Autonomous behaviour and learning in an uncertain world

The key challenges facing research, development and deployment of autonomous systems require principled solutions in order for scalable systems to become viable. This proposal intertwines probabilistic (Bayesian) inference, model-predictive control, distributed information networks, human-in-the-loop and multi-agent systems to an unprecedented degree. The project focuses on the principled handling of uncertainty for distributed modelling in complex environments which are highly dynamic, communication poor, observation costly and time-sensitive. We aim to develop robust, stable, computationally practical and principled approaches which naturally accommodate these real-world challenges.

Our proposed framework will enable significant progress to be made in a large number of areas essential to intelligent autonomous systems, including 1) the assessment of reliability and fusion of disparate sources of data, 2) allow active data selection based on Bayesian sequential decision making under realistic time, information & computation constraints, 3) allow the advancement of Bayesian reinforcement algorithms in complex systems, and 4) extend Model predictive control (MPC) to probabilistic settings using Gaussian process non-parametric models.

At the systems level, these developments will permit the design of overarching methods for 1) controlled autonomous systems which interact and collaborate, 2) integration of sensing, inference, decision making and learning in acting systems and 3) design methods for validation and verification of systems to enhance robustness and safety.

The ability to meet these objectives depends on a multitude of recent technical developments. These include, 1) development of practical non-parametric algorithms for on-line learning and adaptation 2) approximate inference for Bayesian sequential decision making under constraints, 3) the development of sparse data selection and sparse representation methods for practical handling of large data sets with complex decentralised systems and 4) the implementation of and deployment on powerful modern parallel architectures such as GPUs.

We aim to build on our expertise in Bayesian machine learning, multi-agent systems and control theory and by drawing together closely related developments in these complementary fields we will be able to make substantial improvements to the way artificial agents are able to learn and act, combine and select data sources intelligently, and integrate in robust ways into complex environments with multiple agents and humans in the loop.

Autonomous intelligent systems are expected to have an increasing effect on our lives. Initially they will be used within highly technical systems, such as robots for handling radioactive materials in power stations, for performing military reconnaissance without risking soldiers' lives, for helping surgeons, or for excavating building sites. Later they are likely to become used in situations which involve interaction with the general public, for example to provide assistance to the elderly or disabled, or perhaps to take over the driving of cars.

In order to allow any of these things to be done successfully and safely, research is needed in some underpinning engineering and science. The biggest challenge is that of dealing with the huge amount of uncertainty that will be faced by autonomous intelligent systems - uncertainty that humans can often deal with pretty well, but that we can still handle to only a limited degree in our mathematics and engineering. Therefore the scientific core of our project rests on the use of probability theory and in particular on the use of Bayes' theorem, and a whole family of methods that result from it, for calculating and keeping track of uncertainty in very complex situations. This is necessary if autonomous agents are to act intelligently in non-trivial situations.

Many people have already done plenty of work on 'Bayesian methods', so what is new in this project? The novelty is the use of these methods as a 'glue' to tie together the many and various technologies that are needed to make autonomous intelligent systems work. Some of these (such as flying an aeroplane) are well known and can achieve great precision; others (such as distinguishing a pedestrian in the road from a shadow, or an underground cable from a rock) are still at a relatively early stage of evolution. A key capability required of autonomous intelligent systems is that of planning, and re-planning in the face of incoming evidence and measurements, how to perform or complete a task. This requires some understanding of behaviours, both of one's own system (eg 'how does this aeroplane work?') and of one's environment (eg 'where will that storm go next?'). The methods currently used to represent and analyse these different aspects differ a lot in their mathematical forms. Our ambition is to develop methods, with a Bayesian basis, which will allow them all to be used with each other.

Our project will investigate in detail how to integrate such technologies in a way that enables them all to work together. In addition to discovering how to do this, we will also train a number of researchers in the required techniques, spread the word to other academic researchers, and share our results with a number of companies who are trying to develop autonomous intelligent systems.