Systems Engineering Research Group (SERG)

The AI and Systems Engineering Research in the three group areas are being pulled together to create new research into Ambient Intelligence, Sensor Processing and Cognitive Systems Engineering under the following headings:

Ambient Intelligence

General research:

• Interactive systems
• Context-sensitivity
• Safety and security
• Turning Ambient Intelligence information into knowledge
• Energy efficiency
• Model-based reasoning
• Human acceptance and motivation

Specific research:

• Intelligent sensor networks
• Digital environments
• Manufacturing
• Energy management
• Energy control systems (control of energy use and energy consumption)
• Intelligent user interfaces
• Process performance, reconfigurability and reliability
• Correlation of different types of knowledge (including incomplete and / or ill-structured knowledge); management of tacit knowledge
• On-line data capture and maintenance
• e-KM tools
• Human-centred approaches and Knowledge Management
• Combining Ambient Intelligence and knowledge from data capture and processing

Sensors

General research:

• Wireless sensors
• Neuroscience sensor systems and information processing in animals
• Interfacing and sensor fusion to form single representations
• Sensor imperfections (noise and segmentation etc)
• On-line capturing and maintenance
• Combining Ambient Intelligence and knowledge from data capture and processing

Specific research:

• Sensor structures and systems within the partner companies
• Isolation of parts of systems to explore the sensors in the different companies
• Identifying complementary competitive and cooperative sensors
• How they react to the environment
• Data consistency and problems experienced
• Data extraction retrieval and fusion
• Information and knowledge extraction and representation
• Information management
• Representations of the local world created by the sensors and actuators
• How the systems behave and create their models of reality
• How they maintain their model and change the map of behaviour
• Differences and similarities between different representations (or maps) of reality
• Ways of combining the different representations and maps

Low-level Cognitive Systems

General research:

• Modelling and monitoring industrial environments
• Working short-term and long-term memory and data structures
• Knowledge retrieval storing and management
• Automatic decision making systems and inference engines

Specific research

• Deriving models of sensory data and creating consistent and sharable models
• Object recognition state space pattern recognition and feature fusion
• Languages used by different systems within the organisations and systems in the consortium
• Learning algorithms to estimate probability density functions from empirical data
• Data-mining and inference algorithms to interpret the processed data
• Methods of information processing
• Information processing and reasoning problem solving and perception
• Sharing of human models of reality with systems or machine models of reality
• Creation of a set of heuristics (rules of thumb) empirically derived to investigate manufacturing systems behaviour or human-machine interaction in a useful consistent and sharable way

Higher level Cognitive Systems

General research:

• Decision-making and problem solving in natural cognitive systems (humans and societies)
• Mapping higher-level knowledge
• Agent-based systems
• Modelling industrial processes
• Systems evolution within a changing environment
• Ontologies and enhancement and maintenance of ontologies
• Cognitive Analysis high-level tools and decision fusion

Specific research:

• Describing the maps of the world (including the systems and organisations etc)
• Creating prototypes of the maps of the world
• Methods to identify and share the maps in a useful way
• Filtering to avoid systems becoming overwhelmed by data and then information and then potential knowledge
• Reasoning methods and prioritisation
• Reasoning systems to assist decision making over a distributed system(s)
• Creation of models that simulate the interactive processes (assuming that the systems are based on certain 'self-organizing' principles that tend to seek optimal states of balance or homeostasis)
• Creation of richer and fuller maps that respect the systemic nature and ecology of the systems and the human operators and the interaction between the two
• Creation of systems that enrich the choices that a system has and perceives
• Pursuit of excellence from having more choice provided by shared models
• Allowing systems to anticipate and then suggest or take effective action
• Improving processes by allowing some prediction of future problems
• Anticipation and decisions about course(s) of action
• Pursuit of a basic level of wisdom from having multiple perspectives