Behavioural cloning is a simple and effective technique for automatically and non-intrusively producing comprehensible and implementable models of human control skill. Behavioural cloning applies machine learning techniques to behavioural trace data, in a transparent manner, and has been very successful in a wide range of domains. The limitations of early behavioural cloning work are: that the clones lack goal-structure, are not robust to variation, are sensitive to the nature of the training data and often produce complicated models of the control skill. Recent behavioural cloning work has sought to address these limitations by adopting goal-structured task decompositions and combining control engineering representations with more sophisticated machine learning algorithms. These approaches have had some success but by compromising either transparency or robustness. This thesis addresses these limitations by investigating: new behavioural cloning representations, control structures, data processing techniques, machine learning algorithms, and performance estimation and testing techniques. First a novel hierarchical decomposition of control is developed, where goal settings and the control skill to achieve them are learnt. This decomposition allows feedback control mechanisms to be combined with modular goal-achievement. Data processing limitations are addressed by developing data-driven, correlative and sampling techniques, that also inform the development of the learning algorithm. The behavioural cloning process is developed by performing experiments on simulated aircraft piloting tasks, and then the generality of the process is tested by performing experiments on simulated gantry-crane control tasks. The performance of the behavioural cloning process was compared to existing techniques, and demonstrated a marked improvement over these methods. The system is capable of handling novel goal-settings and task structure, under high noise conditions. The ability to produce successful controllers was greatly improved by using the developed control representation, data processing and learning techniques. The models produced are compact but tend to abstract the originating control behaviour. In conclusion, the control representation and cloning process address current limitations of behavioural cloning, and produce reliable, reusable and readable clones.

Over the past two decades graphical models have been widely used as powerful tools for compactly representing distributions. On the other hand, kernel methods have been used extensively to come up with rich representations. This thesis aims to combine graphical models with kernels to produce compact models with rich representational abilities. Graphical models are a powerful underlying formalism in machine learning. Their graph theoretic properties provide both an intuitive modular interface to model the interacting factors, and a data structure facilitating efficient learning and inference. The probabilistic nature ensures the global consistency of the whole framework, and allows convenient interface of models to data. Kernel methods, on the other hand, provide an effective means of representing rich classes of features for general objects, and at the same time allow efficient search for the optimal model. Recently, kernels have been used to characterize distributions by embedding them into high dimensional feature space. Interestingly, graphical models again decompose this characterization and lead to novel and direct ways of comparing distributions based on samples. Among the many uses of graphical models and kernels, this thesis is devoted to the following four areas: Conditional random fields for multi-agent reinforcement learning Conditional random fields (CRFs) are graphical models for modelling the probability of labels given the observations. They have traditionally been trained with using a set of observation and label pairs. Underlying all CRFs is the assumption that, conditioned on the training data, the label sequences of different training examples are independent and identically distributed (iid ). We extended the use of CRFs to a class of temporal learning algorithms, namely policy gradient reinforcement learning (RL). Now the labels are no longer iid. They are actions that update the environment and affect the next observation. From an RL point of view, CRFs provide a natural way to model joint actions in a decentralized Markov decision process. They define how agents can communicate with each other to choose the optimal joint action. We tested our framework on a synthetic network alignment problem, a distributed sensor network, and a road traffic control system. Using tree sampling by Hamze & de Freitas (2004) for inference, the RL methods employing CRFs clearly outperform those which do not model the proper joint policy. Bayesian online multi-label classification Gaussian density filtering (GDF) provides fast and effective inference for graphical models (Maybeck, 1982). Based on this natural online learner, we propose a Bayesian online multi-label classification (BOMC) framework which learns a probabilistic model of the linear classifier. The training labels are incorporated to update the posterior of the classifiers via a graphical model similar to TrueSkill (Herbrich et al., 2007), and inference is based on GDF with expectation propagation. Using samples from the posterior, we label the test data by maximizing the expected F-score. Our experiments on Reuters1-v2 dataset show that BOMC delivers significantly higher macro-averaged F-score than the state-of-the-art online maximum margin learners such as LaSVM (Bordes et al., 2005) and passive aggressive online learning (Crammer et al., 2006). The online nature of BOMC also allows us to effciently use a large amount of training data. Hilbert space embedment of distributions Graphical models are also an essential tool in kernel measures of independence for non-iid data. Traditional information theory often requires density estimation, which makes it unideal for statistical estimation. Motivated by the fact that distributions often appear in machine learning via expectations, we can characterize the distance between distributions in terms of distances between means, especially means in reproducing kernel Hilbert spaces which are called kernel embedment. Under this framework, the undirected graphical models further allow us to factorize the kernel embedment onto cliques, which yields efficient measures of independence for non-iid data (Zhang et al., 2009). We show the effectiveness of this framework for ICA and sequence segmentation, and a number of further applications and research questions are identified. Optimization in maximum margin models for structured data Maximum margin estimation for structured data, e.g. (Taskar et al., 2004), is an important task in machine learning where graphical models also play a key role. They are special cases of regularized risk minimization, for which bundle methods (BMRM, Teo et al., 2007) and the closely related SVMStruct (Tsochantaridis et al., 2005) are state-of-the-art general purpose solvers. Smola et al. (2007b) proved that BMRM requires O(1/έ) iterations to converge to an έ accurate solution, and we further show that this rate hits the lower bound. By utilizing the structure of the objective function, we devised an algorithm for the structured loss which converges to an έ accurate solution in O(1/√έ) iterations. This algorithm originates from Nesterov's optimal first order methods (Nesterov, 2003, 2005b).

Behavioural cloning is a simple and effective technique for automatically and non-intrusively producing comprehensible and implementable models of human control skill. Behavioural cloning applies machine learning techniques to behavioural trace data, in a transparent manner, and has been very successful in a wide range of domains. The limitations of early behavioural cloning work are: that the clones lack goal-structure, are not robust to variation, are sensitive to the nature of the training data and often produce complicated models of the control skill. Recent behavioural cloning work has sought to address these limitations by adopting goal-structured task decompositions and combining control engineering representations with more sophisticated machine learning algorithms. These approaches have had some success but by compromising either transparency or robustness. This thesis addresses these limitations by investigating: new behavioural cloning representations, control structures, data processing techniques, machine learning algorithms, and performance estimation and testing techniques. First a novel hierarchical decomposition of control is developed, where goal settings and the control skill to achieve them are learnt. This decomposition allows feedback control mechanisms to be combined with modular goal-achievement. Data processing limitations are addressed by developing data-driven, correlative and sampling techniques, that also inform the development of the learning algorithm. The behavioural cloning process is developed by performing experiments on simulated aircraft piloting tasks, and then the generality of the process is tested by performing experiments on simulated gantry-crane control tasks. The performance of the behavioural cloning process was compared to existing techniques, and demonstrated a marked improvement over these methods. The system is capable of handling novel goal-settings and task structure, under high noise conditions. The ability to produce successful controllers was greatly improved by using the developed control representation, data processing and learning techniques. The models produced are compact but tend to abstract the originating control behaviour. In conclusion, the control representation and cloning process address current limitations of behavioural cloning, and produce reliable, reusable and readable clones.

Thesis (M.S.)--Brigham Young University. Dept. of Computer Science, 2006. / Includes bibliographical references (p. 50-53).

Thesis (M.S.)--West Virginia University, 2008. / Title from document title page. Document formatted into pages; contains vii, 83 p. : ill. (some col.). Vita. Includes abstract. Includes bibliographical references (p. 81-83).

Thesis (Ph.D.)--Tufts University, 2004. / Adviser: Roni Khardon. Submitted to the Dept. of Computer Science. Includes bibliographical references (leaves 157-161). Access restricted to members of the Tufts University community. Also available via the World Wide Web;

Thesis (Ph.D.) -- McMaster University, 1998. / Includes bibliographical references (leaves 139-147). Also available via World Wide Web.

Thesis (Ph. D.)--University of Missouri--Rolla, 2007. / Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed November 27, 2007) Includes bibliographical references (p. 124-137).

Thesis (Ph. D.)--Georgia State University, 2007. / Title from file title page. Melody Moore, committee chair; Detmar Straub, Veda Storey, Bruce Walker, committee members. Electronic text (166 p. : ill. (some col.)) : digital, PDF file. Description based on contents viewed Nov. 5, 2007. Includes bibliographical references (p. 160-164).

Thesis (M.S.)--Ohio University, August, 2000. / Title from PDF t.p.