Transcript Agents - Computational Logic

Agents
Some Examples
Fariba Sadri
Imperial College London
ICCL Summer School Dresden
August 2008
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Contents
• Teleo-Reactive agents
• Agent-0
• BDI/AgentSpeak(L)
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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
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TR-Programs
They are named sequences of conditionaction rules :
Program for Goal G:
G => nil
% i.e. do nothing
C1 => A1
C2 => A2
..
Cn => An.
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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
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Demo
http://www.robotics.stanford.edu/users/
nilsson/trweb/TRTower/TRTower_links.
html
http://www.robotics.stanford.edu/users/
nilsson/trweb/tr.html
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TR-Programs
• The Ci are tests to be evaluated on the world model.
• The Ai 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.
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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.
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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)
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TR Triple Tower Architecture
Perception
Tower
(Rules)
Model
Action
Tower
Tower
(Predicates
+ TMS)
(Action
Routines)
Sensors
Environment
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TR Triple Tower Architecture
Example
¬XOn(x,Y)
¬Holding(Y)
Clear(Y)
Clear(A)
TRProgram
On(A,B)
Holding(C)
Sensors
Environment
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TR-Programs
• The actions Ai 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.
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TR-Programs
• New Info from environment deletes old
info (TMS).
• Also forward reasoning to derive all
provable facts.
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TR-Programs
Where do TR-programs fit in within the
agent classification given in the
introduction ?
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Agent-0
• Reference:
Yoav Shoham,
Agent0: A simple agent language and its
interpreter, Proceedings AAAI-91, 1991, 704709
• One of the early multi-agent models and
programming languages.
• Fairly simple
• Motivation: partly to gain experience from
implementing an agent model
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Agent-0
Agents send messages to each other:
Inform, Request, Unrequest
A1
A2
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Agent-0 Mental State
Mental state is made up of :
• Capabilities
- fixed
• Commitment rules
- fixed
• Beliefs
- get updated
• Commitments
- get updated
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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.
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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.
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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.
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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.
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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.
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Agent0 Cycle
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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
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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.
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Agent-0 Actions
• Private
– Can be anything
• Communicative
–
–
–
–
Inform(t a fact)
Request(t, a, action)
Unrequest(t, a, action)
Refrain(action)
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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
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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 3rd
International Conference on Principles of
Knowledge Representation and Reasoning,
KRR92, Boston, 1992
R. Bordini et al, Programming MAS in
AgentSpeak using Jason, Wiley, 2007
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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)
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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
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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 subgoals and actions (similar to ECA rules).
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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
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AgentSpeak Agent Cycle
plans
generate new
intentions
desires
intentions
events
see
execute next step
action of some intention
beliefs
environment
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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
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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
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AgentSpeak Plans
Examples
+location(waste, X) :
location(robot,X) & location(bin,Y) <pick(waste);
!location(robot,Y);
drop(waste).
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AgentSpeak Plans
Examples
+location(waste, X) :
location(robot,X) & location(bin,Y) <pick(waste);
!location(robot,Y); drop(waste).
Triggering
EventAddition
of a fact
Context
Body of the plan
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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.
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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).
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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) ….
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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
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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
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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
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location(robot, X) 
current_location(robot,Y) &
¬ X=Y &
adjacent(Y,Z) &
¬ current_location(car,Z) &
move(Y,Z) &
location(robot,X).
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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 datadirected invocation of plans.”
compare with Abductive Logic
Programs
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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)
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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
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