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

A Roadmap to Pervasive Systems Verification

yesThe complexity of pervasive systems arises from the many different aspects that such systems possess. A typical pervasive system may be autonomous, distributed, concurrent and context-based, and may involve humans and robotic devices working together. If we wish to formally verify the behaviour of such systems, the formal methods for pervasive systems will surely also be complex. In this paper, we move towards being able to formally verify pervasive systems and outline our approach wherein we distinguish four distinct dimensions within pervasive system behaviour and utilise different, but appropriate, formal techniques for verifying each one.EPSR

Extending External Agent Capabilities in Healthcare Social Networks

A social health care system, such as palliative care, can be viewed as a social network of interacting patients and care providers. Each patient in the network has a set of capabilities to perform his or her intended daily tasks. However, some patients may not have the required capabilities to carry out their desired tasks. Consequently, different groups of care providers - consist of doctors, volunteers, nurses, etc.- offer the patients support by providing them with a variety of needed services. Assuming there are a cost and resource limitations for providing care within the system, where each care provider can support a limited number of patients, the problem is to find a set of suitable care providers to match the needs of the maximum number of patients. In this dissertation, we propose a novel agent-based model to address this problem by extending the agent\u27s capabilities using the benefit of the social network. Our assumption is that each agent, or patient, can cover its disabilities and perform its desired tasks through collaboration with other agents, or care providers, in the network. The goal of this work is to improve the quality of services in the network at both individual and system levels. On the one hand, an individual patient wants to maximize the quality of his/her life, while at the system level we want to achieve quality care for as many patients as possible with minimum cost. The performance and functionality of this proposed model have been evaluated based on various synthetic networks. The results demonstrate a significant reduction in the operational costs and enhancement of the service quality

Governance of Autonomous Agents on the Web: Challenges and Opportunities

International audienceThe study of autonomous agents has a long tradition in the Multiagent System and the Semantic Web communities, with applications ranging from automating business processes to personal assistants. More recently, the Web of Things (WoT), which is an extension of the Internet of Things (IoT) with metadata expressed in Web standards, and its community provide further motivation for pushing the autonomous agents research agenda forward. Although representing and reasoning about norms, policies and preferences is crucial to ensuring that autonomous agents act in a manner that satisfies stakeholder requirements, normative concepts, policies and preferences have yet to be considered as first-class abstractions in Web-based multiagent systems. Towards this end, this paper motivates the need for alignment and joint research across the Multiagent Systems, Semantic Web, and WoT communities, introduces a conceptual framework for governance of autonomous agents on the Web, and identifies several research challenges and opportunities

Towards a reusable architecture for message exchange in pervasive healthcare

The main objective of this paper is to present a reusable architecture for message exchange in pervasive healthcare environments meant to be generally applicable to different applications in the healthcare domain. This architecture has been designed by integrating different concepts and technologies of ubiquitous computing, software agents, and openEHR archetypes, in order to provide interoperability between healthcare systems. The architecture was demonstrated and evaluated in controlled experiments that we conducted at three cardiology clinics, an analysis laboratory, and the cardiology sector of a hospital located in Marília (São Paulo, Brazil). Three applications were developed to evaluate this architecture, and the results showed that the architecture is suitable to facilitate the development of healthcare systems by offering generic and powerful message exchange capabilities. The reusable architecture speeds up the development of new applications, reducing the number of mistakes and the development time. The proposed architecture facilitates message exchanging between caregivers, contributing in this way to the development of pervasive healthcare systems that allow healthcare to be available anywhere, anytime, and to anyone

Abductive Design of BDI Agent-based Digital Twins of Organizations

For a Digital Twin - a precise, virtual representation of a physical counterpart - of a human-like system to be faithful and complete, it must appeal to a notion of anthropomorphism (i.e., attributing human behaviour to non-human entities) to imitate (1) the externally visible behaviour and (2) the internal workings of that system. Although the Belief-Desire-Intention (BDI) paradigm was not developed for this purpose, it has been used successfully in human modeling applications. In this sense, we introduce in this thesis the notion of abductive design of BDI agent-based Digital Twins of organizations, which builds on two powerful reasoning disciplines: reverse engineering (to recreate the visible behaviour of the target system) and goal-driven eXplainable Artificial Intelligence (XAI) (for viewing the behaviour of the target system through the lens of BDI agents). Precisely speaking, the overall problem we are trying to address in this thesis is to “Find a BDI agent program that best explains (in the sense of formal abduction) the behaviour of a target system based on its past experiences . To do so, we propose three goal-driven XAI techniques: (1) abductive design of BDI agents, (2) leveraging imperfect explanations and (3) mining belief-based explanations. The resulting approach suggests that using goal-driven XAI to generate Digital Twins of organizations in the form of BDI agents can be effective, even in a setting with limited information about the target system’s behaviour