P L A N E T - I n f o r m a t i o n D a y (May 26, 2003) Agent Based Production Planning Michal Pechoucek Gerstner Laboratory, Czech Technical University

P L A N E T - I n f o r m a t i o n D a y (May 26, 2003)

Agent Based Production Planning

Michal Pechoucek

Gerstner Laboratory, Czech Technical University

intro

agent-based production

planning

decomposition

techniquesSBC/ISBC

ExPlanTech MAS

CPlanT MAS

conclusions


Agent Based Systems

agent is an encapsulated computational system, that is situated in some environment, and that is capable of flexible, autonomous behaviour in order to meet its design objective (Wooldridge).

an agent is not only an object, process, program, situated robot, ..

critical difference: agents internal decision making processes are not transparent – one cannot prove what the other agent will do.

this property (and fact that agents are usually developed by different developers) causes emergent behaviour that has not been thought of at

the design time

agents can be standalone or members of a multi-agent system

distributed artificial intelligence is a branch of science that studies social aspects of artificial intelligence, e.g. communication, cooperation, collective mental states

multi-agent system is a collection of agents that work together in order to meet an in-community-shared goal

agent based system is a system whose functionality is based on operation of agent(s), which may be of collaborative or self-interested nature

intro


What can Agents Provide Production Planning with?

design architecture (e.g. Prosa architecture, Holonic Manufacturing Systems, ProPlanT architecture, etc.)

integration/agentification technology (e.g. FIPA standards, agent development environments)

planning algorithms – distributed decision making (e.g. stigmergy, negotiation and auctioning, social intelligence based interaction, etc.)


Agent-based Production Planning

advantages of agent-based planning approaches:

reconfigurability and flexibility, tractability (distributed), higher degree of planning efficiency

there are three fundamental approaches to agent-based planning:

(i) decomposition based planning – there is a temporary or permanent hierarchy of agents where each decomposes a task into subtasks and coordinates its completion. can be done via contract-net-protocols, subscriptions, etc.

(ii) fully autonomous planning – all agents see the planning problem and form their local plans. these plans are later merged and conflicts are resolved by re-planning – e.g. PGP – Partial Global Planning. agents share a common knowledge structure (blackboard) or there is a high-level coordinator (who resolves the conflicts) or agents interact via rather inefficient distributed techniques (negotiation, broadcast, rings, voting, etc.)

(iii) backward chaining planning – a compromise between (i) and (ii). the request backpropagets in the manufacturing flow. there is no command-and-control hierarchy and no central component, but agents negotiate via contract-net-protocols, subscriptions, etc.

agent-based production

planning


Decomposition Based Planning

we want to arrive at a distributed plan that will achieve a high-level task

each task can be planned either by means of a

team action plan – result of inter-agent negotiation and mutual agreeing upon joint commitments or

individual plan – shall implement a single agent’s commitment (planning by linear/non-linear planning)

the problem is to decide

how to decompose a task into subtask whom to subcontract for cooperation


Team Action Plan

team action plan () is as a set () = {i, Aj, start(i), due(i), price(i)}.

() is correct if all the collaborators Aj are able to implement the

task j in the given time and for the given price.

() is accepted if all agents Aj get committed to implementing the

task j in the given time and for the given price.

is achievable, if there exists such () that is correct. is planned, if there exists () that is accepted


Individual Action Plan

individual plan () is as either an unordered set

() = {i, start(i), due(i), price(i)}.

or a partially ordered set

() = {i, price(i)}.

() is correct (complete and consistent) if it is executable and implements .

() is complete iff all the preconditions of the operators are satisfied by an effect of another operator (or by initial conditions).

() plan is consistent iff ordering among operators does not contradict or operators from the same world do not provide contradicting effects


Decomposition/Contraction Techniques

contract-net-protocol (CNP)

auctions

subscription based contraction (SBC)

iterated SBC (I-SBC)

Request for offers

Time

Reply service offers

coordinator

Reply service results

Request for service


collaborator

Request for offers

Request for offers


collaborator collaborator

decomposition

techniquesSBC/ISBC




auctions



auction protocols:

English (first-price open-cry) – sometimes an open-exit sealed-bid first-price Dutch auction Vickery (sealed-bid second-price) all-pay auctions (computer science)




Time

Reply service results

Request for service

Subscribe service

Subscribe service

Subscribe service

Inform service

coordinatorcollaborator collaborator collaborator

Inform service

Inform service

Inform service

Inform service

Inform service

Inform service

registration

revision

contract

revision


Social Knowledge (SK)

agent’s knowledge is either:

problem solving knowledge – “asocial” type of skill – guide agent’s autonomous local decision making processes (aimed e.g. at providing an expertise or search in the agent’s database)

self knowledge – knowledge about agent’s behavior, status and commitments (a special instance of social knowledge – below)

social knowledge – knowledge about other agents, their behavioral patterns, their capabilities, load, experiences, commitments, but also knowledge and belief

social knowledge is located in agent’s wrapper – in an acquaintance model

wrapper

body

acquaintance model

communication layer

body


Tri-base Acquaintance Model

acquaintance model is a computational model of agents’ mutual awareness, it stores and maintains agents’ social knowledge

decomposition on request:

exploitation of the pre-prepared plan new plan generation (based on SB knowledge) new plan generation (broadcasting)

replanning driven by state-base update

agent’s acquaintance model

task basecoop-basestate-base

agent

agent

agent

agent

agent

PRS

PLS


CF Acquaintance Model

Soc-BB(A0)={KS(Ai)} for Ai (A0), Com-BB(A0)={Kp(Ai)} for Ai (A0)

Self-BB(A0)= {{Kp(A0)}, {KS(A0)}, {KPr(A0)}}, Coal-BB(A0)= coalitions, rules

reduces the communication traffic and thus the increases problem solving efficiency, while it requires substantial communication for the acquaintance model maintenance

agent’sbody

self-belief base

social-belief base

community base

coalition base

3bATask base3bA Cooperator base

3bA Task base


Example

Self-Belief Base

public knowledge: Semi-private knowledge: Private knowledge

Port: 1500 ip_ address: “147.32.86.167” Country: suffer terra City: north port Type: Religious Ontologies: fipa-am, cplant-ontology

Food: 3000 Nurses: 50 Trucks: 13

Alliance restrictions: (“country”,“Suffer Terra”) Leader restrictions: (“type”,“Military”). City restrictions: (“muslim”,50) Cooperates with: (“type”,“government”)

Social belief base

Agent: ST Police Armed-people:30

Agent: Christian STHO

Food: 3500 Clothing: 280 Nurses: 22 Medical-people: 12

Community belief base

Agent: Suffer Terra Government

Suffer Terra Government@iiop:/ /147.32.84.131:2188/Suffer Terra Government Type: Government Services: Food, Civil-material, Medical-material, Clothing Ontologies: FIPA-AGENT-MANAGEMENT, MAP-ONTOLOGY, PORT-ONTOLOGY, CPLANT, ALLIANCE Languages: SL1, KIF, State: ACTIVE Country: Suffer Terra, City: Suffer Town

Agent: Christian STHO

Christian Suffer Terra Humanitarian Organization@iiop:/ /147.32.84.131:2210/Chr ST Humanitarian Organization Type: Religious Services: Food, Clothing, Medical-people, Nurses, Medical-material Ontologies: FIPA-AGENT-MANAGEMENT, MAP-ONTOLOGY, PORT-ONTOLOGY, CPLANT, ALLIANCE Languages: SL1, KIF, State: ACTIVE Country: Suffer Terra, City: North Port

Coalition Base

Rules (VOLCANIC-AVERAGE-SMALL-TOWN Time: 220 (Requirements: Medical-material 60, Food 1500, Civil-material 30000, Medical-people 16, Civil-people 27, Nurses 19) …

Coalitions (coalition (Members: Suffer Terra Government, Suffer Terra Police, Christian Suffer Terra Humanitarian Organization) (Services: Food, Civil-material, Medical-material, Clothing, Military-people, Food, Clothing, Medical-people, Nurses) (Price-for-coordination: 5))

(planned-coalition ( Task name: Suffer-Town-24-1-2002/17-49-53.1 (Coalition members: Suffer Terra Government, Suffer Terra Police, Christian Suffer Terra Humanitarian Organization) (Coalition leader: Christian Suffer Terra Humanitarian Organization (Disaster: Volcanic, Degree: 1, (Allocations: Civil-material, 80000, Allocation Time: 350 Food, 80000, Allocation Time: 350 Medical-material, 80000, Allocation Time: 350)) …




auctions



SBC difficulties:

maintenance – too much of data, how often, … monitoring selectivity frequency of requests still high complexity on the side of the coordinator




auctions



therefore we suggest an improvement of SBC that is good for very complex domains, where not all data are available (confidentiality reasons) or there are too much of data (complexity problems)

exploitation of the concept of the private, public and semi-private knowledge (as much as the concept alliances), where only approximation of the planning data is made available to agents social models


Iterated SBC (I-SBC)

coordinator



coordinator



coordinator



coordinator



agent1

agent2

agent3

t

resources

t



agent1

agent2

agent3

t

resources

t


Agent-Based Planning in the Gerstner Laboratory

ExPlanTech – Production Planning Multi-agent System

CPlanT – Coalition Planning Multi-Agent System for OOTW planning

ExPlanTech MAS


ExPlanTech: Domain Specification

ExPlanTech – a production planning system with a functionality to: estimating due dates and resources requirements providing a project plan implementing re-planning

extra-enterprise extension to allow remote access integrate supply-chain relations


ExPlanTech: Architecture

factory hardware/ software

PPA

PMA

PMAPMA

PMA

PMA

PAPA

PA PA

PAPA

PMA

PMA

ent. resourceplanning

human resources

accountingenterprisemachinery

material resource

intra-enterprise ProPlanT

PPAPPAintra-enterprise

meta-agent


ExPlanTech: Implementation

operator: an instance of the ppa and pma classes – project configuration and decomposition, management of the overall project

workshop: an instance of the pa class – scheduling and resource allocation on a department or CNC machine

database agent: an instance of the pa class – an integration agent, integrates ExPlanTech with factory ERP

material agent: an instance of the pa class – integrates an MRP - material resource planning system

FIPA compliant system, implemented in JADE (Java Agent Development Environment).

Distributed over several machines, each agent has got a GUI for user interaction

new agents can login and the confuigu-ration can be altered in runtime

Integrated with MS-Project, JDBC, IE Special visualization and user

manipulation meta-agent


ExPlanTech: ExtraPlanT Exetnsion


Agent-Based Planning in the Gerstner Laboratory

ExPlanTech – Production Planning Multi-agent System

CPlanT – Coalition Planning Multi-Agent System for OOTW planning

CPlanT MAS


CPlanT: Domain Specification

domain: Operations other than war (OOTW): humanitarian relief operations, peace-keeping missions, non-combat operations

each entity/actor (governmental institutions, troops, humanitarian bodies, NGOs, charities) represented by an agent

domain specifics (simplified):

equality – anyone can initiate forming a coalition – no hierarchy reluctance to share vital planning information agents inaccessibility – poor communication links, … collaborative/self interested – different cultural backgrounds

key problems:

minimize required communication traffic (affects problem solving efficiency) keep the quality of the operation the coalitions perform reasonably good minimize loss of agents private knowledge disclosure, minimize the amount of the shared information


CPlanT: Key Ideas

organizing the agents into alliances (structural decomposition)

a particular task (a mission) accomplished by a coalition (preferably created as a subset of an alliance)

structuring the agents private, semi-private, public knowledge

using the concept of the tri-base acquaintance model and social intelligence

designing advanced methods for inter-agent negotiation


CPlanT: Coalition Formation Operation Lifecycle

Registration: central registration of the public knowledge Alliance Formation: communicated via selective single-stage CNP

Coalition Leader Selection: collective decision making Coalition Formation: communicated via acquaintance models

based contraction Team Action Planning: collective planning of a team action –

combination of CNP and AM


CPlanT: Implementation


Conclusions

agents in production and resource allocation planning are good as

the planning system is scalable and easy to be reconfigured problem solving efficiency can be increased by an appropriate

structuring of the community and acquaintance model design they are efficient in areas with natural distribution (e.g. supply

chains) for handling imprecise information and inexact knowledge

http://agents.felk.cvut.cz http://gerstner.felk.cvut.cz

conclusions

Documents

P L A N E T - I n f o r m a t i o n D a y (May 26, 2003) Agent Based Production Planning Michal Pechoucek Gerstner Laboratory, Czech Technical University