Abstract: Facial related analysis represented milestones in the fields of computer vision for many decades. Lots of methods have been designed and implemented so as to solve the specific requirements. One of the methods, Relevance Vector Machines (RVM) stands
for a novel supervised learning technique that is based on a probabilistic approach of Support Vector Machines. The data for training were selected from the Cohn-Kanade Facial Expression Database. The application associated with the current research aims
at demonstrating the use of the RVM as a novel classifier for face detection.
Abstract: This paper discusses inference problems in probabilistic graphical models that often occur in a machinelearning setting. In particular it presents a unified view of several recently proposed approximation schemes. Expectation consistent approximations and expectation propagation are both shown to be related to Bethe free energies with weak consistency constraints, i.e. free energies where local approximations are only required to agree on certain statistics instead of full marginals.
Abstract: Our work addresses the problem of autonomous concept formation from a design point of view, providing an initial answer to the question: What are the design features of an architecture supporting the acquisition of different types of concepts by an autonomous agent?
Autonomous agents, that is systems capable of interacting independently with their environment in the pursuit of their own goals, will provide the framework in which we study the problem of autonomous concept formation. Humans and most animals may in this sense also be regarded as autonomous agents, but our concern will be with artiﬁcial autonomous agents. A detailed survey and discussion of the many issues surrounding the notion of ‘artiﬁcial agency’ is beyond the scope of this thesis and a good overview can be found in [Wooldridge and Jennings, 1995]. Instead we will focus on how artiﬁcial agents could be endowed with representational and modelling capabilities.
The ability to form concepts is an important and recognised cognitive ability, thought to play an essential role in related abilities such as categorisation, language understanding, object identiﬁcation and recognition, reasoning, all of which can be seen as different aspects of intelligence. Concepts and categories are studied within cognitive science, where scientists are concerned with human conceptual abilities and mental representations of categories, but they have been addressed also in the rather different domain of machinelearning and classiﬁcatory data analysis, where the focus is on the development of algorithms for clustering problems and induction problems [Mechelen et al., 1993]. The two ﬁelds are well distinct and only recently have started to interact, but even though the importance of concepts have been recognised, the nature of concepts is controversial, in the sense that there is no commonly agreed theory of concepts, and it is still far from obvious which representational means are most suited to capture the many cognitive functions that concepts are involved in.
Among the goals of this thesis there is the attempt to bring together different lines of argumentation that have emerged within philosophy, cognitive science and AI, in order to establish a solid foundation for further research into the representation and acquisition of concepts by autonomous agents. Thus, our results and conclusions will often be stated in terms of new insights and ideas, rather than resulting in new algorithms or formal methods.
Our focus will be on affordance concepts — discussed in detail in Chapter 4 — and our main contributions will be:
* An argument showing that concepts should be thought of as belonging to different kinds, where the differences among these kinds are to be captured in terms of architecture features supporting their acquisition.
* A description (and partial implementation) of a minimal architecture (the Innate Adaptive Behaviour architecture – IAB architecture for short) supporting the acquisition of affordance concepts; the IAB architecture is actually a proposal for a sustaining mechanism, in the sense of [Margolis, 1999], for affordances, and makes clear the necessity of a minimal structure for the representation of affordances.
When addressing concept formation in AI, what can be called the ‘system level’ is often overlooked, which means that concepts and categories are rarely studied from the point of view of a system, autonomous and complete, that might need such constructs and can acquire them only by means of interactions with its environment, under the constraints of its cognitive architecture. Also within psychology, the focus is usually on structural aspects of concepts rather than on developmental issues [Smith and Medin, 1981]. Our approach – an architecture-based approach – is an attempt (i) to show that a system level perspective on concept formation is indeed possible and worth exploring, and (ii) to provide an initial, maybe simple, but concrete example of the insights that can be gained from such an approach. Since the methodology that we propose to study concept formation is a general one, and can be applied also to other types of concepts, we decided to mention broadly ‘autonomous concept formation’ rather than ‘autonomous affordance-concepts formation’ in the title of the thesis.
Abstract: Free space detection based on visual clues is an
upcoming approach in robotics. Our working domain is the
Virtual Rescue League of the RoboCup. In this domain efficient
obstacle avoidance is crucial to find victims under challenging
conditions. In this study a machine-learning approach is applied
to distinguish the difference in visual appearance of obstacles
and free space. Omnidirectional camera images are transformed
to bird-eye view, which makes comparison with local occupancy
maps possible. Bird-eye view images are automatically labeled
using Laser Range information, allowing completely autonomous
and continuous learning of accurate color models. Two colorbased
models are compared; a Histogram Method and a Gaussian
Mixture Model. Both methods achieve very good performances,
with results in a high precision and recall on a typical map
from the Rescue League. The Gaussian Mixture Model achieves
the best scores with much less parameters on this map, but is
beaten by the Histogram Method on real data collected by our
Nomad robot. Additionally, the importance of the right color
normalization scheme and model parameters is demonstrated in
Abstract: The increasing complexity of our world demands new perspectives on the role of technology in human decision making. We need new technology to cope with the increasingly complex and information-rich nature of our modern society. This is particularly true for critical environments such as crisis management and traffic management, where humans need to engage in close collaborations with artificial systems to observe and understand the situation and respond in a sensible way. The book Interactive Collaborative Information Systems addresses techniques that support humans in situations in which complex information handling is required and that facilitate distributed decision-making. The theme integrates research from information technology, artificial intelligence and human sciences to obtain a multidisciplinary foundation from which innovative actor-agent systems for critical environments can emerge. It emphasizes the importance of building actor-agent communities: close collaborations between human and artificial actors that highlight their complementary capabilities in situations where task distribution is flexible and adaptive. This book focuses on the employment of innovative agent technology, advanced machinelearning techniques, and cognition-based interface technology for the use in collaborative decision support systems.
Abstract: Facial related analysis represented milestones in the fields of computer vision for many decades. Lots of methods have been designed and implemented so as to solve the specific requirements. In the current paper we present three different classification algorithms that we use to fulfill the tasks concerning face detection and facial expression recognition.
One of the methods, Relevance Vector Machines (RVM) stands for a novel supervised learning technique that is based on a probabilistic approach of Support Vector Machines. The mathematical base of the models is presented. The data for testing were selected from the Cohn-Kanade Facial Expression Database. We report recognition rates for six universal expressions based on a range of experiments. Some discussions on the comparison of different classification methods are included.