Koen HindriksMulti-Agent Systems 2012 Introduction Agent Programming Koen Hindriks Delft University...

Koen Hindriks Multi-Agent Systems 2012

IntroductionAgent Programming

Koen HindriksDelft University of Technology, The Netherlands

Learning to program teaches you how to think.

Computer science is a liberal art.

Steve Jobs

Multi-Agent Systems Course

Koen Hindriks Multi-Agent Systems 2012 2 Koen Hindriks Multi-Agent Systems 2013 2

Introducing Myself• Dr. Koen Hindriks

– PhD on Agent Programming Languages (Utrecht, 1996 - 2000)– Consultant at Accenture (Amsterdam, 2000 - 2005)– Assistant Professor Artificial Intelligence (Nijmegen, 2005 - 2006)– Assistant Professor MMI Group (Delft, 2006 - …)

• Main Research Interests– Rational agent programming and theory– Automated negotiation– Logic (with applications to agent modeling and NLP semantics)– Cognitive engineering and architectures


Outline

• This lecture:

– Blocks, Blocks, Blocks…

Basic Blocks World example in

the GOAL agent programming language

– Actions

– Rule-Based Action Selection


This Part: BW4T Assignment

• Abstract simulated environment called: Blocks World for Teams (BW4T)

• Search and retrieval task

• Control robot by cognitive agent

program

• Your task: implement decision-

making

Koen Hindriks Multi-Agent Systems 2012

The GOALAgent Programming Language

Koen Hindriks Multi-Agent Systems 2012 6 Koen Hindriks Multi-Agent Systems 2012

THE BLOCKS WORLDThe Hello World example of Agent Programming


The Blocks World

• Positioning of blocks on table is not relevant.• A block can be moved only if it there is no other block on top of it.

Objective: Move blocks in initial state such that result is goal state.

A classic AI planning problem.


Mental State of GOAL Agent

knowledge{ block(X) :- on(X, _). clear(X) :- block(X), not(on(Y,X)). clear(table). tower([X]) :- on(X,table). tower([X,Y|T]) :- on(X,Y), tower([Y|T]).}beliefs{ on(a,b). on(b,c). on(c,table). on(d,e). on(e,table). on(f,g). on(g,table).}goals{ on(a,e), on(b,table), on(c,table), on(d,c), on(e,b), on(f,d), on(g,table).}

The knowledge, belief, and goal sections together constitute the specification of the mental state of a GOAL Agent.

Initial mental state of agent


Previous Lecture

Percepts

Action

events

actions goals

plans

beliefs

environment

agent


This Lecture

Percepts

Action

events

actions goals

plans

beliefs

environment

agent


Next Lecture

Percepts

Action

events

actions goals

plans

beliefs

environment

agent


Why “Hello World” Example?

• The Blocks World is:– Single agent:

no other agents that can change the world– Fully observable:

the agent can see “everything”– Static:

only actions of agent can change the world

We do not need sensing / percepts.


Agent Oriented Programming• Agents provide a very effective way of building

applications for dynamic and complex environments

+• Develop agents based on Belief-Desire-Intention

agent metaphor, i.e. develop software components as if they have beliefs and goals, act to achieve these goals, and are able to interact with their environment and other agents.


ACTIONS SPECIFICATIONSChanging Blocks World Configurations


Actions Change the Environment…

move(a,d)


and Require Updating Mental States.• To ensure adequate beliefs after performing an action the belief base

needs to be updated (and possibly the goal base).

– Add effects to belief base: insert on(a,d) after move(a,d).– Delete old beliefs: delete on(a,b) after move(a,d).


and Require Updating Mental States.• If a goal has been (believed to be) completely achieved, the goal is

removed from the goal base.

• It is not rational to have a goal you believe to be achieved.• Default update implements a blind commitment strategy.

move(a,b)

beliefs{ on(a,table), on(b,table).}goals{ on(a,b), on(b,table).}

beliefs{ on(a,b), on(b,table).}goals{ }


Action Specifications• Actions in GOAL have preconditions and

postconditions.• Executing an action in GOAL means:

– Preconditions are conditions that need to be true:• Check preconditions on the belief base.

– Postconditions (effects) are add/delete lists (STRIPS):• Add positive literals in the postcondition• Delete negative literals in the postcondition

• STRIPS-style specificationmove(X,Y){ pre { clear(X), clear(Y), on(X,Z), not( on(X,Y) ) } post { not(on(X,Z)), on(X,Y) }}


move(X,Y){

pre { clear(X), clear(Y), on(X,Z), not( on(X,Y) ) }

post { not(on(X,Z)), on(X,Y) }

}

Example: move(a,b)• Check: clear(a), clear(b), on(a,Z), not( on(a,b) )• Remove: on(a,Z)• Add: on(a,b)

Note: first remove, then add.

Actions Specifications

table


move(X,Y){



}

Example: move(a,b)

Actions Specifications

beliefs{ on(a,table). on(b,table).}

beliefs{ on(b,table). on(a,b).}


move(X,Y){



}

1. Is it possible to perform move(a,b)?

2. Is it possible to perform move(a,d)?

Actions SpecificationsEXERCISE:

knowledge{ block(a), block(b), block(c), block(d), block(e), block(f), block(g), block(h), block(i). clear(X) :- block(X), not(on(Y,X)). clear(table). tower([X]) :- on(X,table). tower([X,Y|T]) :- on(X,Y), tower([Y|T]).}beliefs{ on(a,b). on(b,c). on(c,table). on(d,e). on(e,table). on(f,g). on(g,table).}

No, clear(b) fails. Yes.


move(X,Y){



}

We already check clear(Y). Why do we also have not(on(X,Y)) in the precondition?

Actions SpecificationsEXERCISE:

Since we always have clear(table). The conditionprevents moving blocks that are already on the table.

knowledge{ block(a), block(b), block(c), block(d), block(e), block(f), block(g), block(h), block(i). clear(X) :- block(X), not(on(Y,X)). clear(table). tower([X]) :- on(X,table). tower([X,Y|T]) :- on(X,Y), tower([Y|T]).}beliefs{ on(a,b). on(b,c). on(c,table). on(d,e). on(e,table). on(f,g). on(g,table).}


ACTION RULESSelecting actions to perform


Agent-Oriented Programming

• How do humans choose and/or explain actions?

• Examples:• I believe it rains; so, I will take an umbrella with me.• I go to the video store because I want to rent I-robot.• I don’t believe busses run today so I take the train.

• Use intuitive common sense concepts:

beliefs + goals => action

See Chapter 1 of the Programming Guide


Selecting Actions: Action Rules

• Action rules are used to define a strategy for action selection.

• Defining a strategy for blocks world:– If constructive move can be made, make it.– If block is misplaced, move it to table.

program{ if bel(tower([Y|T])), a-goal(tower([X,Y|T])) then move(X,Y). if a-goal(tower([X|T])) then move(X,table).}



What happens?

•Check whether condition

a-goal(tower([X|T])

can be derived from the current mental state of agent.

•If so, then apply rule and perform move(X,table).

if a-goal(tower([X|T])) then move(X,table).



What happens?if a-goal(tower([X|T])) then move(X,table).

knowledge{ block(X) :- on(X, _). clear(X) :- block(X), not(on(Y,X)). clear(table). tower([X]) :- on(X, table). tower([X,Y|T]) :- on(X,Y), tower([Y|T]).}beliefs{ on(a, b). on(c, d).}goals{ on(a, table), on(c, table).}

d

c

b

a

EXERCISE:

Random choice for either moving a or c.


Order of Action Rules

• Action rules are executed by default in linear order.• The first rule that fires is executed.

• Default order can be changed to random.• Arbitrary rule that is able to fire may be selected.

program{ if bel(tower([Y|T])), a-goal(tower([X,Y|T])) then move(X,Y). if a-goal(tower([X|T])) then move(X,table).}

program[order=random]{ if bel(tower([Y|T])), a-goal(tower([X,Y|T])) then move(X,Y). if a-goal(tower([X|T])) then move(X,table).}


Example Program: Action RulesAgent program may allow for multiple action choices

dTo table

Random, arbitrary choice

program[order=random]{ if bel(tower([Y|T])), a-goal(tower([X,Y|T])) then move(X,Y). if a-goal(tower([X|T])) then move(X,table).}


Organisation• Read Programming Guide Ch1-3 (+ User Manual)

See: http://ii.tudelft.nl/trac/goal#Documentation

• Tutorial:– Download GOAL: See http://ii.tudelft.nl/trac/goal– Practice exercises from Programming Guide– BW4T assignments 3 and 4 made available today

• Next lecture:– Sensing, perception, environments– Other types of rules & macros– Agent architecture

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