**
IEEE International Conference on Intelligent Systems, London, 2010.
**

**
High level conceptual thought seems to be at the basis of the
impressive human cognitive ability, and AI researchers aim to
replicate this ability in artificial agents. Classical top-down
(Logic based) and bottom-up (Connectionist) approaches to the problem
have had limited success to date. We review a small body of work that
represents a different approach to AI. We call this work the Bottom
Up Symbolic (BUS) approach and present a new BUS method to concept
construction. While valid concepts have been constructed using
previous methods under this approach, we show in this paper that the
one-sided clustering methods generally used there may fail to uncover
valid concepts even when they clearly exist. We show that by using a
Co-clustering algorithm that searches for an optimal partitioning
based on the Mutual Information between the category and consequent
components of a concept, the concept formation outcome is improved. We
test our approach on data from experiments using a real mobile robot
operating in the real world, and show that our Co-clustering based
approach leads to significant performance improvement compared to
previous approaches.
**

**
The International Conference on Intelligent Agent Technology, Milan, 2009
**

**
High level conceptual thought seems to be at the basis of the
impressive human cognitive ability. Classical top-down (Logic based)
and bottom-up (Connectionist) approaches to the problem have had
limited success to date. We identify a small body of work that
represents a different approach to AI. We call this work the Bottom
Up Symbolic (BUS) approach and present a new BUS method to concept
construction. The main novelty of our work is that we apply
statistical methods in the concept construction process. Our
findings here suggest that such methods are necessary since a
symbolic description of the true agent-environment interaction
dynamics is often hidden among a background of non-representative
descriptions, especially if data from unconstrained real-world
experiments is considered. We consider such data (from a mobile
robot randomly roaming an office environment) and show how our
method can correctly grow a set of true concepts from the data.
**

**
The Encyclopedia of Data Warehousing and Mining (2nd Edition) , Ed. J.Wang, IGI Global, 2008
**

**
Clustering analysis is a tool used widely in the Data Mining
community and beyond (Everitt et al. 2001). In essence, the method
allows us the information in a large data set X by creating a very
much smaller set C of representative points (called centroids) and a
membership map relating each point in X to its representative in C. An
obvious but special type of data set that one might want to cluster is
a time series data set. Such data has a temporal ordering on its
elements, in contrast to non-time series data sets. In this article we
explore the area of time series clustering, focusing mainly on a
surprising recent result showing that the traditional method for time
series clustering is meaningless. We then survey the literature of
recent papers and go on to argue how time series clustering can be
made meaningful.
**

**
The Australasian Data Mining Conference, Gold Coast, Australia, 2007
**

**
Clustering time series data using the popular subsequence (STS)
technique has been widely used in the data mining and wider
communities. Recently the conclusion was made that it is
meaningless, based on the findings that it produces (a) clustering
outcomes for distinct time series that are not distinguishable from
one another, and (b) cluster centroids that are smoothed. More
recent work has since showed that (a) could be solved by introducing
a lag in the subsequence vector construction process, however we
show in this paper that such an approach does not solve (b).
Motivating the terminology that a clustering method which overcomes
(a) is meaningful, while one which overcomes (a) and (b) is useful,
we propose an approach that produces useful time series
clustering. The approach is based on restricting the clustering
space to extend only over the region visited by the time series in
the subsequence vector space. We test the approach on a set of 12
diverse real-world and synthetic data sets and find that (a) one can
distinguish between the clusterings of these time series, and (b)
that the centroids produced in each case retain the character of the
underlying series from which they came.
**

**
Knowledge and Information Systems, Springer
**

**
Sequential time series clustering is a technique used to extract
important features from time series data. The method can be shown to
be the process of clustering in the Delay-Vector space formalism
used in the Dynamical systems literature. Recently, the startling
claim was made that sequential time series clustering is
meaningless. This has important consequences for a significant
amount of work in the literature, since such a claim invalidates
these work's contribution. In this paper, we show that sequential
time series clustering is not meaningless, and that the problem
highlighted in these works stem from their use of the Euclidean
distance metric as the distance measure in the delay vector space.
As a solution, we consider quite a general class of time series, and
propose a regime based on two types of similarity that can exist
between delay vectors, which give rise naturally to an alternative
distance measure to Euclidean distance in the delay vector space. We
show that, using this alternative distance measure, sequential time
series clustering can indeed be meaningful.
**

**
Int. Conf. Data Mining, Houston Tex. Nov. 2005
**

** Recently, the startling claim was made that sequential time series
clustering is meaningless. This has important consequences for a
significant amount of work in the literature, since such a claim
invalidates this work's contribution. In this paper, we show that
sequential time series clustering is not meaningless, and that the
problem highlighted in these works stem from their use of the
Euclidean distance metric as the distance measure in the subsequence
vector space. As a solution, we consider quite a general class of
time series, and propose a regime based on two types of similarity
that can exist between subsequence vectors, which give rise
naturally to an alternative distance measure to Euclidean distance
in the subsequence vector space. We show that, using this
alternative distance measure, sequential time series clustering can
indeed be meaningful.
**

**
Int. J. Robotics Research, December 2005
**

**Programming by Demonstration (PbD) is a technique for programming
robots that holds much promise in making robots more accessible to
ordinary, non-technical users. However, a well known difficulty with
the method is that a human will often demonstrate the task to be
programmed inconsistently or even erroneously, leading to the
inclusion of what is essentially noise in the demonstration. A number
of techniques exist in the literature for filtering out this type of
noise, however most focus on very low level control command
details. In this paper we propose a new, complimentary direction of
research. We take a ``task-level'' view of the demonstration, and note
that noise can exist at this level also. We propose a framework, based
on a Hybrid Dynamic System modelling approach, to select the most
optimal, task-level execution strategies that were demonstrated. We
apply our framework to a real household task of inserting the
compressible spindle of a paper towel holder into its supports. We
conduct experiments to show that significant improvements in robot
performance of the task can be achieved by a PbD regime that includes
our method.
**

**Int. J. Robotics Research, vol. 22, no. 5, pp. 299-319, May 2003
© 2003 Massachusetts Institute of Technology**

The difficulty associated with programming existing robots is one of the main impediments to them finding application in domestic environments like the home. A promising method for simplifying robot programming is Programming by Demonstration (PbD). Here, an end user can provide a demonstration of the task to be programmed, with a PbD ``interface'' interpreting the demonstration in order to determine low-level control details for the robot. A key aspect of the interpretation process is to make it robust to the noise typically included in a demonstration by the human. In this paper we present a method to help identify and eliminate any noise present in the demonstration. Our method involves two steps. The first step uses the demonstration to build-up a partial knowledge of the geometry present in the task. Statistical regression analysis is used on demonstrated trajectories to determine equations describing C-surfaces in Configuration Space. The second step in our method uses the geometric information obtained in the first step to determine if there are more optimal paths than those demonstrated for completing the task. If there are, our method proposes these as the appropriate control commands for the robot. We show the validity of our approach by presenting successful experiments on a realistic household-type task - changing rolls on a paper roll holder.

**Unpublished PhD Thesis, July 2001
**

Finding a simple but powerful robot programming method for realistic
tasks has been one of the main aims of robotics researchers for over
two decades. A promising approach is robot Programming by
Demonstration (PbD). Here, a demonstration of the task is interpreted
by a PbD interface so that a set of control commands to achieve the
task are produced for the robot. PbD is a promising approach to robot
programming, however a well known weakness of the method is that human
demonstrations can be suboptimal. Research has identified that
demonstrations can contain inconsistencies, noise, or even incorrect
or unintended actions. In this thesis our focus is on identifying and
removing sub-optimality from the demonstration. Our aim is to ensure
that control commands formed for the robot from demonstrated actions
encode efficient and reliable execution of the task. The work we propose
is divided into three distinct areas. They are: (i) determining
task-specific, geometric properties of a task from demonstration, (ii)
deriving efficient, low-level, robot control-commands from
demonstration, and (iii) determining optimal task-level strategies
from demonstration.

Research area (i) is important since knowledge of task geometry can
help identify the presence of suboptimal actions in the
demonstration. Our solution uses the concept of Configuration Space
(C-space) as a means to represent task geometry. We apply statistical
regression analysis to build-up a knowledge of task geometry in
regions of the task that were visited in the
demonstration. Experimental results showed the validity of the
approach. The two key results were, first, that only a partial
representation of C-space could be determined. That is, a
representation of C-space was determined only for regions of the task
visited in the demonstration. Second, the method could determine quite
accurately the true geometric properties of the task, so long as it
had sufficient information to do so. That is, C-space was derived
accurately in regions visited often in the demonstration, and less
accurately in less-visited regions.
%-what is a region of the task??

Research area (ii) involves deriving low-level control commands for
the robot from demonstration, and has been the main focus of research
into removing demonstration sub-optimality in PbD to date. In this
thesis we adopt the well known control regime of hybrid force-position
control, and so the problem divides into two sub-areas: position
control-command synthesis, and force control-command synthesis. For
position control-command synthesis we present a novel method for path
planning in a partially known C-space. The method has the advantages
compared to other methods in the literature that, it can derive paths
containing undemonstrated points, it is applicable to a task with any
degree of freedom, and that it does not assume a set form of
demonstration topology. A drawback of the approach is that,
theoretically, in some circumstances a valid control command will not
be derived. However, we show with experimental results for a realistic
task how with appropriate tuning, the method will produce a valid set
of position control commands. For force control-command synthesis, a
well known characteristic of force commands recorded from a
demonstration are that: they include friction, and that force sensors
introduce into these forces a high frequence noise component. We use
our knowledge of C-space to determine an appropriate direction for
force control. We base the magnitude of force control commands on the
forces commanded by the human in the demonstration, however we remove
friction and noise using filtering and spline fitting techniques.

Research area (iii) has to our knowledge not been the subject of work
in the PbD field to date. In this thesis, we identify that a set of
demonstrations will contain many different task-level strategies. Some
of these will result in more optimal robot performance of the task
than others. We present a method for selecting a task-level strategy
for the robot. It is based on the concept of modeling skill in a task
as a Hybrid Dynamic System. The Hybrid Dynamic System representation
allows a demonstration to be represented at a task level as a sequence
of discrete events. Our method is to form an automaton from the event
sequences encoded by each demonstration. We use a set of metrics to
determine a cost for each event in the automaton. Finally, an optimal
task level strategy is produced by conducting a least cost path search
in the automaton. We show with experiments how the task-level
strategies selected by our method do in fact result in more optimal
robot performance of the task.