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Published on January 14, 2008

Author: Candelora

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Flexibility Through Multiagent Systems: Solution or Illusion? :  Flexibility Through Multiagent Systems: Solution or Illusion? Peter C. Lockemann Slide2:  OEM Dispoweb KRASH- MAS IntaPS- MAS FABMAS- MAS dispoweb dispoweb ATT/SCC intraplant interplant external Monitoring Execution Planning Customer Scenario: Production and supply chain Slide3:  Scenario: Production and supply chain Scenario: Production and supply chain:  Scenario: Production and supply chain Slide5:  Scenario: Health care Scenario: Digital libraries:  Scenario: Digital libraries Scenario: Traffic prognosis:  Scenario: Traffic prognosis Scenario: Traffic prognosis:  Scenario: Traffic prognosis Services Real traffic Model world The optimist:  The optimist Social organizations are resilient and responsive to ever changing situations because they include enough intelligent decision makers sufficient slack to take decisions. Then why not mirror these properties to build software systems that are flexible (resilient and responsive)? Agents are a most natural metaphor to model complex social organizations that must be highly adaptive and rely on flexible and adaptive members. Such systems are known from AI and referred to as multiagent systems (MAS). Hypothesis: If well done, MAS offer the adaptivity and flexibility needed for the modeled world. The pessimist:  The pessimist Multiagent systems are distributed include asynchronous communication have components with indeterministic behavior. Therefore, they are inherently difficult to engineer: It is hard to give a sufficiently precise system specification and even if there is one, what are the chances to verify or validate that the system implementation satisfies the specification? Where the two may meet:  Where the two may meet Are there situations with compelling reasons for a multiagent approach? Can one yet impose constraints to be able to engineer multiagent systems? Agenda:  Agenda Are there situations with compelling reasons for a multiagent approach? What is an agent after all? ... and a multiagent system? Useful multiagent software systems: A hypothesis Testing the hypothesis Mixed results and a refined hypothesis Can one yet impose constraints to engineer MAS? A database person’s view At least it should be reliable! Transactional agents Reliable agent cooperation Conclusions and challenges What is an agent?:  What is an agent? Wooldrige and Jennings on agents: “An agent is a computer system that is situated in some environment, and that is capable of autonomous action in its environment in order to meet its design objectives.“ Wooldrige on intelligent agents: “An intelligent agent is a reactive, proactive, interacting agent.“ What’s behind the definition:  An open system: must have an observable effect Effective indeterminism in time and kind What’s behind the definition “An agent is a computer system that is situated in some environment, and that is capable of autonomous action in its environment in order to meet its design objectives.“ “An intelligent agent is a reactive, proactive, interacting agent.“ Agents can be many things to many people A piece of code: software agent Stimulators and responders Multiagent systems:  Multiagent systems autonomous situated reactive proactive interacting interacting interacting autonomous situated reactive proactive autonomous situated reactive proactive Common objective Multiagent systems:  Multiagent systems autonomous situated reactive proactive interacting interacting interacting autonomous situated reactive proactive autonomous situated reactive proactive Common objective Can the systems be engineered? How much flexibility can we manage? Specialize! Specialize! Specialize! Specialize! Who needs all that flexibility?:  the programmer’s view the software architect’s view the database person’s view Who needs all that flexibility? Hypothesis: Multiagent systems are useful for an environment where the range of situations (the problem space) is too large for enumeration and instead needs a qualitative description, the problem space can be divided into sets of simpler tasks, each requiring specialized competence, the simpler tasks can be dealt with autonomously by individual agents, solving the overall situation requires cooperation among the agents. the AI person’s view Flexibility through multiagent systems:  Flexibility through multiagent systems autonomous situated reactive proactive interacting interacting interacting autonomous situated reactive proactive autonomous situated reactive proactive Descriptive competence Descriptive competence Descriptive competence Assignment of individual responsibilities & cooperative problem solving Testing the hypothesis: Scenario:  Testing the hypothesis: Scenario Benchmark:  Benchmark Molding area Buffer Product assembly A B C Unit assembly Manual assembly External supplier Supply Order Kanban Flexibility via Kanban production organization: Production control: buffer minimization via pull principle High variability within homogeneous product spectrum Benchmark:  Benchmark Molding area Buffer Product assembly A B C Unit assembly Manual assembly External supplier Supply Order Kanban Analytical and simulation-based layout planning for a given production program Test and Benchmark:  Test and Benchmark Centrally planned Kanban is not flexible enough to deal with machine failures short-term deviations from the delivery schedule Solution options Passive: Simulate disturbances and adjust layout planning Reactive: Rescheduling algorithms (known to be suboptimal) Reactive: Agents The best performing agent model :  The best performing agent model Mixed-model Assembly Line Balancing Problem (MALBP): Reactive MAS Approach Experiments by simulation:  Experiments by simulation Parameter variations: Master data: Bill of materials Operation list fore each product Transport lot size Affects the number of suborders for a single order Production order generated by production planning and control Disturbance profile per machine (statistical) Interval between disruptions Disruption duration Output: Throughput: Average Standard deviation Experiments by simulation:  Experiments by simulation Throughput and Processing Time Corresponding Standard Deviations Parameterisation Experiments: Sample Analysis:  Experiments: Sample Analysis Throughput Time Standard Deviation Suitability of MAS Compared to Centralized OR Approaches Depending on Decision Variables Reliability and Predictability of the Results Improvement by MAS Empirical results:  Empirical results Multiagent systems need slack: If assembly lines run close to capacity, MAS are even inferior. They performed better if slack was provided by additional machines or by delaying assembly orders. Multiagent systems need inhomogeneity: More assembly lines by themselves have no effect unless the machines follow very different disturbance profiles. Different disturbance profiles have even a positive effect if no machines are added. Multiagent systems operate best in a dynamic, non-deterministic environment: MAS are superior when it comes to evening out fluctuations due to disruptions. Revisiting the hypothesis:  Revisiting the hypothesis Hypothesis: Multiagent systems are useful for an environment where the range of situations (the problem space) is too large for enumeration and instead needs a qualitative description, the range of decisions (the solution space) is commensurate in size with the problem space, the problem space can be divided into sets of simpler tasks, each requiring specialized competence, the simpler tasks can be dealt with autonomously by individual agents, solving the overall situation requires cooperation among the agents. Practical implementation:  Practical implementation DB eMPlant Simulation Model Communication Platform using Event Mechanisms FIPA-OS Implementation A database persons’ view:  A database persons’ view Hypothesis: Multiagent systems are useful for an environment where the range of situations (the problem space) is too large for enumeration and instead needs a qualitative description, the range of decisions (the solution space) is commensurate in size with the problem space, the problem space can be divided into sets of simpler tasks, each requiring specialized competence, the simpler tasks can be dealt with autonomously by individual agents, solving the overall situation requires cooperation among the agents. Take a descriptive approach! After all, SQL does it! State your objectives, let the system determine the best way how to achieve them! We need software engineering for MAS. Disciplines that could help: Model-driven programming Service-oriented programming Component-orientation Agent platforms are available. But then, agents and MAS should not be indeterministic beyond their own control, such as own disturbances! Mastering the complexity:  Mastering the complexity autonomous situated reactive proactive interacting interacting interacting autonomous situated reactive proactive autonomous situated reactive proactive Transactions:  Transactions autonomous situated reactive proactive interacting interacting interacting autonomous situated reactive proactive autonomous situated reactive proactive Agent may be involved in concurrent conversations! INTERRAP BDI agent architecture:  INTERRAP BDI agent architecture World Model Mental Model Cooperation Model SG PS SG PS SG PS World Interface Agent knowledge base Cooperative planning layer Local planning layer Behavior based layer SG: Situation recognition and Goal activation PS: Planning, Scheduling and execution information access control flow Corresponding transaction model:  Corresponding transaction model Recovery Conversation Evolutionary transactions:  Evolutionary transactions World Model Mental Model Cooperation Model SG PS SG PS SG PS World Interface Agent knowledge base Cooperative planning layer Local planning layer Behavior based layer Agent cooperation by synchronization:  Agent cooperation by synchronization agent 1 transaction agent 2 transaction Agent cooperation by synchronization:  Agent cooperation by synchronization M1 M12 M11 M13 M111 M121 ... M131 ... M1111 ... M112 S1 S12 S122 S123 S121 S11 S0 S2 Master Slave Task Delegation Experiments with an orthogonal architecture:  Experiments with an orthogonal architecture Isolation Not a nice solution because …:  Not a nice solution because … agent 1 transaction agent 2 transaction Transactional conversations:  distributed transaction Transactional conversations agent 1 transaction agent 2 transaction conversation Modeling of conversations:  Modeling of conversations ready initiator participant initiator participant Transactional conversations:  Transactional conversations User Task DB I DB II User Task FIPA conformant conversation Drawback: Conversations must be attached to the leaf nodes The next idea:  Message-oriented middleware (MOM) with messages based on speech acts The next idea agent 1 transaction agent 2 transaction conversation Conclusions: Flexibility:  Conclusions: Flexibility MAS are very costly to engineer: System-level behavior is close to impossible to predict analytically, requires large simulative or experimental effort. The added cost does not even pay off in the majority of cases! The expenses seem only justified for applications with large problem and solution spaces, where the problem space is or appears non-deterministic. A natural application seems health care. Candidate technical applications are those with a high level of disruptions. Conclusions: Flexibility:  Conclusions: Flexibility Approach: Transactional properties Requires an expensive and extensive infrastructure of database manager and transaction manager. Heavy-weight nodes needed for agents. Distributed transactions (a foe of autonomy!) and active database mechanisms (e.g., trigggers) needed. Challenges: Industrial strength:  Challenges: Industrial strength Transactional synchronization: Evolutionary transactions to deal with reaction. Transactions with built-in cooperation. Non-orthogonal individual and cooperative behavior. Transactional conversations: Difficult to attach to agent transactions. Rigid, must have ACID properties. Message-oriented middleware: Still an unknown area for agents. Long term: A combination of transactional agents and message-oriented middleware with higher-level guarantees. Contribute to standards! Conversation Policy XML (cpXML):  Conversation Policy XML (cpXML) Origin: IBM (Conversation Support for Webservices) Status: V 1.0 (August 2002) Specification: XML Schema, Standards document, papers, tutorial Coverage: Sequencing and timing of messages Tools Reference implementation for WebSphere JCA extension by Conversation Manager and Conversation Adapter Remarks Compatible to BPEL4WS CPs exchangeable FIPA-Standard:  FIPA-Standard Applications Nomadic Application Support Agent Software Integration Message Buffering QoS Network Mgmt Personal Assistant, Personal Travel Assistant Audio-visual Entertainment and Broadcasting Foundation of Interoperable Agents Augmenting FIPA:  Augmenting FIPA FIPA-OS Agent Management System „White Pages“ Robustness service Standard system DW Standard system Scheduling Task Task Data Warehouse Software Task Standard system ... Directory Facilitator „Yellow Pages“ Task Data Warehouse „Agent“ Order agent Machine agent ...

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