february 24th 2000peter demeester - philippe de pauw 1 object oriented agents for distributed...

February 24th 2000 Peter Demeester - Philippe De Pauw1

ObjeCt oriented Agents for distributed PlannIng

systems

OCAPI-COALA team

February 24th 2000Peter Demeester - Philippe De Pauw2

Overview

Presentation OCAPI – COALA Goal Description of the sample problem Representation of actors by agents Co-ordination techniques

– Blackboard– CNP

Results Demo What’s next: COALA


Presentation


Presentation OCAPI – COALA (1)

Team members:– Patrick De Causmaecker– Peter Demeester– Philippe De Pauw-Waterschoot– Greet Vanden Berghe

URL:– OCAPI: http://www2.kahosl.be/~ocapi/– COALA: http://coala.tsx.org


Presentation (2)

The OCAPI project is sponsored by IWT (=Institute for the Promotion of Innovation by Science and Technology in Flanders)in the framework of the ‘HOBUfonds’.

The agent software (Correlate) used was part of a project at the KU Leuven (Academic Partner).

Concurrent object-ORiented REflective LAnguage TEchnology: extension of an OO-programming language that offers support for distributed applications.


Hobufonds

IWTUser committee

Academic Partners

Public

OCAPI project


Presentation (3)

OCAPI Alcatel e-com De Clercq Engineering Impakt KU Leuven MediaGenix Nimble

COALA Alcatel e-com De Clercq Engineering MediaGeniX Nimble SmartMove Starlab TNI Universiteit Maastricht University of Nottingham

User Committee & Academic Partners


Goal of the OCAPI project


Goal of the OCAPI project

Show the usefulness of the intelligent agent paradigm in design & implementation.

Can agents be used to speed up the software development, to increase the maintenance & can SMEs (‘KMO’) take advantage of it?


Description of the sample problem


Lab session planning

example to demonstrate the characteristics of agents: dynamic planning of lab sessions in a polytechnic.

negotiations between users are necessary: ideal for an agent approach.


The problem

there already exists a lab plan this plan changes frequently due to

– lab room is unavailable– teacher is unavailable (ill, holiday,…)

Real life: Adaptive lab session planning: hard problem

•fast decision taking

•many parties are involved


The problem

•Central (human) scheduler: experienced, he knows how teachers will react on a rescheduling & anticipates their reactions.•Selects those solutions which he knows will make a chance of success.


An example

Teacher A ,Teacher B Teacher A: ill on Wednesday, cannot give lab

session on Wednesday afternoon Teacher B: gives a lab session on Friday

afternoon to the same class group in the same lab room

Possible solution: switch session of teacher A with session of teacher B

A B


Example

Central scheduler Are you willing to switch with your colleague AAnswer of Teacher B can depend on a lot of personal things:

– He cannot stand teacher A– He promised his wife to take care of the kids on

Wednesday afternoon and so on

Question: How can we automate this process?

Teacher B


Representation of actors by agents


Types of agents (RETSINA)

Information agents: system directs his questions to this kind of agent, this decreases dependency on the database structure

Task agents: can implement the algorithm Interface agents: realise the interaction

between users of the system


Types of agents (RETSINA)

Interface Agents

Agency

Task Agents

Information Agents


Agents

actors: teachers, head of department and the central scheduler

each actor is represented by an interface agent

•defend their interests

•make the system function better


Agents

when there has to be taken a decision:

1. Agents try to contact their actor

2. If this fails, they decide autonomously.This decision is based on information provided by the actor (information that is only known to the agent of the actor)


Co-ordination techniques


First Solution

Agents communicate with their actors and other agents.

We experimented with 2 different negotiating protocols:1st method

– 1st step: algorithm (started by a task agent) generates the 10 ‘best’ solutions. These solutions satisfy the so-called hard constraints, these constraints ALWAYS have to be fulfilled!


Hard Constraints

One teacher can only be in one lab at the same timeIn a lab room there is only one lab session that can take place at the same timeA student can attend only one lab session at the same time Each lab room has a limited capacity Some lab rooms are not equipped to accommodate certain lab sessionsThese hard constraints will be taken into account in the algorithm


First Solution

1st method– 2nd step: agents discuss about the above

solutions.To make this negotiation possible

blackboard


Blackboard (1)

Principle A solution is written by an agent on the

blackboard; The other agents search on the blackboard

for solutions where they are involved; They evaluate that solution (taking into

account the ‘soft constraints’ or their own preferences) and generate a price;


Soft constraints

Every teacher has his own preferences about the period he wants to teach a lab session

A teacher will not be equally prepared to do concessions to all colleagues

Of those lab rooms which are equipped some are more appropriate than others


Preferences


Blackboard (2)

The solutions and the price (based on the soft constraints) are then put back on the blackboard;

The agent who has put the solution on the blackboard in the first place evaluates the prices and takes the solution with the minimal cost;

This solution is then selected and the changes are made.


Second solution

2nd method: in this method the agents themselves search for a solution. To make this possible

Contract Net Protocol


Contract Net Protocol

An agent can play 2 different roles: a manager divides a problem in sub

problems and searches for contractors who can execute the sub problems and keeps an eye on the global solution.

A contractor executes a sub task. Contractors however can also become a manager who divides the problem again and contract it out to other contractors.


CNP

Managers find contractors through a bid process that goes like this:

1. A manager announces a task;

2. Contractors evaluate this task (keeping in mind that they have limited capabilities);

3. Contractors submit bids to the manager;


CNP

4. The manager evaluates the received bids, he chooses one of these bids and thus an associated contractor and gives him the contract;

5. The manager waits for the result of the contract.


CNP (figure)

manager

announcement of a task

contractors

manager

submit bid

contractors

grants contract

manager

contractor


Sympathy (only CNP)

Up to now: cost function exists of– cost– profit

The sum of these two forms the price

We add to the cost function ‘sympathy’.


Sympathy (only CNP)

When an agent gets a question from another agent to change or move his lab session he takes into account not only the cost to move or change that session but also the sympathy he feels for that particular agent.

New price becomes:

new_price = cost + profit - sympathy


Sympathy (only CNP)

Example: Agent B will remember that agent A did him a favour. Agent A moved his own lab session to a bad period just to help agent B. When agent A asks agent B to move or switch his lab session, agent B will remember that agent A did him a favour and he will faster allow the change.


Results


Results

Implementation of a prototype of:– Blackboard– CNP with sympathy

existing database: all teachers who give lab sessions in 1 particular department of our polytechnic

This kind of software development can be based on agents

Test case: application of our model to a dynamic scheduling problem in transportation which we worked out up to the level of analysis


Demo


Demo

Demo of the software CNP and sympathy Blackboard


What’s next?


COoperative And Learning Agents

Agents and Planning systems– Ontology for existing planning systems (User

Committee)– Definition of an agent interface– Integration of agents to develop

Learning Agents– Modelling the user– Modelling the system– Support maintenance and system development


The End

Thank you for your attention

february 24th 2000peter demeester - philippe de pauw 1 object oriented agents for distributed...

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