35. Fariba Sadri - ICCL 08 Agent Examples 35 AgentSpeak Plans Each agent has its own repertoire of (primitive) actions and plan library. Plans are ECA rules of the form: e:b1,…,bm <- h1;..;hk e is an event term the bi are belief terms – b1, …, bm is called context the hi are goals or (primitive) actions Plans are used to respond to belief update events and new goal events

36. Fariba Sadri - ICCL 08 Agent Examples 36 AgentSpeak Plans Examples +location(waste, X) : location(robot,X) & location(bin,Y) <- pick(waste); !location(robot,Y); drop(waste).

37. Fariba Sadri - ICCL 08 Agent Examples 37 AgentSpeak Plans Examples +location(waste, X) : location(robot,X) & location(bin,Y) <- pick(waste); !location(robot,Y); drop(waste). Context Triggering Event- Addition Body of the plan of a fact

38. Fariba Sadri - ICCL 08 Agent Examples 38 AgentSpeak Plans Examples +location(waste, X) : location(robot,X) & location(bin,Y) <- pick(waste); !location(robot,Y); drop(waste). The intended reading of this is very similar to event-condition-action rules (except that the action part is more sophisticated): On event of noticing waste at X, if robot is at X and bin at Y, then (robot) pick waste, make its location Y and drop waste.

39. Fariba Sadri - ICCL 08 Agent Examples 39 AgentSpeak Plans Examples +!location(robot, X) : location(robot,X) <- true. +!location(robot, X) : location(robot,Y) & not X=Y & adjacent(Y,Z) & not location(car,Z) <- move(Y,Z); +!location(robot,X).

40. Fariba Sadri - ICCL 08 Agent Examples 40 AgentSpeak Plans Examples +!location(robot, X) : location(robot,Y) & not X=Y & adjacent(Y,Z) & not location(car,Z) <- move(Y,Z); +!location(robot,X). The intended reading of this is similar to goal reduction rules: To achieve a goal location(robot,X) ….

41. Fariba Sadri - ICCL 08 Agent Examples 41 AgentSpeak Plan Example +!quench_thirst:have_glass <- !have_soft_drink; fill_glass, drink +!have_soft_drink:soft_drink_in_fridge <- open_fridge; get_soft_drink

42. Fariba Sadri - ICCL 08 Agent Examples 42 AgentSpeak Plans Some statements from Anand Rao “Rules in a pure logic program are not context-sensitive as plans.” ???? –Situation calculus and its many descendents - State context –Event calculus - Temporal context –Conditions/preconditions of plan provide context

43. Fariba Sadri - ICCL 08 Agent Examples 43 AgentSpeak Plans Some statements from Anand Rao “Rules execute successfully returning a binding for unbound variables; however, execution of plans generates a sequence of ground actions that affect the environment.” compare with Abductive Logic Programs

44. Fariba Sadri - ICCL 08 Agent Examples 44 location(robot, X)  current_location(robot,Y) & ¬ X=Y & adjacent(Y,Z) & ¬ current_location(car,Z) & move(Y,Z) & location(robot,X).

45. Fariba Sadri - ICCL 08 Agent Examples 45 AgentSpeak Plans Some statements from Anand rao “In a pure logic program there is no difference between a goal in the body of a rule and the head of a rule. In an agent program the head consists of a triggering event, rather than a goal.... allows both goal-directed and data- directed invocation of plans.” compare with Abductive Logic Programs

46. Fariba Sadri - ICCL 08 Agent Examples 46 location(robot, X)  current_location(robot,Y) & not X=Y & adjacent(Y,Z) & not current_location(car,Z) & move(Y,Z) & location(robot,X). location(waste, X) & ¬X=bin  pick(waste) & drop(waste, bin)

47. Fariba Sadri - ICCL 08 Agent Examples 47 AgentSpeak Plans Some statements from Anand rao “While a goal is being queried the execution of that query cannot be interrupted in a logic program. However, the plans in an agent program can be interrupted.” compare with Abductive logic programs run within An Agent Cycle