ion and delegation: Agents can be made extensible and composable in ways that common iconic interface objects cannot. Because we can "communicate" with them, they can share our goals, rather than simply process our commands. They can show us how to do things and tell us what went wrong (Miller and Neches 1987). . Flexibility and opportunism: Because they can be instructed at the level of 16 BRADSHAW goals and strategies, agents can find ways to "work around" unforeseen problems and exploit new opportunities as they help solve problems. . Task orientation: Agents can be designed to take the context of the person's tasks and situation into account as they present information and take action. . Adaptivity: Agents can use learning algorithms to continually improve their behavior by noticing recurrent patterns of actions and events. Toward Agent-Enabled System Architectures In the future, assistant agents at the user interface and resource-managing agents behind the scenes will increas...

In this paper we propose and justify a methodology for the development of the control systems, or `cognitive architectures', of autonomous mobile robots. We argue that the design by hand of such control systems becomes prohibitively difficult as complexity increases. We discuss an alternative approach, involving artificial evolution, where the basic building blocks for cognitive architectures are adaptive noise-tolerant dynamical neural networks, rather than programs. These networks may be recurrent, and should operate in real time. Evolution should be incremental, using an extended and modified version of genetic algorithms. We nally propose that, sooner rather than later, visual processing will be required in order for robots to engage in non-trivial navigation behaviours. Time constraints suggest that initial architecture evaluations should be largely done in simulation. The pitfalls of simulations compared with reality are discussed, together with the importance of incorporating noise. To support our claims and proposals, we present results from some preliminary experiments where robots which roam office-like environments are evolved.

We are quickly passing through the historical moment when people work in front of a single computer, dominated by a small CRT and focused on tasks involving only local information. Networked computers are becoming ubiquitous and are playing increasingly significant roles in our lives and in the basic infrastructures of science, business, and social interaction. For human-computer interaction to advance in the new millennium we need to better understand the emerging dynamic of interaction in which the focus task is no longer confined to the desktop but reaches into a complex networked world of information and computer-mediated interactions. We think the theory of distributed cognition has a special role to play in understanding interactions between people and technologies, for its focus has always been on whole environments: what we really do in them and how we coordinate our activity in them. Distributed cognition provides a radical reorientation of how to think about designing and supporting human-computer interaction. As a theory it is specifically tailored to understanding interactions among people and technologies. In this article we propose distributed cognition as a new foundation for human-computer interaction, sketch an integrated research framework, and use selections from our earlier work to suggest how this framework can provide new opportunities in the design of digital work materials.

…Truth [is] being involved in an eternal conversation about things that matter, conducted with passion and discipline…truth is not in the conclusions so much as in the process of conversation itself…if you want to be in truth you must be in conversation. Parker Palmer

This paper proposes a research direction to study the development of `artificial social intelligence' of autonomous robots which should result in `individualized robot societies'. The approach is highly inspired by the `social intelligence hypothesis', derived from the investigation of primate societies, suggesting that primate intelligence originally evolved to solve social problems and was only later extended to problems outside the social domain. We suggest that it might be a general principle in the evolution of intelligence, applicable to both natural and artificial systems. Arguments are presented why the investigation of social intelligence for artifacts is not only an interesting research issue for the study of biological principles, but may be a necessary prerequisite for those scenarios in which autonomous robots are integrated into human societies, interacting and communicating both with humans and with each other. As a starting point to study experimentally the development ...

Behavior-oriented AI is a scientific discipline that studies how behavior of agents emerges and becomes intelligent and adaptive. Success of the field is defined in terms of success in building physical agents that are capable of maximising their own self-preservation in interaction with a dynamically changing environment. The paper addresses this artificial life route towards artificial intelligence and reviews some of the results obtained so far. 1 Official reference: Steels, L. (1994) The artificial life roots of artificial intelligence. Artificial Life Journal, Vol 1,1. MIT Press, Cambridge. 1 Introduction For several decades, the field of Artificial Intelligence has been pursuing the study of intelligent behavior using the methodology of the artificial [104]. But the focus of this field, and hence the successes, have mostly been on higher order cognitive activities such as expert problem solving. The inspiration for AI theories has mostly come from logic and the cognitive...

Developmental robotics is an emerging field located at the intersection of robotics, cognitive science and developmental sciences. This paper elucidates the main reasons and key motivations behind the convergence of fields with seemingly disparate interests, and shows why developmental robotics might prove to be beneficial for all fields involved. The methodology advocated is synthetic and two-pronged: on the one hand, it employs robots to instantiate models originating from developmental sciences; on the other hand, it aims to develop better robotic systems by exploiting insights gained from studies on ontogenetic development. This paper gives a survey of the relevant research issues and points to some future research directions.

The role that affective states play in learning was investigated from the perspective of a constructivist learning framework. We observed six different affect states (frustration, boredom, flow, confusion, eureka and neutral) that potentially occur during the process of learning introductory computer literacy with AutoTutor, an intelligent tutoring system with tutorial dialogue in natural language. Observational analyses revealed significant relationships between learning and the affective states of boredom, flow and confusion. The positive correlation between confusion and learning is consistent with a model that assumes that cognitive disequilibrium is one precursor to deep learning. The findings that learning correlates negatively with boredom and positively with flow are consistent with predictions from Csikszentmihalyi’s analysis of flow experiences.

... This paper develops active strategies for a robot to acquire visual experience through simple experimental manipulation. The experiments are oriented towards determining what parts of the environment are physically coherent -- that is, which parts will move together, and which are more or less independent. We argue that following causal chains of events out from the robot's body into the environment allows for a very natural developmental progression of visual competence, and relate this idea to results in neuroscience.

An Open Source Software (OSS) project is unlikely to be successful unless there is an accompanied community that provides the platform for developers and users to collaborate. Members of such communities are volunteers whose motivation to participate and contribute is of essential importance to the success of OSS projects. In this paper, we aim to create an understanding of what motivates people to participate in OSS communities. We theorize that learning is one of the motivational forces. Our theory is grounded in the learning theory of Legitimate Peripheral Participation, and is supported by analyzing the social structure of OSS communities and the co-evolution between OSS systems and communities. We also discuss practical implications of our theory for creating and maintaining sustainable OSS communitie as well as for software engineering research and education..