intro Agent Based Production Planning agentbased production planning
intro Agent Based Production Planning agent-based production planning decomposition techniques SBC/ISBC Michal Pechoucek Ex. Plan. Tech MAS Gerstner Laboratory, Czech Technical University CPlan. T MAS conclusions PLANET - Information Day (May 26, 2003)
Agent Based Systems intro 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 PLANET - Information Day (May 26, 2003)
What can Agents Provide Production Planning with? design architecture (e. g. Prosa architecture, Holonic Manufacturing Systems, Pro. Plan. T 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. ) PLANET - Information Day (May 26, 2003)
Agent-based Production Planning advantages of agent-based planning approaches: - reconfigurability and flexibility, tractability (distributed), higher degree of planning efficiency agent-based production planning there are three fundamental approaches to agent-based planning: 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-andcontrol hierarchy and no central component, but agents negotiate via contract-net-protocols, subscriptions, etc. (i) PLANET - Information Day (May 26, 2003)
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 PLANET - Information Day (May 26, 2003)
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 PLANET - Information Day (May 26, 2003)
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 PLANET - Information Day (May 26, 2003)
Decomposition/Contraction Techniques contract-net-protocol (CNP) auctions decomposition techniques SBC/ISBC subscription based contraction (SBC) iterated SBC (I-SBC) PLANET - Information Day (May 26, 2003)
Decomposition/Contraction Techniques contract-net-protocol (CNP) auctions subscription based contraction (SBC) iterated SBC (I-SBC) 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) PLANET - Information Day (May 26, 2003)
Decomposition/Contraction Techniques subscription based contraction (SBC) PLANET - Information Day (May 26, 2003)
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 communication layer wrapper body acquaintance model body PLANET - Information Day (May 26, 2003)
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 PLANET - Information Day (May 26, 2003)
CF Acquaintance Model Soc-BB(A 0)={KS(Ai)} for Ai (A 0), Com-BB(A 0)={Kp(Ai)} for Ai (A 0) Self-BB(A 0)= {{Kp(A 0)}, {KS(A 0)}, {KPr(A 0)}}, Coal-BB(A 0)= coalitions, rules reduces the communication traffic and thus the increases problem solving efficiency, while it requires substantial communication for the acquaintance model maintenance PLANET - Information Day (May 26, 2003)
Example PLANET - Information Day (May 26, 2003)
Decomposition/Contraction Techniques contract-net-protocol (CNP) auctions subscription based contraction (SBC) iterated SBC (I-SBC) SBC difficulties: - maintenance – too much of data, how often, … monitoring selectivity frequency of requests still high complexity on the side of the coordinator PLANET - Information Day (May 26, 2003)
Decomposition/Contraction Techniques contract-net-protocol (CNP) auctions subscription based contraction (SBC) iterated SBC (I-SBC) 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 PLANET - Information Day (May 26, 2003)
Iterated SBC (I-SBC) coordinator PLANET - Information Day (May 26, 2003)
Iterated SBC (I-SBC) coordinator PLANET - Information Day (May 26, 2003)
Iterated SBC (I-SBC) coordinator PLANET - Information Day (May 26, 2003)
Iterated SBC (I-SBC) coordinator PLANET - Information Day (May 26, 2003)
Iterated SBC (I-SBC) agent 1 resources agent 2 agent 3 t t PLANET - Information Day (May 26, 2003)
Iterated SBC (I-SBC) agent 1 resources agent 2 agent 3 t t PLANET - Information Day (May 26, 2003)
Agent-Based Planning in the Gerstner Laboratory Ex. Plan. Tech – Production Planning Multi-agent System Ex. Plan. Tech MAS CPlan. T – Coalition Planning Multi-Agent System for OOTW planning PLANET - Information Day (May 26, 2003)
Ex. Plan. Tech: Domain Specification Ex. Plan. Tech – 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 PLANET - Information Day (May 26, 2003)
Ex. Plan. Tech: Architecture PLANET - Information Day (May 26, 2003)
Ex. Plan. Tech: 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 Ex. Plan. Tech 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 confuiguration can be altered in runtime Integrated with MS-Project, JDBC, IE Special visualization and user manipulation meta-agent PLANET - Information Day (May 26, 2003)
Ex. Plan. Tech: Extra. Plan. T Exetnsion PLANET - Information Day (May 26, 2003)
Agent-Based Planning in the Gerstner Laboratory Ex. Plan. Tech – Production Planning Multi-agent System CPlan. T MAS CPlan. T – Coalition Planning Multi-Agent System for OOTW planning PLANET - Information Day (May 26, 2003)
CPlan. T: 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 PLANET - Information Day (May 26, 2003)
CPlan. T: 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 PLANET - Information Day (May 26, 2003)
CPlan. T: 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 PLANET - Information Day (May 26, 2003)
CPlan. T: Implementation PLANET - Information Day (May 26, 2003)
Conclusions agents in production and resource allocation planning are good as conclusions - 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 PLANET - Information Day (May 26, 2003)
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