Abstract: This paper appeared at the ISCRAM 2010  and presents an intelligent system facilitating better in-
formed decision making under severe uncertainty, as often found in emergency management. The
construction of decision-relevant scenarios, being plausible descriptions of a situation and its future
development, is used as a rationale for collecting, organizing, filtering and processing information for
decision making. The development of scenarios is geared to assessing decision alternatives, thus avoiding
time-consuming analysis and processing of irrelevant information.
The scenarios are constructed in a distributed setting allowing for a flexible adaptation of reasoning (prin-
ciples and processes) to the problem at hand and the information available. Each decision can be founded
on a coherent set of scenarios, which was constructed using the best expertise available within a limited
timeframe. Our theoretical framework is demonstrated in a distributed decision support system by
orchestrating both automated systems and human experts into workflows tailored to each specific problem.
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: This paper describes the sample implementation of a distributed goaloriented reasoning engine for multi-agent systems. The paper summarizes part of the design and programming issues that we addressed for providing the initial prototype with customization and self-configuration facilities.
Abstract: Autonomous agents are believed to have control over their internal state and over their behaviour. For that reason, an agent should control how and by whom it is being influenced. We introduce a reasoning component for BDI-agents that deals with the control over external influences, and we propose heuristics using local knowledge to process incoming stimuli. One of those heuristics is based on information relevance with respect to the agent's current plans and goals. We have developed a way to determine the relevance of information in BDI-agents using magic sets from database research as basis. The method presented shows a new application of magic sets by applying the theory in agent systems.
Abstract: Each person holds numerous values that represent what is
believed to be important. As a result, our values inﬂuence our behavior
and play a role in practical reasoning. Various argumentation approaches
use values to justify actions, but they assume a function that determines
what values a state or action promotes and demotes. However, this is
often open for debate, since values are abstract and can be interpreted
in many ways. After giving an overview of how values are deﬁned in
social psychology, this paper deﬁnes values as preferences and introduces
several argument schemes to reason about preferences. These schemes
are used to give meaning to values and to determine whether values are
promoted or demoted. Furthermore, value systems are used for practical
reasoning and allow resolving conﬂicts when pursuing your values. An
example is given of how the new argument schemes can be used to do
practical reasoning using values.
Abstract: Multi-agent systems get deployed more and more often in settings where they share a common real-world environment. Therefore, it becomes necessary to include sensory information in the reasoning and coordination of agents. The Active Sensor Web (ASW) aims to deliver coherent models and components to this end. The architeccture and some exemplary applications of the ASW are described in this paper.
Abstract: This paper illustrates objectives of the DIADEM project which focuses on a
novel combination of advanced technologies which facilitate collaborative information
processing in environmental management applications. The emphasis is on the principles
and tools which facilitate creation of a single information space in advanced systems of
systems through a systematic integration of heterogeneous processes. In particular, we
illustrate the main principles of the DIADEM Process Integration framework, which
supports collaborative processing based on a combination of automated reasoning
processes and cognitive capabilities of multiple human experts, each contributing specific
expertise and processing resources.