Abstract: As UAV systems become more and more sophisticated in terms of intelligence and autonomy,
it becomes increasingly important to find a common language between human operators and
the automation. Not only should the UAV system make good decisions, its decisions should
also be transparent to the operator. This paper looks at the issue of interfacing UAV operators
with their work domain and explores how principles from cognitive system engineering can be
used to provide a shared representation for the system and its operators. Such a shared
representation should allow natural interaction between multiple autonomous agents and
human actors. They should be able to understand each other’s actions in the context of the
Abstract: We introduce a support system concept that offers both work-centered and human-aware support for operators in tactical command and control environments. The support system augments the cognitive capabilities of the operator by offering instant, personalized task and work support. The operator obtains support by entering into a collaborative agreement with support agents. Such an agreement creates opportunities to establish adaptive capabilities and human-aware support features. We describe the concept in and propose an experimental design to evaluate its effectiveness in tactical environments.
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 actor-agent teams human and artificial entities interact and cooperate in order to enhance and augment their individual and joint cognitive ergonomic and problem solving capabilities. Also actor-agent communities can benefit from ‘ambient cognition’, a novel further reaching concept than ambient intelligence that hardly takes into account the resource limitations and capabilities changing over time of both humans and agents in collaborative settings. The Dutch Companion project aims at the
realization of an agent that takes advantage of the ambient cognition concerning actor-agent system dynamics such that natural emotion-sensitive interaction with an actor over a longer period of time can be sustained. We elaborate on our vision of
pursuing ambient cognition within actor-agent systems and present the plans and expected results of the Dutch Companion project.
Abstract: In actor-agent teams human and artiﬁcial entities interact and cooperate in order to enhance and augment their individual and joint cognitive ergonomic and problem solving capabilities. Also actor-agent communities can beneﬁt from ‘ambient cognition’, a novel further reaching concept than ambient intelligence that hardly takes into account the resource limitations and capabilities changing over time of both humans and agents in collaborative settings. The Dutch Companion project aims at the realization of an agent that takes advantage of the ambient cognition concerning actor-agent system dynamics such that natural social and emotion-sensitive interaction with an actor over a longer period of time can be sustained. We elaborate on our vision of pursuing ambient cognition within actor-agent systems and brieﬂy describe the goals of the Dutch Companion project.
Abstract: Emotion has been found to influence humans’ cognitive information processing and decision-making (Schwarz, 2000). A state of sadness, for example, is accompanied by substantive information processing, with greater attention to detail, whereas people in a happier state tend to process information more heuristically. Mobile applications or services presenting information to users, especially those used primarily in emotionally laden contexts, could adapt information
presentation to users’ current emotional state to improve compliance. This paper reports the results of an 2x2 betweensubject survey experiment (N=91) with affective state (happy vs. sad) and information presentation style (heuristic vs.
substantive) as dimensions. The results confirm that participants in a sad affective state are more likely to comply with mobile agents’ advice when information is tailored to a substantive processing style. They base decisions on substantive
information and provide longer descriptions. In contrast, people in a happy affective state prefer heuristic information. These findings reinforce the importance of affect-sensitive adaptation, especially for mobile agents in potentially emotionally laden contexts.