Abductive Logic Programming (ALP) and its Application in Agents and Multi-agent Systems

1
Abductive Logic Programming(ALP) and its
Application in Agents and Multi-agent Systems

• Fariba Sadri
• Imperial College London
• ICCL Summer School Dresden
• August 2008

2
Contents

• ALP recap
• ALP and agents
• Abductive planning via abductive event calculus
  (AEC)
• Dynamic planning with AEC
• Abductive proof procedures IFF and CIFF
• Hierarchical Planning
• Reactivity
• Plan repair
• Dialogues and Negotiation

3
Abductive Logic ProgrammingRecap from Bob
Kowalskis Slides

• Abductive Logic Programs ltP, A, ICgt have three
  components
• P is a normal logic program.
• A is a set of abducible predicates.
• IC, the set of integrity constraints.
• Often, ICs are expressed as conditionals
• If A1 ... An then B A1 ... An ? B
• or as denials
• not (A1 ... An not B) A1 ... An not B
  ? false
• Normally, P is not allowed to contain any clauses
  whose conclusion contains an abducible predicate.
  
• (This restriction can be made without loss of
  generality.)

4
ALP Semantics Recap from Bob Kowalskis Slides

• Semantics
• Given an abductive logic program,
• lt P,A,IC gt, an abductive answer for a goal G is a
  set ? of ground atoms in terms of the abducible
  predicates such that
• G holds in P ? ?
• IC holds in P ? ? or P ? ? ? IC is
  consistent.

5
Abduction is normally used to explain
observationsRecap from Bob Kowalskis Slides

• Program P Grass is wet if it rained
• Grass is wet if the sprinkler was on
• The sun was shining
• Abducible predicates A it rained, the
  sprinkler was on
• Integrity constraint
• it rained the sun was shining ? false
• Observation Grass is wet
• Two potential explanations it rained, the
  sprinkler was on
• The only explanation that satisfies the integrity
  constraint is
• the sprinkler was on.

6
ALP and Agents

• Some references Two ALP-based agent models
• R.A. Kowalski, F. Sadri, From logic programming
  towards multi-agent systems. In Annals of
  Mathematics and Artificial Intelligence Volume
  25, pages 391-419  (1999)
• Later developments of this model in later papers
  by Bob Kowalski
• A. Kakas. P. Mancarella, F. Sadri, K. Stathis, F.
  Toni. Computational logic foundations of KGP
  agents, Journal of Artificial Intelligence
  Research. To appear

7
ALP and Agents

• Agents can be seen as ALPs
• Logic Programs represent beliefs
• (more elaborate beliefs than Agent0 or
  AgentSpeak)
• Abducibles represent observations and actions
• Integrity Constraints represent
• Condition-action rules for reactivity
• Plan repair rules
• Communication policies
• Obligations and prohibitions

8
ALP and Agents

• Abductive Logic Programs used for
• Planning
• Reactivity
• Plan repair
• Negotiation

9
ALP and AgentsA small planning example

• P have(X) if buy(X)
• have(X) if borrow(X)
• A buy, borrow, register (actions)
• IC buy(X) no-money ? false
• buy(tv) ? register(tv)
• Goal have(tv)
• ?1 buy(tv) register(tv) (Plan 1)
• ?2 borrow(pc) (Plan 2)
• If P also includes no-money then
• The only solution (only plan) is ?2 borrow(pc).

10
ALP for PlanningAbductive Event Calculus (AEC)

• Some References
• Original Event Calculus
• R.A. Kowalski and M.J. Sergot (1986)
• A logic-based calculus of events. New Generation
  Computing, 4(1), 67-95.
• Abductive Event calculus
• P. Mancarella, F. Sadri, G. Terreni, F. Toni
  (2004) Planning partially for situated agents. In
  Leite, Torroni (eds.), Computational Logic in
  Multi-agent Systems, CLIMA V, Lecture Notes in
  Computer Science, Springer, 230-248.
• Also papers by M. Shanahan.

11
ALP for PlanningAbductive Event Calculus (AEC)

• Domain Independent Rules
• holds_at(F,T2) happens(A,T1), T1ltT2,
  initiates(A, T1, F),
• clipped(T1, F, T2)
• holds_at(F,T) initially(F), 0?T,
• clipped(0,F,T)
• clipped(T1,F,T2) happens(A,T),
  terminates(A,T,F), T1?TltT2

12
ALP for PlanningAbductive Event Calculus (AEC)

• holds_at(F,T2) happens(A,T1), T1ltT2,
  terminates(A, T1, F),
• declipped(T1, F, T2)
• holds_at(F,T) initially(F), 0?T,
• declipped(0,F,T)
• declipped(T1,F,T2) happens(A,T),
  initiates(A,T,F), T1?TltT2

13
AEC Domain dependent rules

• Example
• initially(have(money))
• initiates(buy(X), T, have(X) ) ? Xmoney
• initiates(borrow(X), T, have(X))
• terminates(buy(X), T, have(money) )
• precondition(buy(X), have(money))

14
AEC Domain dependent rules

• Another Example
• Actions can be communicative actions
  tell(Ag1, Ag2, Content, D)
• initially(no-info(tr-ag, arrival(tr101))
• initiates(tell(X, tr-ag, inform(Q,I),
  D),T,have-info(tr-ag,Q,I)) holds_at(trustworthy(
  X),T)
• terminates(tell(X, tr-ag, inform(Q,I),
  D),T,no-info(tr-ag,Q)) holds_at(trustworthy(X),T
  )
• precondition(tell(tr-ag,X, inform(Q,I), D),
  have-info(tr-ag,Q,I))

15
AEC

• Abducible happens
• Domain Independent Integrity Constraints
• holds_at(F,T) holds_at(?F,T) ? false
• happens(A,T) precondition(A,P)?holds_at(P,T)
• and any Domain Dependent Integrity Constraints
• For example
• holds_at(open-shop, T) ? T9 T18
• happens(tell(a, Ag, accept(R), D), T)
• happens(tell(a, Ag, refuse(R), D), T) ? false

16
ALP with Constraints

• Notice that AEC has times and constraint
  predicates, T1ltT2, T1?T2, T1T2, etc.
• We can extend the notion of abductive answer to
  cater for these, in the spirit of Constraint
  Logic Programming (CLP).
• Structure R consisting of
• a domain D(R) and
• a set of constraint predicates and
• an assignment of relations on D(R) for each such
  constraint predicate.

17
ALP with ConstraintsSemantics

• Given an ALP with constraints,
• lt P,A,IC,Rgt, an abductive answer for a goal G is
  a ?(D, C) such that
• D is in terms of the abducible predicates, and
• for all groundings ? of the variables in G, D, C
  such that ? satisfies C (according to R)
• G holds in P ? D?
• IC holds in P ? D? or P ? D? ? IC is
  consistent.

18
Back to AEC

• Given AEC and a goal G
• holds_at(g1, T1) holds_at(g2, T2)
  holds_at(gn, Tn)
• an answer for G is a parially ordered plan for
  achieving G.
• That is an answer for G is a
• ? (As, TC)
• As is a set of happens atoms, and
• TC is a set of temporal constraints, and
• ? is an abductive answer to G wrt AEC with
  constraints.

19
Example

• Domain dependent part
• initially(have(money))
• initiates(buy(X), T, have(X) ) ? Xmoney
• initiates(borrow(X), T, have(X))
• terminates(buy(X), T, have(money) )
• precondition(buy(X), have(money))
• happens(buy(X), T) ? T9 T18

20
Example cntd.

• Goal holds_at(have(pc), T) Tlt12
• One answer (plan)
• ?1 (happens(borrow(pc), T1), T1ltTlt12)
• ?2 (happens(borrow(money),T1),
• happens(buy(pc), T2)
• T1ltT2, T2?9, T2ltTlt12)

21
Example cntd.

• Goal holds_at(have(pc), T) holds_at(have(tv),
  T)
• How many answers (plans) are there ????
• Some answers are
• ?1(happens(borrow(pc),T1), happens(borrow(tv),T2)
  , T1ltT, T2ltT)
• ?2(happens(borrow(money),T1),
• happens(buy(pc), T2),
• happens(borrow(money),T3),
• happens(buy(tv), T4),
• T1ltT2, T2?9, T2 18, T2ltT3, T3ltT4, T4?9, T4 18,
  T4ltT)

22
Agent Cycle

• Messages Goals ALP
• Environment Observations
• Effects of actions

23
An ALP agent cycle with explicit time

• to cycle at time T,
• record any observations at time T,
• resume thinking,
• giving priority to forward reasoning with the
  new observations,
• evaluate to false any alternatives containing
  sub-goals that are not marked as observations but
  are atomic actions to be performed at an earlier
  time,
• select sub-goals, that are not marked as
  observations, from among those that are atomic
  actions to be performed at times consistent with
  the current time,
• attempt to perform the selected actions,
• record the success or failure of the performed
  actions and mark the records as observations,
• cycle at time T n, where n is small.
•  
• Note selecting an action involves both selecting
  an alternative branch of the search space and
  prioritising conjoint subgoals.

24
Agent Cycle

• Now consider using AEC in an agent cycle.
• We will have
• Observations
• Plan execution
• Interleaved planning and plan execution
• So we have to extend the AEC theory for this
  dynamic setting.

25
(Dynamic) AECBridge Rules

• Bridge rules for connecting the AEC theory to
  observations
• holds_at(F,T2) observed(F,T1), T1 ? T2,
• clipped(T1,F,T2)
• holds_at(F,T2) observed(F,T1), T1 ? T2,
• declipped(T1,F,T2)
• happens(A,T) executed(A,T)
• happens(A,T) observed(_, AT, _)
• happens(A, T) assume_happens(A, T)
• clipped(T1,F,T2) observed(F,T), T1?TltT2
• declipped(T1,F,T2) observed(F,T), T1?TltT2

26
(Dynamic) AEC

• Abducible assume_happens
• As well as abducibles we can also have a set of
  observables. These are fluents (or fluent
  literals) that can only be proved by being
  observed. The agent cannot plan to achieve them.
• In the KGP architecture they are called sensing
  goals.
• E.g. shop is open
• it is raining.

27
Observations

• In general observations can involve
• Observable predicates
• Abducible predicates
• Defined predicates in which case the
  observation may be explained through abductions.

28
(Dynamic) AEC

• Modify the set of domain independent integrity
  constraints
• holds_at(F,T) holds_at(?F,T) ? false
• assume_happens(A,T) precondition(A,P) ?
  holds_at(P,T)
• assume_happens(A,T)
• executed(A,T), time_now(T) ? TgtT

29
Example

• Domain dependent part
• initially(have(money))
• initiates(buy(X), T, have(X) ) ? Xmoney
• initiates(borrow(X), T, have(X))
• terminates(buy(X), T, have(money) )
• precondition(buy(X), have(money))
• precondition(buy(X), open-shop)
• observable

30
Example cntd.

• Goal holds_at(have(pc), T) Tlt12
• The agent can consider two alternative plans, to
  borrow pc
• to buy pc.
• Then an observation open-shop
• (maybe a notice saying the shop is closed all day
  or until further notice) makes the agent focus on
  the first plan.

31
Proof Procedures for Abductive Logic Programs
(with constraints)

• Some references
• CIFF
• Endriss U., Mancarella P., Sadri F., Terreni G.,
  Toni F., Abductive logic programming with CIFF
  system description.
• The CIFF proof procedure for abductive logic
  programming with constraints.
• Both in Proceedings of Jelia 2004.

32
Proof Procedures

• Endriss U., Mancarella P., Sadri F., Terreni G.,
  Toni F., The CIFF proof procedure for abductive
  logic programming with constraints theory,
  implementation and experiments. Forthcoming.
• A-System
• Kakas A., Van Nuffelen B., Denecker M., A-system
  problem solving through abduction. In Proceedings
  of the 17th Internationla Joint Conference on
  Artificial Intelligence, 2001, 591-596.

33
Proof Procedure CIFF

• Builds on earlier work by Fung and Kowalski The
  IFF proof procedure for abductive logic
  programming. Journal of Logic Programming, 1997.
• CIFF adds constraint satisfaction and a few other
  features for efficiency and extended
  applicability.
• Here we review the IFF proof procedure.

34
IFF

• Roughly speaking
• Given ALP ltP,A,ICgt
• IFF reasons forwards with IC and backwards with
  the selective completion of P (wrt
  non-abducibles).

35
IFF Selective Completion

• Example of selective completion wrt
  non-abducibles)
• P have(X) if buy(X)
• have(X) if borrow(X)
• A buy, borrow (actions)
• IC buy(X) no-money ? false
• Selective completion of P is
• have(X) iff buy(X) or borrow(X)
• no-money iff false
• i.e. the abducibles are open predicates, the rest
  are completed.
• We can also designate the observables as open
  predicates.

36
IFF Backward reasoning (Unfolding)

• Backward reasoning (Unfolding) uses
  iff-definitions to reduce atomic goals
• (and observations) to disjunctions of
  conjunctions of sub-goals.
• E.G.
• A goal have(pc) will be unfolded to
• buy(pc) or borrow(pc).

37
IFF Forward reasoning (Propagation)

• Forward reasoning (Propagation) tests and
  actively maintains consistency and the integrity
  constraints
• by matching a new observation or new atomic goal
  p
• with a condition of an implicational goal p
  q ? r
• to derive the new implicational goal q ? r.
•  
•   q can be reduced to subgoals by backward
  reasoning (unfolding)
• or can be removed by forward reasoning
  (propagation).
•  
• r is added as a new goal (after p q has been
  removed).
• r can then trigger forward reasoning
• or can be reduced to sub-goals.
•  
• r is (in general) a disjunction of conjunctions
  of sub-goals.

38
IFF Forward reasoning (Propagation)

• In the example propagation will give
• buy(pc) (no-money ? false) or borrow(pc).
• Suppose now we observe no-money.
• Another propagation step will give
• buy(pc) false or borrow(pc).

39
IFF Some Other Inference Rules

•  
• Logical equivalences replace a subformula by
  another
• formula which is both logically equivalent and
  simpler.
• These include the following equivalences used as
  rewrite rules
•  
• G true iff G G false iff false
• D or true iff true D or false iff D.
• true ? D iff D false ? D iff true
• Splitting uses distributivity to replace a
  formula of the form
•  
• (D or D') G by (D G) or (D'
  G)
•  
• Factoring unifies two atomic subgoals P(t)
  P(s)
• replacing them by the equivalent formula
•  
• P(t) st or P(t) P(s) s ?t

40
IFF - Negation

• Two versions
• Negation re-writing
• P is re-written as p ?false.
• Combining negation re-writing and negation as
  failure
• Some negative literals are marked These are
  evaluated using special inference rules that
  provide the effect of NAF.

41
IFF - Search Space

• The search space is represented by a logical
  formula,
• e. g.
• happens(borrow(money), T1) happens(buy(pc),
  T2) T1ltT2 happens(borrow(tv), T3)
  precondition(buy(pc),P) ? holds_at(P,T2)
• or
• happens(borrow(pc),T) happens(happens(borrow(t
  v),T)
• Each disjunct is analogous to an alternative
  branch of a prolog-like search space.

42
Notice - 1

• Propagation together with unfolding allows ECA or
  condition-action rule type of behaviour.
• E C ? A E C ? G
• trigger via propagation
• evaluate via propagation or unfolding
• fires the action or goal

43
Notice - 2

• Factoring allows repeating actions at a later
  stage if an earlier attempt has not been
  effective.
• Scenario e-shopping logged on with one credit
  card, choose item, then card fails because there
  are not enough funds.
• Given
• happens(logon(Card), T1) happens(logon(visa),
  5) Rest(Card)
• planned action recorded observation
• factoring obtains
• happens(logon(Card), T) Cardvisa T5
  Rest(Card) or
• happens(logon(Card), T) happens(logon(visa),
  5) (T?5 or Card?visa) Rest(Card)

44
Notice 2 cntd.

• So if Rest(Card) fails with Cardvisa T5 the
  2nd disjunct allows further attempts
• either to logon with a different card at a later
  time
• or to logon with visa at a later time
• Notice also that any work done in Rest(Card) in
  the first attempt is saved and available in the
  2nd disjunct, for example choice of item to buy.

45
Semantics

• Let D be a conjunction of definitions in iff-form
• Let G be a goal, i.e.conjunction of literals
• Let IC be a set on integrity constraints
• Let O be a conjunction of positive and negative
  observations, including actions successfully or
  unsuccessfully
• performed by the agent.
•  
• ?G ?O is an answer iff all the below hold
•  
• ?G and ?O are both conjunctions of formulae in
  the abducible and constraint (e.g.) (or
  observable) predicates
• D ?G ?O G
• D ?O O
• D ?G ?O IC.

46
AEC for Hierarchical Planning

• So far you have seen AEC formalised for planning
  from 1st principles.
• This may not be ideal in an agent setting.
• AEC lends itself to formalising plan libraries,
  macro-actions and hierarchical and progressive
  planning.
• We will look at some of these through examples.

47
AEC for Hierarchical Planning

• A Reference
• M. Shanahan, An abductive event calculus planner.
  The Journal of Logic Programming, Vol 44, 2000,
  207-239

48
AEC for Hierarchical PlanningExample (from
Shanahans paper)

• Robot mail delivery domain
• Primitive actions
• initiates(pickup(P), T, got(P)) ?
  holds_at(in(R),T), holds_at(in(P,R),T),
• initiates(putdown(P),T, in(P,R)) ?
  holds_at(in(R),T), holds_at(got(P),T)
• initiates(gothrough(D), T, in(R1)) ?
  holds_at(in(R2),T), connects(D,R2,R1)
• terminates(gothrough(D), T, in(R)) ?
  holds_at(in(R),T)

49
AEC for Hierarchical PlanningExample cntd.

• Compound action
• happens(shiftPack(P,R1,R2,R3),T1,T6) ?
  happens(goToRoom(R1,R2),T1,T2),
  clipped(T2,in(R2),T3), clipped(T1,in(P,R2),T3)
  , happens(pickup(P),T3), happens(goToRoom(R2,R3)
  ,T4,T5), clipped(T3,got(P),T6),
  clipped(T5,in(R3),T6), happened(putdown(P),T6),
  
• T2ltT3ltT4, T5ltT6

50
AEC for Hierarchical PlanningExample cntd.

• goToRoom(R1,R2)
• maintain effect
• pickup(P)
• maintain effect
• goToRoom(R2,R3)
• maintain effect
• putdown(P)

shiftPack (P, R1, R2, R3)
51
AEC for Hierarchical PlanningExample cntd.

• initiates(shiftPack(P,R1,R2,R3),T, in(P,R3)) ?
  holds_at(in(R1), T), holds_at(in(P,R2),T)
• Verifiable
• The effects of compound actions should follow
  from the effects of their sub-actions. This can
  be verified formally, or, in this case by
  inspection.

52
AEC for Hierarchical PlanningExample cntd.

• happens(goToRoom(R,R),T,T)
• happens(goToRoom(R1,R3),T1,T3) ?
  connects(D,R1,R2), happens(gothrough(D),T1),
  clipped(T1,in(R2),T2), T1ltT2
• happens(goToRoom(R2,R3),T2,T3)
• initiates(goToRoom(R1,R2),T,in(R2)) ?
  holds_at(in(R1),T)
• Conditional and recursive compound action

53
AEC for Hierarchical Planningin agent model

• Hierarchical planning in the agent model allows
• Building heuristics and expertise into planning
• Generating actions in progressive order first
  action first (as opposed to regressive order
  last action first).
• Progressive planning fits well within an agent
  cycle
• A partial plan can be executed and give useful
  results.
• Observe effect of actions and the state of the
  environment to decide whether it is worth
  continuing with that plan.

54
ALP for Reactivity

• To specify reactions to the changes/events in the
  environment, similar to condition-action rules
• To specify plan repair steps
• To specify interaction policies

55
specify reactions to the environment

• ALP allows and extends the active behaviour
  provided by ECA/Condition-Action rules and gives
  it semantics.

56
ALP for Reactivity

• Domain Dependent Integrity Constraints in the ALP
  can be specified and used to achieve reactive
  behaviour.
• Triggers, Other-Conditions ? Reaction
• Conjunction of Conjunction of
• Observations holds_at(F,T)
• Executed actions happens(A,T)
• Planned actions temporal constraints

57
Examples

• Informal syntax
• in room R at time T alarm sounds in R at T ?
  leave R at T1
• (Smart Home Ambient Intelligence AmI- See for
  example work by Augusto et al on agent-based
  ECA-based AmI)
• P leaves kitchen at T gas is on at T
• X enters kitchen by T15 ?
• raise alarm at T15

58
Plan Repair

• Most agent models that have plan repair use (ad
  hoc) ECA/Condition-Action rules for this purpose
  (see, for example 2APL/3APL).
• A reference for 3APL
• M. Dastani, B, van Riemsdijk, F. Dignum, J-J.
  Meyer, A programming language for cognitive
  agents goal directed 3APL. Proceedings of the
  First Workshop on Programming Multiagent Systems
  Languages, frameworks, techniques, and tools
  (ProMAS03) to be held at AAMAS'03, Melbourne,
  July 2003.

59
Plan Repair

• An example from 2APL
• Goto(R2,R1)X lt- not pos(R2) and pos(R3)
  goto(R3,R1)X
• An example from 3APL
• G(on(X,Y))lt- B(tooheavy(X) and heavy(Z))
  G(on(Z,Y)

60
Plan Repair

• Using ALP we can
• Formalise and use such ad hoc (active) rules for
  plan repair examples seen earlier
• Achieve the behaviour of such rules just by
  executing AEC example on next slide
• Derive such specialised plan repair rules from
  the general AEC theory and then use them
  explicitly example on slide after next
• Simulate TR-Program type of behaviour by
  manipulating the search space and search strategy
  work in progress

61
Plan Repair

• Achieve the behaviour of such rules just by
  executing AEC
• Example
• Goto(R2,R1)X lt- not pos(R2) and pos(R3)
  goto(R3,R1)X
• In AEC there will be a goal, e.g pos(r1)
  requiring an action goto(Y,r1) with a
  precondition pos(Y).

62
Plan Repair

• pos(r1)
• goto(Y,r1)
• pos(Y), goto(Y,r1)
• Yr2, clipped(pos(r2)), goto(Y,r1)
• observe(pos(r3))
• This entails clipped(pos(r2)) and pos(r3)
• Yr3, clipped(pos(r3)), goto(Y,r1)

63
Plan Repair

• Deriving specialised plan repair rules from the
  general AEC theory
• E-shopping Example using simplified notation
• logged on with one credit card, choose item, then
  card fails because there are not enough funds. We
  want to use another card, but first we have to
  logout the first card.
• Specialised plan repair rule
• logon(Card1,T1) logged-on(Card2, T2) T2ltT1
  ?logout(Card2, T3) T2ltT3ltT1
• Such a rule is derivable from AEC and
  domain-dependent rules
• precondition(logon(Card), logged-on(Card))
• terminates(logout(Card), T, logged-on(Card))

64
Plan Repair

• How? Briefly Given
• happens(logon(Card1),T1)
• holds(logged-on(Card2), T1)
• using the precondition IC
• holds(logged-on(Card2),T1) ? false
• using the F and F cannot hold together IC
• happens(logon(Card2),T2) T2ltT1 clipped(T2,
  logged-on(Card2), T1) ? false
• using definition of holds
• happens(logon(Card2),T2) T2ltT1?clipped(T2,logged
  -on(Card2),T1)
• using negation re-writing
• happens(logon(Card2),T2) T2ltT1?
  happens(logoout(Card2),T3) T2ltT3ltT1
• using definition of clipped and terminates
• So
• happens(logon(Card1),T1) happens(logon(Card2),T2
  ) T2ltT1 ? happens(logoout(Card2),T3) T2ltT3ltT1

65
ALP for negotiation

• Some references
• Sadri F., Toni F., Torroni P.
• An abductive logic programming architecture for
  negotiating agents, Jelia 02.
• Minimally intrusive negotiating agents for
  resource sharing, IJCAI 03.
• Dialogues for negotation agent varieties and
  dialogue sequences, ATAL 01.
• Endriss U., Maudet N., Sadri F., Toni F.
  Protocol conformance for logic-based agents,
  IJCAI 03.
• F. Sadri Multi-agent Cooperative Planning and
  Information Gathering, 11th International
  Workshop CIA 2007 on Cooperative Information
  Agents, September 2007, LNAI series by Springer
  Verlag.

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ALP for negotiation

• Dialogues between agents as a means of
  interaction
• Often based on fixed protocols (rules of
  interaction)
• Negotiation is one form of dialogue
• Others include (Classification of dialogues
  Walton Krabbe, 1995)
• Persuation
• Information seeking

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ALP for negotiation

• Negotiation
• Negotiation is the process by which a group of
  agents communicate with each other to try and
  come to a mutually acceptable agreement on some
  matter.
• Bussman Muller 1992
• One reason agents may negotiate is for resource
  sharing and allocation.

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ALP for negotiationGeneral Idea 1

• Agent1 has a plan requiring resources A,B. It
  has A,E, and is missing B.
• Agent2 has a plan requiring resources D,E. It
  has B,C,D, and is missing E.
• Can you give me B?
• Yes, if you give me E.

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ALP for negotiationGeneral Idea 2

• Can you give me B?
• No, why do you want B?
• I have a goal G and a plan
• bla bla and I need B for it.
• Well, You can solve G with plan bla bla
  which needs C but not B. I can give
  you C if you give me E.

70
ALP for negotiationGeneral Idea 3

• Can you give me B
• from 9 to 5?
• No, I can give it to you at 9
• but I need it back at 3.

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ALP for negotiation Communication language

• Communication language
• tell(Ag1,Ag2,Content,D)
• Content can be
• request request(give(R)) request(give(R),(Ts,
  Te)
• accept
• refuse
• promise promise(R, (T1,T2), (T3,T4))
• Change change(promise(R, (T1,T2), (T3,T4)))
• Challenge challenge(request(give(R)))
• ....

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ALP for negotiation Interaction Policies

• Expressed as integrity constraints of the form
• Pi C ? Pi1
• Dialogue move
• Intended meaning
• If the agent receives a move pi and the
  conditions C are satisfied in its KB then it
  generates a move Pi1.

73
ALP for negotiation Interaction Policies Examples

• observed(C, tell(C,a,request(R),D,T1),T)
• holds_at(have(R),T1) holds_at(need(R),T1) ?
• happens(tell(a,C,accept(request(R)),D,T1),T2)
  T5gtT2gtT
• observed(C, tell(C,a,request(R),D,T1),T)
• holds_at(need(R),T1) ?
• happens(tell(a,C,refuse(request(R)),D,T1),T2)
  T5gtT2gtT
• observed(C, tell(C,a,request(R),D,T1),T)
• holds_at(have(R),T1) ?
• happens(tell(a,C,refuse(request(R)),D,T1),T2)
  T5gtT2gtT

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Interaction Protocols

• These policies conform to the following simple
  protocol
• request accept
• refuse

75
Interaction Protocols

• request refuse
• 
  accept
• promise
• change promise
• accept promise

76
ALP for negotiation

• Considerations
• Policies conforming to protocols
• Characterising weak/strong conformance
• Properties of conversations induced by policies,
  for example
• Termination of conversations
• Success of policies in ensuring a resource
  sharing solution is found if one exists