Artificial intelligence

Artificial Intelligence is a hybrid between two disciplines: the engineering of smarter machines using natural intelligence as a model, and the science of understanding natural intelligence using machines as models. Our group has internationally-known researchers in both of these areas.

Research

Current research activities for this group can be placed under three broad headings.

Agents

The exploration of the specification, validation and application of institutional frameworks that can be used to mediate agent-to-agent and agent-to-human interaction.

Current research includes:

• the delivery of culture and tourism information to wireless devices (involving mobility, user modelling and information retrieval)
• discovery and orchestration of mathematical web services (involving semantic brokerage, mathematical reasoning and ontology development)
• music, where the goal is that agents can interpret a score in real-time and interact with human performers, possibly in distant geographical locations, to create a unified musical performance (involving ontology development, web services and institutions)

There is also research on institutions themselves, including how to represent the rules that institutional participants should observe and how software agents might reason about them.

Artificial models of natural intelligence

This research area is dedicated to using the best available AI technology in order to advance the scientific understanding of animal and human behaviour.

Current research includes:

• building a complete model of a jellyfish nervous system
• explaining the differences between different species of monkey (macaque) social organizations
• modelling the origins of human language
• explaining the role of the hippocampal system in learning associative knowledge
• explaining the coordination of social predators such as lions, wolves and tuna
• making smarter VR game opponents through observational learning
• providing intelligent assistants for dementia patients so that they can live longer in their own homes
• spiking neural networks
• realistic emotions for avatars and intelligent assistants
• modelling social behaviour, the evolution of human behaviour, and task learning
• machine learning and artificial life
• AI music systems

Knowledge representation and reasoning

Knowledge can be studied through logic, sociology, biology and psychology but, from whichever perspective, the goal is the same: to represent the knowledge effectively and to reason efficiently.

Applications include:

• web-service for mathematics
• data-mining
• decision-problems
• diagnostic application
• negotiating multi-agent frameworks
• the semantic web
• reactive planners

An alternative approach to this topic is machine learning, through which we can discover or infer knowledge by training or experience. We are interested in how to learn compact knowledge structures reliably that perform complex tasks accurately using simple rewards, whilst finding the optimal generalisations. We are also seeking to develop genetic-based machine learning techniques that autonomously discretise the environment to build high-level internal models that can be used to enhance future learning and performance.

Current work uses decidable fractions of logic, non-monotonic reasoning structures, and action languages. The research includes:

• semantic web services (mathematics and music)
• semantic brokerage
• mathematical reasoning
• answer set programming
• ontologies and ontological reasoning
• game theory
• reactive planning and goal arbitration

PhDs

There are opportunities for postgraduate research throughout our group. Interested students can either contact academic staff directly or see the Computer Science PhD project page.

Our website also contains more information on Computer Science PhDs or, for more general information on postgraduate study, please visit the Faculty of Science Graduate School.