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

Flexible conversation management using a BDI agent approach

We describe a BDI (Belief, Desire, Intention) goal-oriented architecture for a conversational virtual companion embodied as a child's Toy, designed to be both entertaining and capable of carrying out col- laborative tasks. We argue that the goal-oriented approach supports both structured conversational activities (e.g., story-telling, collaborative games) as well as more \free- owing" engaging dialogue with variation and some unpredictability. BDI plans encode the knowledge required for the structured engagements, with the use of multiple plans for conversa- tional goals providing variation in the interactions

Agent programming in the cognitive era

It is claimed that, in the nascent ‘Cognitive Era’, intelligent systems will be trained using machine learning techniques rather than programmed by software developers. A contrary point of view argues that machine learning has limitations, and, taken in isolation, cannot form the basis of autonomous systems capable of intelligent behaviour in complex environments. In this paper, we explore the contributions that agent-oriented programming can make to the development of future intelligent systems. We briefly review the state of the art in agent programming, focussing particularly on BDI-based agent programming languages, and discuss previous work on integrating AI techniques (including machine learning) in agent-oriented programming. We argue that the unique strengths of BDI agent languages provide an ideal framework for integrating the wide range of AI capabilities necessary for progress towards the next-generation of intelligent systems. We identify a range of possible approaches to integrating AI into a BDI agent architecture. Some of these approaches, e.g., ‘AI as a service’, exploit immediate synergies between rapidly maturing AI techniques and agent programming, while others, e.g., ‘AI embedded into agents’ raise more fundamental research questions, and we sketch a programme of research directed towards identifying the most appropriate ways of integrating AI capabilities into agent programs

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