A Multi-Agent Systems Based Conceptual Ship Design Decision Support System The Ship Stability Research Centre Department of Naval Architecture and Marine Engineering Universities of Glasgow and Strathclyde Bekir S. Türkmen

Design Exploration and Support Distributed Architecture Encapsulation of Design Experience Motivations

What is an agent? An Agent : one that acts or has the power or authority to act or represent another. An Intelligent Agent is the agent does the things rationally in a given situation (Russell 1995)

Intelligent Agents Autonomy Collaborative Behaviour Adaptivity Mobility Proactivity Reactivity

Multi-Agent Systems

MAS- Three Important Questions Communication Control Co-ordination, Collaboration, Negotiation

Communication Semantics and Syntax KQML, FIPA-ACL KIF, FIPA-SL FIPA-ACL (INFORM :sender ( agent-identifier :name :addresses () :receiver (set ( agent-identifier :name ) :content "Hello SSRC" ) FIPA-SL (query ‑ ref :sender (agent-idenfier :name B) :receiver (set (agent-identifier :name A)) :content ((iota ?x (p ?x))) :language FIPA-SL :reply ‑ with query1) KQML/KIF (evaluate :sender A :receiver B :language KIF :ontology motors :reply-with q1 :content (val (torque m1))) (reply :sender B :receiver A :language KIF :ontology motors :in-reply-to q1 :content (= (torque m1) (scalar 12 kgf)))

Control Centralized Federated Autonomous

Co-ordination Auctions Contract-Net (Task Sharing) Planning Game Theory Argumentation Catalogue of Conflicts

Proposed IA Architecture Communication Layer Coordination Layer Conflict Resolution Module Optimisation Module Knowledge Base for Conflicts Rule-based Case-based Optimisation Module Local-Search Algorithms Global-Search Algorithms Learning Module Task Layer ENVIRONMENT Acquaintance Module Task Layer Knowledge Base Wrapped Simulation Tools Acquaintance Module List of Agents Agents’ work definition Intelligent Agent Architecture User Interface

Proposed MAS Architecture Static Stability Agent Dynamic Stability Agent Evacuation Agent Resistance Agent Hull Generation Agent CFD Agent FEA Agent Worker Agents Multi-Objective Optimisation Agent Multi-Attribute Decision Maker Agent Decision Theoretic Agents 3D Real-Time Simulation / Virtual Reality Agent User Interface Agents Geometry Transfer Multi-Agent System Architecture ………………………..

Decision-Theoretic Agents Multi-Objective Optimisation Agent Multi-Attribute Decision Maker Agent Decision Theoretic Agents Ranking and Selection Methods TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) …… Multi-Objective Optimisation Algorithms VEGA (Vector Evaluated GA) NSGA (Non-Dominated Sorting GA) NSGA2 (A Fast and Elitist NSGA) SPEA/SPEA2 ( Strength Pareto Genetic Algorithm)

Multi-Objective Optimisation Decision-Making Before Search Decision-Making After Search Decision-Making during Search

Comparison of MOGA Methods Objective Functions : f 1 (x) = x 2 ; f 2 (x) = (x-2) 2 Figure 1. VEGA Results Figure 2. NSGA Results Figure 3. NSGA II Results Figure 1 Figure 3 Figure 2

Integrated Decision-Making and Search In order to reduce the calculation cost and scalability we guide the search by introducing designer preferences into search. Applied as A Priori and Progressive, Final Selection from Reduced Pareto-Set

Proposed Approach for Introducing Bias NSGA II + TOPSIS Algorithm Reference Point Method Approach NADIR POINT IDEAL POINT

Proposed Approach for Introducing Bias Continued Two modifications to introduce bias, Modification of Elitist Strategy Modification of Crowding Distance Assignment Preference is given as, one unit of a is worth at most x units of b

Internal Hull Subdivision Optimisation Objectives Survivability –Max. Cargo Capacity (In Car Lanes) Max. Limiting KG – Max. Constraints Two Adjacent Bulkhead Distance greater than SOLAS’90 Longitudinal Damage Extent, SOLAS’ 90 Regulations,Limiting KG Reduction for operational Life cycle

Internal Hull Subdivision Optimisation

Internal Hull Subdivision Optimisation Continued Cargo Capacity (Car Lanes)

Internal Hull Subdivision Optimisation Results

Distributed Optimisation Test Problem in A Multi-Agent Systems

Distributed Optimisation in A MAS Early Results

Conclusions and Future Development Advantages of proposed approach Distributed Computation (Less computation time) Distribution of Expertise (Intelligent Agent Architecture) Integrated Multi-Criteria Decision-Making and Decision Support Environment. Future Research Integration with CAD Environment Case Study for Intelligent Agents in Multi-Agent Systems

Questions