T3 Agent oriented programming languages

Outline
1 AOP: Agent Oriented Programming
About AOP
Shortfalls
Trends
Jason
Introduction to Jason
Reasoning Cycle
Main Language Constructs: Beliefs, Goals, and Plans
Other Language Features
Comparison With Other Paradigms
The Jason Platform
Perspectives: Some Past and Future Projects
Conclusions

AOP About AOP Jason

Agent Oriented Programming

Use of mentalistic notions and a societal view of
computation [Shoham, 1993]

Heavily inﬂuence by the BDI architecture and reactive
planning systems

Various language constructs for the sophisticated
abstractions used in AOSE
Agent: Belief, Goal, Intention, Plan
Organisation: Group, Role, Norm, Interactions
Environment: Artifacts, Percepts, Actions

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Agent Oriented Programming
Features

Reacting to events × long-term goals
Course of actions depends on circumstance
Plan failure (dynamic environments)
Rational behaviour
Social ability
Combination of theoretical and practical reasoning

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Literature
Books: [Bordini et al., 2005a], [Bordini et al., 2009]
Proceedings: ProMAS, DALT, LADS, ... [Baldoni et al., 2010,
Dastani et al., 2010, Hindriks et al., 2009, Baldoni et al., 2009,
Dastani et al., 2008b, Baldoni et al., 2008, Dastani et al., 2008a,
Bordini et al., 2007a, Baldoni and Endriss, 2006,
Bordini et al., 2006b, Baldoni et al., 2006, Bordini et al., 2005b,
Leite et al., 2005, Dastani et al., 2004, Leite et al., 2004]
Surveys: [Bordini et al., 2006a], [Fisher et al., 2007] ...
Languages of historical importance: Agent0 [Shoham, 1993],
AgentSpeak(L) [Rao, 1996],
MetateM [Fisher, 2005],
3APL [Hindriks et al., 1997],
Golog [Giacomo et al., 2000]
Other prominent languages: Jason [Bordini et al., 2007b],
Jadex [Pokahr et al., 2005], 2APL [Dastani, 2008],
GOAL [Hindriks, 2009], JACK [Winikoﬀ, 2005]
But many others languages and platforms... 6 / 68

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Some Languages and Platforms

Jason (H¨bner, Bordini, ...); 3APL and 2APL (Dastani, van
u
Riemsdijk, Meyer, Hindriks, ...); Jadex (Braubach, Pokahr);
MetateM (Fisher, Guidini, Hirsch, ...); ConGoLog (Lesperance,
Levesque, ... / Boutilier – DTGolog); Teamcore/ MTDP (Milind
Tambe, ...); IMPACT (Subrahmanian, Kraus, Dix, Eiter); CLAIM
(Amal El Fallah-Seghrouchni, ...); GOAL (Hindriks); BRAHMS
(Sierhuis, ...); SemantiCore (Blois, ...); STAPLE (Kumar, Cohen,
Huber); Go! (Clark, McCabe); Bach (John Lloyd, ...); MINERVA
(Leite, ...); SOCS (Torroni, Stathis, Toni, ...); FLUX
(Thielscher); JIAC (Hirsch, ...); JADE (Agostino Poggi, ...);
JACK (AOS); Agentis (Agentis Software); Jackdaw (Calico
Jack); ...

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The State of Multi-Agent Programming

Already the right way to implement MAS is to use an AOSE
Methodology (Prometheus, Gaia, Tropos, ...) and an MAS
Programming Language!
Many agent languages have eﬃcient and stable interpreters
— used extensively in teaching
All have some programming tools (IDE, tracing of agents’
mental attitudes, tracing of messages exchanged, etc.)
Finally integrating with social aspects of MAS
Growing user base

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Some Shortfalls

IDEs and programming tools are still not anywhere near the
level of OO languages
Debugging is a serious issue — much more than “mind
tracing” is needed
Combination with organisational models is very recent —
much work still needed
Principles for using declarative goals in practical
programming problems still not “textbook”
Large applications and real-world experience much needed!

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Some Trends I

Modularity and encapsulation
Debugging MAS is hard: problems of concurrency, simulated
environments, emergent behaviour, mental attitudes
Logics for Agent Programming languages
Further work on combining with interaction, environments,
and organisations
We need to put everything together: rational agents,
environments, organisations, normative systems, reputation
systems, economically inspired techniques, etc.
Multi-Agent Programming

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Research on Multi-Agent Systems...

—
Whatever you do in MAS, make it available in a
programming language/platform for MAS!!!
—

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AgentSpeak
The foundational language for Jason

Originally proposed by Rao [Rao, 1996]
Programming language for BDI agents
Elegant notation, based on logic programming
Inspired by PRS (Georgeﬀ Lansky), dMARS (Kinny), and
BDI Logics (Rao Georgeﬀ)
Abstract programming language aimed at theoretical results

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Jason
A practical implementation of a variant of AgentSpeak

Jason implements the operational semantics of a variant of
AgentSpeak
Has various extensions aimed at a more practical
programming language (e.g. deﬁnition of the MAS,
communication, ...)
Highly customised to simplify extension and
experimentation
Developed by Jomi F. Hbner and Rafael H. Bordini

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Main Language Constructs and Runtime Structures

Beliefs: represent the information available to an agent (e.g.
about the environment or other agents)
Goals: represent states of aﬀairs the agent wants to bring
about
Plans: are recipes for action, representing the agent’s
know-how

Events: happen as consequence to changes in the agent’s
beliefs or goals
Intentions: plans instantiated to achieve some goal

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Main Architectural Components

Belief base: where beliefs are stored
Set of events: to keep track of events the agent will have to
handle
Plan library: stores all the plans currently known by the agent
Set of Intentions: each intention keeps track of the goals the
agent is committed to and the courses of action it
chose in order to achieve the goals for one of
various foci of attention the agent might have

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Jason Interpreter
Basic Reasoning cycle

perceive the environment and update belief base
process new messages
select event
select relevant plans
select applicable plans
create/update intention
select intention to execute

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Jason Rreasoning Cycle

Belief Agent
Beliefs Base
5
1 2 Events
Percepts Percepts SE Plan
perceive BUF BRF
Library

External External Selected
Events Events Events Beliefs Event

4 7 Relevant 6
Beliefs to Internal
Check Plans Unify
SocAcc Add and Events Plans
Delete Context Event

Applicable Beliefs
Plans
3
Messages Messages 8 9 Selected 10
checkMail SM Intended Intention Execute Action Actions
SO SI act
Means Intention

.send
Intentions

Push New Messages
Suspended Intentions Intentions Intention sendMsg
(Actions and Msgs) New Plan

... New
New
... Updated
Intention

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Beliefs — Representation

Syntax
Beliefs are represented by annotated literals of ﬁrst order logic

functor(term1 , ..., termn )[annot1 , ..., annotm ]

Example (belief base of agent Tom)
red(box1)[source(percept)].
friend(bob,alice)[source(bob)].
lier(alice)[source(self),source(bob)].
˜lier(bob)[source(self)].

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Beliefs — Dynamics I

by perception
beliefs annotated with source(percept) are automatically updated
accordingly to the perception of the agent

by intention
the plan operators + and - can be used to add and remove
beliefs annotated with source(self) (mental notes)

+lier(alice); // adds lier(alice)[source(self)]
-lier(john); // removes lier(john)[source(self)]

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Beliefs — Dynamics II

by communication
when an agent receives a tell message, the content is a new belief
annotated with the sender of the message

.send(tom,tell,lier(alice)); // sent by bob
// adds lier(alice)[source(bob)] in Tom’s BB
...
.send(tom,untell,lier(alice)); // sent by bob
// removes lier(alice)[source(bob)] from Tom’s BB

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Goals — Representation

Types of goals
Achievement goal: goal to do
Test goal: goal to know

Syntax
Goals have the same syntax as beliefs, but are preﬁxed by
! (achievement goal) or
? (test goal)

Example (Initial goal of agent Tom)
!write(book).

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Goals — Dynamics I

by intention
the plan operators ! and ? can be used to add a new goal
annotated with source(self)

...
// adds new achievement goal !write(book)[source(self)]
!write(book);

// adds new test goal ?publisher(P)[source(self)]
?publisher(P);
...

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Goals — Dynamics II

by communication – achievement goal
when an agent receives an achieve message, the content is a new
achievement goal annotated with the sender of the message

.send(tom,achieve,write(book)); // sent by Bob
// adds new goal write(book)[source(bob)] for Tom
...
.send(tom,unachieve,write(book)); // sent by Bob
// removes goal write(book)[source(bob)] for Tom

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Goals — Dynamics III

by communication – test goal
when an agent receives an askOne or askAll message, the
content is a new test goal annotated with the sender of the
message

.send(tom,askOne,published(P),Answer); // sent by Bob
// adds new goal ?publisher(P)[source(bob)] for Tom
// the response of Tom will unify with Answer

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Triggering Events — Representation

Events happen as consequence to changes in the agent’s
beliefs or goals
An agent reacts to events by executing plans
Types of plan triggering events
+b (belief addition)
-b (belief deletion)
+!g (achievement-goal addition)
-!g (achievement-goal deletion)
+?g (test-goal addition)
-?g (test-goal deletion)

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Plans — Representation

An AgentSpeak plan has the following general structure:

triggering event : context ¡- body.

where:
the triggering event denotes the events that the plan is
meant to handle
the context represent the circumstances in which the plan
can be used
the body is the course of action to be used to handle the
event if the context is believed true at the time a plan is
being chosen to handle the event

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Plans — Operators for Plan Context

Boolean operators Arithmetic operators
(and) + (sum)
| (or) - (subtraction)
not (not) * (multiply)
= (uniﬁcation) / (divide)
, = (relational) div (divide – integer)
, = (relational) mod (remainder)
== (equals) ** (power)
== (diﬀerent)

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Plans — Operators for Plan Body
A plan body may contain:
Belief operators (+, -, -+)
Goal operators (!, ?, !!)
Actions (internal/external) and Constraints
Example (plan body)
+rain : time to leave(T) clock.now(H) H ¿= T
¡- !g1; // new sub-goal
!!g2; // new goal
?b(X); // new test goal
+b1(T-H); // add mental note
-b2(T-H); // remove mental note
-+b3(T*H); // update mental note
jia.get(X); // internal action
X ¿ 10; // constraint to carry on
close(door).// external action
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Plans — Example

+green patch(Rock)[source(percept)]
: not battery charge(low)
¡- ?location(Rock,Coordinates);
!at(Coordinates);
!examine(Rock).
+!at(Coords)
: not at(Coords) safe path(Coords)
¡- move towards(Coords);
!at(Coords).
+!at(Coords)
: not at(Coords) not safe path(Coords)
¡- ...
+!at(Coords) : at(Coords).

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Plans — Dynamics

The plans that form the plan library of the agent come from
initial plans deﬁned by the programmer
plans added dynamically and intentionally by
.add plan
.remove plan
plans received from
tellHow messages
untellHow

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Strong Negation

Example
+!leave(home)
: ˜raining
¡- open(curtains); ...

+!leave(home)
: not raining not ˜raining
¡- .send(mum,askOne,raining,Answer,3000); ...

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Prolog-like Rules in the Belief Base

Example
likely color(Obj,C) :-
colour(Obj,C)[degOfCert(D1)]
not (colour(Obj, )[degOfCert(D2)] D2 ¿ D1)
not ˜colour(C,B).

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Plan Annotations

Like beliefs, plans can also have annotations, which go in
the plan label
Annotations contain meta-level information for the plan,
which selection functions can take into consideration
The annotations in an intended plan instance can be changed
dynamically (e.g. to change intention priorities)
There are some pre-deﬁned plan annotations, e.g. to force a
breakpoint at that plan or to make the whole plan execute
atomically

Example (an annotated plan)
@myPlan[chance of success(0.3), usual payoff(0.9),
any other property]
+!g(X) : c(t) ¡- a(X).

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Failure Handling: Contingency Plans

Example (an agent blindly committed to g)
+!g : g.

+!g : ... ¡- ... ?g.

-!g : true ¡- !g.

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“Higher-Order” Variables

Example (an agent that asks for plans on demand)
-!G[error(no relevant)] : teacher(T)
¡- .send(T, askHow, { +!G }, Plans);
.add plan(Plans);
!G.

in the event of a failure to achieve any goal G due to no
relevant plan, asks a teacher for plans to achieve G and
then try G again

The failure event is annotated with the error type, line,
source, ... error(no relevant) means no plan in the agent’s
plan library to achieve G
{ +!G } is the syntax to enclose triggers/plans as terms
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Internal Actions

Unlike actions, internal actions do not change the
environment
Code to be executed as part of the agent reasoning cycle
AgentSpeak is meant as a high-level language for the agent’s
practical reasoning and internal actions can be used for
invoking legacy code elegantly

Internal actions can be deﬁned by the user in Java

libname.action name(. . .)

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Standard Internal Actions

Standard (pre-deﬁned) internal actions have an empty library
name
.print(term1 , term2 , . . .)
.union(list1 , list2 , list3 )
.my name(var )
.send(ag,perf ,literal)
.intend(literal)
.drop intention(literal)

Many others available for: printing, sorting, list/string
operations, manipulating the beliefs/annotations/plan library,
creating agents, waiting/generating events, etc.

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Jason × Java I

Consider a very simple robot with two goals:
when a piece of gold is seen, go to it
when battery is low, go charge it

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Jason × Java II

Example (Java code – go to gold)
public class Robot extends Thread {
boolean seeGold, lowBattery;
public void run() {
while (true) {
while (! seeGold) {
}
while (seeGold) {
a = selectDirection();

doAction(go(a));

} } } }

(how to code the charge battery behaviour?)

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Jason × Java III

Example (Java code – charge battery)
public class Robot extends Thread {
boolean seeGold, lowBattery;
public void run() {
while (true) {
while (! seeGold)
if (lowBattery) charge();
while (seeGold) {
a = selectDirection ();
doAction(go(a));
} } } }

(note where the tests for low battery have to be done)

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Jason × Java IV
Example (Jason code)
+see(gold)
¡- !goto(gold).
+!goto(gold) :see(gold) // long term goal
¡- !select direction(A);
go(A);
!goto(gold).
+battery(low) // reactivity
¡- !charge.

ˆ!charge[state(started)] // goal meta-events
¡- .suspend(goto(gold)).
ˆ!charge[state(finished)]
¡- .resume(goto(gold)).

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Jason × Prolog

With the Jason extensions, nice separation of theoretical and
practical reasoning

BDI architecture allows
long-term goals (goal-based behaviour)
reacting to changes in a dynamic environment
handling multiple foci of attention (concurrency)

Acting on an environment and a higher-level conception of a
distributed system

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Communication Infrastructure

Various communication and execution management
infrastructures can be used with Jason:
Centralised: all agents in the same machine,
one thread by agent, very fast
Centralised (pool): all agents in the same machine,
ﬁxed number of thread,
allows thousands of agents
Jade: distributed agents, FIPA-ACL
Saci: distributed agents, KQML
... others deﬁned by the user (e.g. AgentScape)

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Deﬁnition of a Simulated Environment

There will normally be an environment where the agents are
situated
The agent architecture needs to be customised to get
perceptions and act on such environment
We often want a simulated environment (e.g. to test an
MAS application)
This is done in Java by extending Jason’s Environment class

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Interaction with the Environment Simulator

Environment Agent Reasoner
Simulator Architecture
perceive
getPercepts

act
executeAction

change
percepts

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Example of an Environment Class
1 import jason.*;
2 import ...;
3 public class robotEnv extends Environment {
4 ...
5 public robotEnv() {
6 Literal gp =
7 Literal.parseLiteral(”green˙patch(souffle)”);
8 addPercept(gp);
9 }
10
11 public boolean executeAction(String ag, Structure action) {
12 if (action.equals(...)) {
13 addPercept(ag,
14 Literal.parseLiteral(”location(souffle,c(3,4))”);
15 }
16 ...
17 return true;
18 } }
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MAS Conﬁguration Language I

Simple way of deﬁning a multi-agent system
Example (MAS that uses JADE as infrastructure)
MAS my˙system –
infrastructure: Jade
environment: robotEnv
agents:
c3po;
r2d2 at jason.sourceforge.net;
bob #10; // 10 instances of bob
classpath: ”../lib/graph.jar”;
˝

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MAS Configuration Language II

Configuration of event handling, frequency of perception,
user-defined settings, customisations, etc.
Example (MAS with customised agent)
MAS custom –
agents: bob [verbose=2,paramters=”sys.properties”]
agentClass MyAg
agentArchClass MyAgArch
beliefBaseClass jason.bb.JDBCPersistentBB(
”org.hsqldb.jdbcDriver”,
”jdbc:hsqldb:bookstore”,
...
˝

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MAS Conﬁguration Language III

Example (CArtAgO Environment)
MAS grid˙world –

environment: alice.c4jason.CEnv

agents:
cleanerAg
agentArchClass alice.c4jason.CogAgentArch
#3;
˝

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Jason Customisations

Agent class customisation:
selectMessage, selectEvent, selectOption, selectIntetion, buf,
brf, ...

Agent architecture customisation:
perceive, act, sendMsg, checkMail, ...

Belief base customisation:
add, remove, contains, ...
Example available with Jason: persistent belief base (in text
ﬁles, in data bases, ...)

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jEdit Plugin

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Eclipse Plugin

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Mind Inspector

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Some Related Projects I

Speech-act based communication
´
Joint work with Renata Vieira, Alvaro Moreira, and Mike
Wooldridge
Cooperative plan exchange
Joint work with Viviana Mascardi, Davide Ancona
Plan Patterns for Declarative Goals
Joint work with M.Wooldridge
Planning (Felipe Meneguzzi and Colleagues)
Web and Mobile Applications (Alessandro Ricci and
Colleagues)
Belief Revision
Joint work with Natasha Alechina, Brian Logan, Mark Jago

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Some Related Projects II

Ontological Reasoning
´
Joint work with Renata Vieira, Alvaro Moreira
JASDL: joint work with Tom Klapiscak
Goal-Plan Tree Problem (Thangarajah et al.)
Joint work with Tricia Shaw
Trust reasoning (ForTrust project)
Agent veriﬁcation and model checking
Joint project with M.Fisher, M.Wooldridge, W.Visser,
L.Dennis, B.Farwer

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Some Related Projects III

Environments, Organisation and Norms
Normative environments
Join work with A.C.Rocha Costa and F.Okuyama
MADeM integration (Francisco Grimaldo Moreno)
Normative integration (Felipe Meneguzzi)
CArtAgO integration
Moise integration
More on jason.sourceforge.net, related projects

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Some Trends for Jason I

Modularity and encapsulation
Capabilities (JACK, Jadex, ...)
Roles (Dastani et al.)
Mini-agents (?)
Recently done: meta-events
To appear soon: annotations for declarative goals,
improvement in plan failure handling, etc.
Debugging is hard, despite mind inspector, etc.
Further work on combining with environments and
organisations

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Summary

AgentSpeak
Logic + BDI
Agent programming language
Jason
AgentSpeak interpreter
Implements the operational semantics of AgentSpeak
Speech-act based communicaiton
Highly customisable
Useful tools
Open source
Open issues

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Acknowledgements

Many thanks to the
Various colleagues acknowledged/referenced throughout
these slides
Jason users for helpful feedback
CNPq for supporting some of our current researh

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Further Resources

http://jason.sourceforge.net

R.H. Bordini, J.F. H¨bner, and
u
M. Wooldrige
Programming Multi-Agent Systems
in AgentSpeak using Jason
John Wiley Sons, 2007.

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Bibliography I

Baldoni, M., Bentahar, J., van Riemsdijk, M. B., and Lloyd, J., editors (2010).
Declarative Agent Languages and Technologies VII, 7th International
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and Invited Papers, volume 5948 of Lecture Notes in Computer Science.
Springer.
Baldoni, M. and Endriss, U., editors (2006).
Declarative Agent Languages and Technologies IV, 4th International Workshop,
DALT 2006, Hakodate, Japan, May 8, 2006, Selected, Revised and Invited
Papers, volume 4327 of Lecture Notes in Computer Science. Springer.
Baldoni, M., Endriss, U., Omicini, A., and Torroni, P., editors (2006).
Declarative Agent Languages and Technologies III, Third International
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(2008).
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Bibliography II

Baldoni, M., Son, T. C., van Riemsdijk, M. B., and Winikoﬀ, M., editors
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o
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Bibliography III
(2005b).
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2004, New York, NY, USA, July 20, 2004 Selected Revised and Invited Papers,
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of Lecture Notes in Computer Science. Springer.
(2009).
Multi-Agent Programming: Languages, Tools and Applications.
Springer.

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Bibliography IV

Bordini, R. H., H¨bner, J. F., and Wooldridge, M. (2007b).
u
Programming Multi-Agent Systems in AgentSpeak Using Jason.
Wiley Series in Agent Technology. John Wiley Sons.
Dastani, M. (2008).
2apl: a practical agent programming language.
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Dastani, M., Dix, J., and Fallah-Seghrouchni, A. E., editors (2004).
Programming Multi-Agent Systems, First International Workshop, PROMAS
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Dastani, M., Fallah-Seghrouchni, A. E., Leite, J., and Torroni, P., editors
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Revised Selected Papers, volume 5118 of Lecture Notes in Computer Science.
Springer.

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Dastani, M., Fallah-Seghrouchni, A. E., Leite, J., and Torroni, P., editors
(2010).
Languages, Methodologies, and Development Tools for Multi-Agent Systems,
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Metatem: The story so far.
In [Bordini et al., 2006b], pages 3–22.

Fisher, M., Bordini, R. H., Hirsch, B., and Torroni, P. (2007).
Computational logics and agents: A road map of current technologies and
future trends.
Computational Intelligence, 23(1):61–91.

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AOP About AOP Jason

Bibliography VI

Giacomo, G. D., Lesp´rance, Y., and Levesque, H. J. (2000).
e
Congolog, a concurrent programming language based on the situation calculus.
Artif. Intell., 121(1-2):109–169.

Hindriks, K. V. (2009).
Programming rational agents in GOAL.
In [Bordini et al., 2009], pages 119–157.

Hindriks, K. V., de Boer, F. S., van der Hoek, W., and Meyer, J.-J. C. (1997).
Formal semantics for an abstract agent programming language.
In Singh, M. P., Rao, A. S., and Wooldridge, M., editors, ATAL, volume 1365
of Lecture Notes in Computer Science, pages 215–229. Springer.
Hindriks, K. V., Pokahr, A., and Sardi˜a, S., editors (2009).
n
2008, Estoril, Portugal, May 13, 2008. Revised Invited and Selected Papers,
Leite, J. A., Omicini, A., Sterling, L., and Torroni, P., editors (2004).
Declarative Agent Languages and Technologies, First International Workshop,
DALT 2003, Melbourne, Australia, July 15, 2003, Revised Selected and Invited

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Bibliography VII

Leite, J. A., Omicini, A., Torroni, P., and Yolum, P., editors (2005).
Declarative Agent Languages and Technologies II, Second International
Workshop, DALT 2004, New York, NY, USA, July 19, 2004, Revised Selected
Pokahr, A., Braubach, L., and Lamersdorf, W. (2005).
Jadex: A bdi reasoning engine.
In [Bordini et al., 2005a], pages 149–174.

Rao, A. S. (1996).
Agentspeak(l): Bdi agents speak out in a logical computable language.
In de Velde, W. V. and Perram, J. W., editors, MAAMAW, volume 1038 of
Lecture Notes in Computer Science, pages 42–55. Springer.
Shoham, Y. (1993).
Agent-oriented programming.
Artif. Intell., 60(1):51–92.

Winikoﬀ, M. (2005).
Jack intelligent agents: An industrial strength platform.
In [Bordini et al., 2005a], pages 175–193.

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Overview Other APLs
Architectural Considerations

Koen Hindriks, João Leite PL-MAS Tutorial – EASSS12

Overview
• Overview of APL landscape
• Some Architectural Considerations
• Research Themes
• References

Koen Hindriks, João Leite
K. Hindriks, J. PL-MAS Tutorial – EASSS09
EASSS12

Agent Programming
Languages: An Overview


A Brief History of AOP
• 1990: AGENT-0 (Shoham)
• 1993: PLACA (Thomas; AGENT-0 extension with plans)
• 1996: AgentSpeak(L) (Rao; inspired by PRS)
• 1996: Golog (Reiter, Levesque, Lesperance)
• 1997: 3APL (Hindriks et al.)
• 1998: ConGolog (Giacomo, Levesque, Lesperance)
• 2000: JACK (Busetta, Howden, Ronnquist, Hodgson)
• 2000: GOAL (Hindriks et al.)
• 2000: CLAIM (Amal El FallahSeghrouchni)
• 2002: Jason (Bordini, Hubner; implementation of AgentSpeak)
• 2003: Jadex (Braubach, Pokahr, Lamersdorf)
• 2008: 2APL (successor of 3APL)
This overview is far from complete!
EASSS12

• AGENT-0 Speech acts
• PLACA Plans
• AgentSpeak(L) Events/Intentions
• Golog Action theories, logical specification
• 3APL Practical reasoning rules
• JACK Capabilities, Java-based
• GOAL Declarative goals
• CLAIM Mobile agents (within agent community)
• Jason AgentSpeak + Communication
• Jadex JADE + BDI
• 2APL Modules, PG-rules, …

EASSS12

Agent Programming Languages and Agent Logics have not (yet)
converged to a uniform conception of (rational) agents.

Agent Programming Agent Logics

Architectures BDI, Intention Logic, KARO
PRS (Planning) , InterRap

Agent-Oriented Programming Multi-Agent Logics, Norms,
Agent0, AgentSpeak, ConGolog,
Collective Intentionality
3APL/2APL, Jason, Jadex, JACK, …

Conceptual extension
CASL, Games and Knowledge
“Declarative Goals”

EASSS12

Part 1: BDI Agents and AOP

Agent Features
Many diverse and different features have been proposed, but the
unifying theme still is the BDI view of agents.

Agent Programming Agent Logics
• “Simple” beliefs and belief • “Complex” beliefs and belief
revision revision
• Planning and Plan revision • Commitment Strategies
e.g. Plan failure • Goal Dynamics
• Declarative Goals • Look ahead features
• Triggers, Events e.g. beliefs about the future,
e.g. maintenance goals strong commitment
• Control Structures preconditions
• … • Norms
• …

EASSS12

How are these APLs related?
A comparison from a high-level, conceptual point, not taking into account
any practical aspects (IDE, available docs, speed, applications, etc)

Family of Languages Multi-Agent Systems
Basic concepts: beliefs, action, plans, goals-to-do): All of these languages
(except AGENT-0,
AgentSpeak(L), Jason1 PLACA, JACK) have
AGENT-01 versions implemented
(PLACA ) “on top of” JADE.
Golog 3APL2

Main addition: Declarative goals
2APL 3APL + GOAL

Java-based BDI Languages Mobile Agents
Jack (commercial), Jadex CLAIM3
1 mainly interesting from a historical point of view
2 from a conceptual point of view, we identify AgentSpeak(L) and Jason
3 without practical reasoning rules
4 another example not discussed here is AgentScape (Brazier et al.)

EASSS12

Some Architectural
Considerations


GOAL Architecture

Agent

Beliefs
percept Process Action action
percepts Rules
Goals

Environment
Real or simulated
world of events

EASSS12

Interpreters: GOAL
Process
percepts
(= apply percept rules)
Also called
deliberation cycles.
Select action
(= apply action rules)
GOAL’s cycle is a classic
sense-plan-act cycle.
Perform action
(= send to environment)

Update
mental state
(= apply action specs +
commitment strategy)

EASSS12

Interpreter: 2APL

EASSS12

Under the Hood: Implementing AOP
Example: GOAL Architecture

Plugins

EASSS12

2APL IDE: Introspector

EASSS12

2APL IDE: State Tracer

EASSS12

Research Themes

A Personal Point of View


Part 3: Short-term Future

A Research Agenda
Fundamental research questions:
• What kind of expressiveness* do we need in AOP? Or, what needs
to be improved from your point of view? We need your feedback!
• Verification: Use e.g. temporal logic combined with belief and goal
operators to prove agents “correct”. Model-checking agents, mas(!)

Short-term important research questions:
• Planning: Combining reactive, autonomous agents and planning.
• Learning: How can we effectively integrate e.g. reinforcement
learning into AOP to optimize action selection?
• Debugging: Develop tools to effectively debug agents, mas(!).
Raises surprising issues: Do we need agents that revise their plans?
• Organizing MAS: What are effective mas structures to organize
communication, coordination, cooperation?
• Last but not least, (your?) applications!
* e.g. maintenance goals, preferences, norms, teams, ...
EASSS12

Combining AOP and Planning
Combining the benefits of reactive, autonomous agents and planning algorithms

GOAL Planning
• Knowledge • Axioms

• Beliefs • (Initial) state

• Goals • Goal description

• Program Section • x

• Action Specification • Plan operators

Alternative KRT Plugin:
Restricted FOL, ADL, Plan Constraints (PDDL)
EASSS12

Part 3: Short-term Future

Applications
Need to apply the AOP to find out what works and what doesn’t

• Use APLs for Programming Robotics Platform

• Many other possible applications:
• (Serious) Gaming (e.g. RPG, crisis management, …)
• Agent-Based Simulation
• The Web
• add your own example here

EASSS12

References
• 2APL: http://www.cs.uu.nl/2apl/
• ConGolog: http://www.cs.toronto.edu/cogrobo/main/systems/index.html
• GOAL: http://mmi.tudelft.nl/~koen/goal
• JACK: http://en.wikipedia.org/wiki/JACK_Intelligent_Agents
• Jadex: http://jadex.informatik.uni-hamburg.de/bin/view/About/Overview
• Jason: http://jason.sourceforge.net/JasonWebSite/Jason Home.php

• Multi-Agent Programming Languages, Platforms and Applications,
Bordini, R.H.; Dastani, M.; Dix, J.; El Fallah Seghrouchni, A. (Eds.),
2005
introduces 2APL, CLAIM, Jadex, Jason
• Multi-Agent Programming: Languages, Tools and Applications
Bordini, R.H.; Dastani, M.; Dix, J.; El Fallah Seghrouchni, A. (Eds.),
2009
introduces a.o.: Brahms, CArtAgO, GOAL, JIAC Agent Platform

EASSS12

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T3 Agent oriented programming languages

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