Abstract: In this article we present a hierarchical model for planning and coordinating firefighting in situations such as RoboCup Rescue, where an urban earthquake is simulated. We show that a hierarchical approach brings leverage to the planning process. By using formal decision theoretic models, we also present a more formal analysis of the RoboCup Rescue domain. Finally we discuss how our model could be applied and treat further abstractions that may be necessary.
Abstract: Control theory is a mathematical description of how to act
optimally to gain future rewards. In this paper We discuss
a class of non-linear stochastic control problems that can be
eﬃciently solved using a path integral. In this control formalism, the central concept of cost-to-go or value function
becomes a free energy and methods and concepts from statistical physics can be readily applied, such as Monte Carlo
sampling or the Laplace approximation. When applied to a
receding horizon problem in a stationary environment, the
solution resembles the one obtained by traditional reinforcement learning with discounted reward. It is shown that this
solution can be computed more eﬃciently than in the discounted reward framework. As shown in previous work, the
approach is easily generalized to time-dependent tasks and
is therefore of great relevance for modeling real-time interactions between agents.
Abstract: We propose a formal framework based on finite state process algebra for modeling service coordination in agent systems. The model is inspired by WS-coordination specification. Several participant agents can coordinate their actions towards reaching a semantically consistent outcome in a distributed activity. The model defines coordinator and participant agents and their generic interaction protocols and allows integration of application specific protocols by means of protocol handlers. The approach is demonstrated by a sample model showing how coordination is achieved between a set of agents involved in contracting activities.
Abstract: Control theory is a mathematical description of how to act optimally to gain future rewards. In this paper I give an introduction to deterministic and stochastic control theory and I give an overview of the possible application of control theory to the modeling of animal behavior and learning. I discuss a class of non-linear stochastic control problems that can be efficiently solved using a path integral or by MC sampling. In this control formalism the central concept of cost-to-go becomes a free energy and methods and concepts from statistical physics can be readily applied.
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 machine learning 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: In the current paper, the Promedas model for internal medicine,
developed by our team, is introduced. The model is based on up-to-date
medical knowledge and consists of approximately 2000 diagnoses, 1000
ndings and 8600 connections between diagnoses and ndings, covering
large parts of internal medicine. Promedas is currently being evaluated
informally by specialists in internal medicine at the Utrecht university
hospital and is receiving positive responses.We show that Belief Propagation (BP) can be successfully applied to approximate inference problems
in the Promedas network. BP converges on all patient test cases, which
were generated with the help of the model itself. In some cases, however,
we nd errors that are too large for this application. We apply a recently
developed method that improves the BP results by means of a loop expansion scheme. This method, termed Loop Corrected (LC) BP, is able
to improve the marginal probabilities signicantly, leaving a remaining
error which is acceptable for the purpose of medical diagnosis.
Abstract: The successful application of ubiquitous computing in crisis management requires a thorough understanding of the mechanisms that extract information from sensors and communicate it via PDA’s to crisis workers. Whereas query and subscribe protocols are well studied mechanisms for information exchange between different computers, it is not straightforward how to apply them
for communication between a computer and a human crisis worker, with limited cognitive resources. To examine the imposed cognitive load, we focus on the relation of the information supply mechanism with the workflow, or task model, of the crisis worker.We formalize workflows and interaction mechanisms in colored Petri nets, specify various ways to relate them and discuss their pros and cons.
Abstract: In this paper it is shown how specification of behavioural requirements from informal to formal can be integrated within knowledge engineering. The integration of requirements specification has addressed, in particular: the integration of requirements acquisition and specification with ontology acquisition and specification, the relations between requirements specifications and specifications of task models and problem solving methods, and the relation of requirements specification to verification.
Abstract: We consider the problem of cooperative multiagent planning under uncertainty, formalized as a decentralized partially observable Markov decision process (Dec-POMDP). Unfortunately, in these models optimal planning is provably intractable. By communicating their local observations before they take actions, agents synchronize their knowledge of the environment, and the planning problem reduces to a centralized POMDP. As such, relying on communication significantly reduces the complexity of planning. In the real world however, such communication might fail temporarily. We present a step towards more realistic communication models for Dec-POMDPs by proposing a model that: (1) allows that communication might be delayed by one or more time steps, and (2) explicitly considers future probabilities of successful communication. For our model, we discuss how to efﬁciently compute an (approximate) value function and corresponding policies, and we demonstrate our theoretical results with encouraging experiments.
Abstract: Optimal control theory is a mathematical description of how to act optimally
to gain future rewards. In this paper I give an introduction to
deterministic and stochastic control theory; partial observability,
learning and the combined problem of inference and control. Subsequently, I
discuss a new class of non-linear stochastic
control problems for which the Bellman equation becomes linear in the
control and that can be efficiently solved using a path integral.
In this control formalism the central concept of cost-to-go becomes a
free energy and methods and concepts from probabilistic graphical
models and statistical physics can be readily applied. I illustrate the
theory with a number of examples.