A canonical theory of dynamic decision-making

Decision-making behavior is studied in many very different fields, from medicine and eco- nomics to psychology and neuroscience, with major contributions from mathematics and statistics, computer science, AI, and other technical disciplines. However the conceptual- ization of what decision-making is and methods for studying it vary greatly and this has resulted in fragmentation of the field. A theory that can accommodate various perspectives may facilitate interdisciplinary working. We present such a theory in which decision-making is articulated as a set of canonical functions that are sufficiently general to accommodate diverse viewpoints, yet sufficiently precise that they can be instantiated in different ways for specific theoretical or practical purposes. The canons cover the whole decision cycle, from the framing of a decision based on the goals, beliefs, and background knowledge of the decision-maker to the formulation of decision options, establishing preferences over them, and making commitments. Commitments can lead to the initiation of new decisions and any step in the cycle can incorporate reasoning about previous decisions and the rationales for them, and lead to revising or abandoning existing commitments. The theory situates decision-making with respect to other high-level cognitive capabilities like problem solving, planning, and collaborative decision-making. The canonical approach is assessed in three domains: cognitive and neuropsychology, artificial intelligence, and decision engineering

The organisation of sociality: a manifesto for a new science of multi-agent systems

In this paper, we pose and motivate a challenge, namely the need for a new science of multi-agent systems. We propose that this new science should be grounded, theoretically on a richer conception of sociality, and methodologically on the extensive use of computational modelling for real-world applications and social simulations. Here, the steps we set forth towards meeting that challenge are mainly theoretical. In this respect, we provide a new model of multi-agent systems that reflects a fully explicated conception of cognition, both at the individual and the collective level. Finally, the mechanisms and principles underpinning the model will be examined with particular emphasis on the contributions provided by contemporary organisation theory

Emergent Story Generation: Lessons from Improvisational Theater

An emergent approach to story generation by computer is characterized by a lack of predetermined plot and a focus on character interaction forming the material for stories. A potential problem is that no interesting story emerges. However, improvisational theater shows that – at least for human actors – a predetermined plot is not necessary for creating a compelling story. There are some principles that make a successful piece of improvisational theater more than a random interaction, and these principles may inform the type of computational processes that an emergent narrative architecture draws from. We therefore discuss some of these principles, and show how these are explicitly or implicitly used in story generation and interactive storytelling research. Finally we draw lessons from these principles and ask attention for two techniques that have been little investigated: believably incorporating directives, and late commitment

Norm-based and commitment-driven agentification of the Internet of Things

There are no doubts that the Internet-of-Things (IoT) has conquered the ICT industry to the extent that many governments and organizations are already rolling out many anywhere,anytime online services that IoT sustains. However, like any emerging and disruptive technology, multiple obstacles are slowing down IoT practical adoption including the passive nature and privacy invasion of things. This paper examines how to empower things with necessary capabilities that would make them proactive and responsive. This means things can, for instance reach out to collaborative peers, (un)form dynamic communities when necessary, avoid malicious peers, and be “questioned” for their actions. To achieve such empowerment, this paper presents an approach for agentifying things using norms along with commitments that operationalize these norms. Both norms and commitments are specialized into social (i.e., application independent) and business (i.e., application dependent), respectively. Being proactive, things could violate commitments at run-time, which needs to be detected through monitoring. In this paper, thing agentification is illustrated with a case study about missing children and demonstrated with a testbed that uses different IoT-related technologies such as Eclipse Mosquitto broker and Message Queuing Telemetry Transport protocol. Some experiments conducted upon this testbed are also discussed

Detection and resolution of normative conflicts in multi-agent systems : a literature survey

Peer reviewedPostprin

Justice, Charity, and Disaster Relief: What, if Anything, Is Owed to Haiti, Japan and New Zealand?

Whenever fellow humans suffer due to natural catastrophes, we have a duty to help them. This duty is not only acknowledged in moral theory, but also expressed in ordinary people’s reactions to phenomena such as tsunamis, hurricanes, and earthquakes. Despite being widely acknowledged, this duty is also widely disputed: some believe it is a matter of justice, others a matter of charity. Although central to debates in international political theory, the distinction between justice and charity is hardly ever systematically drawn. To fill this gap in the literature, I consider three accounts of this distinction, the ‘Agent-based’, the ‘Recipient-based’, and the ‘Mixed’ View, and argue that they are all unsatisfactory. I then offer a fourth alternative, the ‘Autonomy’ View, which successfully overcomes the difficulties affecting its rivals. I conclude by considering the implications of this view for the moral grounds of disaster relief in earthquake-stricken Haiti, New Zealand, and Japan

The Virtual Storyteller: story generation by simulation

The Virtual Storyteller is a multi-agent framework that generates stories based on a concept called emergent narrative. In this paper, we describe the motivation and approach of the Virtual Storyteller, and give an overview of the computational processes involved in the story generation process. We also discuss some of the challenges posed by our chosen approach

Prediction of intent in robotics and multi-agent systems.

Moving beyond the stimulus contained in observable agent behaviour, i.e. understanding the underlying intent of the observed agent is of immense interest in a variety of domains that involve collaborative and competitive scenarios, for example assistive robotics, computer games, robot-human interaction, decision support and intelligent tutoring. This review paper examines approaches for performing action recognition and prediction of intent from a multi-disciplinary perspective, in both single robot and multi-agent scenarios, and analyses the underlying challenges, focusing mainly on generative approaches