ALADDIN: Autonomous Learning Agents for Decentralised Data and Information Networks
This project aims to develop techniques, methods and architectures for modelling, designing and building decentralised systems that can bring together information from a variety of heterogeneous sources in order to take informed actions. To do this, the project needs to take a total systems view on information and knowledge fusion and to consider the feedback that exists between sensing, decision making and acting in such systems. Moreover, it must be able to achieve these objectives in environments in which: control is distributed; uncertainty, ambiguity, imprecision and bias are endemic; multiple stakeholders with different aims and objectives are present; and resources are limited and continually vary during the system’s operation.
More specifically, the main aims of the project are:
- To devise techniques that enable an actor to effectively balance acting and information gathering in dynamic, uncertain, multi-actor environments.
- To devise techniques that enable an actor to fuse, in a decentralised manner, inter-related information that is uncertain, incomplete, imprecise and ambiguous.
- To develop machine learning algorithms that are
efficient and effective in dynamic, multi-actor
environments that are uncertain and incomplete.
- To develop coordination mechanisms that enable collectives to plan and act collaboratively in order to achieve common goals.
- To develop methods for modelling and predicting the system behaviour that will ensue from specifications of the local behaviour of the individual actors.
- To develop mechanisms that ensure desirable overall properties emerge based on local actions and views.
- To develop decentralised system architectures that can operate effectively in uncertain and dynamic environments and that are robust, scaleable and flexible in their operation.
To ensure the specific methods and techniques developed in the research fit together to give a coherent whole, the project will develop a number of software demonstrations. These will be in the broad area of disaster management.
Type: Normal Research Project
Research Groups: Intelligence, Agents, Multimedia Group, Agents, Interaction and Complexity
Themes: Agent Based Computing, Disaster Management, Machine Learning, Decentralised Architectures, Decentralised Information Systems, E-Business Technologies, Intelligent Systems and Machine Learning, Wireless Sensing and Sensor Networks, Artificial Intelligence
Dates: 2nd October 2005 to 30th March 2011
- BAE Systems
- BAE Systems
- Prof. Erol Gelenbe (Imperial College London)
- Prof. David Hand (Imperial College London)
- Prof. Steve Roberts (Oxford University)
- Dr. David Leslie (University of Bristol)
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