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Constructing the Future with Intelligent Agents

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  1. Constructing the Future with Intelligent Agents Raju Pathmeswaran Dr Vian Ahmed Prof Ghassan Aouad

  2. Today's presentation • Aim of the research • Introduction to agents • Abilities of intelligent agents • Types of agents configuration • Example industrial adoptions • Application of agents in construction • Future trends

  3. Aim • Aim of this research is to investigate the extent to which we can develop and use intelligent agents to evaluate designs from multiple perspectives and under various scenarios

  4. Scope

  5. What is an agent? • An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through effectors.


  6. Some agents • Human agent – eyes, ears, other organs for sensors; hands, legs etc for effectors • Robot agent – camera, infrared for sensors; various motors for effectors • Software agent – encoded bit strings as its percepts and actions

  7. What is an intelligent agent? • An intelligent agent is a computer system that is capable of flexibleautonomous action in order to meet its design objectives. • responsive: agents should perceive their environment and respond in a timely fashion to changes that occur in it • proactive: agents should not simply act in response to their environment, they should be able to exhibit opportunistic, goal-directed behaviour and take the initiative where appropriate • social: agents should be able to interact, when they deem appropriate, with other artificial agents and humans in order to complete their own problem solving and to help others with their activities.

  8. What do agents have to offer? • the ability to solve problems that have been beyond the scope of automation: • either because no existing technology could be used to solve the problem, or • because it was considered too expensive (difficult, time-consuming, risky) to develop solutions using existing technology; • the ability to solve problems that can already be solved in a significantly better (cheaper, more natural, easier, more efficient, or faster) way.

  9. Belief-Desire-Intention (BDI) model of agency • Some philosophical basis in the Belief-Desire-Intention theory of human practical reasoning • Belief - informational state of the agent i.e. beliefs about the world including itself & other agents • Desire - motivational state of the agent i.e. objectives or situations that agent would like to accomplish • Intention - deliberative state of the agent i.e. what the agent has chosen to do

  10. Agents Configurations

  11. 1. Agents based simulations

  12. 1. Applications • Healthcare Simulations • Traffic Simulations • Modelling Human Behaviour

  13. 2. Agents based real-world

  14. 2. Applications • Location based services • Measurement of work-in-progress • Security

  15. 3. Agents based adaptive information systems

  16. 3. Applications • Decision support systems • E-commerce systems • Financial systems e.g. anti money laundering

  17. Agent development toolkits • The JADE (Java Agent Development Environment) is an open source platform that develops Java agents • JACK - operates on top of the Java programming language, acting as an extension of object-oriented development that provides agent-related concepts. • IMPACT provides an agent development environment, called AgentDE, that allows developers to define every aspect of the agent that forms part of the agent wrapper

  18. Agent toolkits cont. • JACK makes use of just TCP and UDP for communication • IMPACT and JADE make use of RMI which is costly • Current trend is towards more lightweight approaches • Web Service technologies beginning to play an important role

  19. Industrial adoption of agent based technologies • Manufacturing Control • Daimler Chrysler demonstrated very high flexibility, increased throughput, robustness and reliability of the agent-based manufacturing facility • Logistics and Supply Chains • ABX use agent-based solution in order to (i) achieve performance scalability, (ii) reflect the geographical distribution of the nodes, (iii) provide local re-planning without the need to rebuild the whole plan and (iv) increase robustness so that a single point of failure would be avoided • Production Planning • Simulation • Unmanned Aerial Vehicle control • Space Exploration Applications

  20. Intelligent agents in construction research • Collaborative design of structures (Anumba et al, 2002) • Designing steel skeleton structures of tall buildings (Skolicki and Kicinger, 2002) • Supporting construction procurement negotiation (Dzeng and Lin, 2004)

  21. Constructing the future trends • Simulation: potential of wider deployment of multi-agent systems in the field of simulation. E.g. Healthcare simulation • Prototyping: Agents enabled virtual prototyping; to identify design errors • Hardware: E.g. agent applications linked to the utilisation of RFID technology for construction materials and equipments

  22. End Thankyou