Million dollar questions: why deliberation is more than information pooling

Models of collective deliberation often assume that the chief aim of a deliberative exchange is the sharing of information. In this paper, we argue that an equally important role of deliberation is to draw participants’ attention to pertinent questions, which can aid the assembly and processing of distributed information by drawing deliberators’ attention to new issues. The assumption of logical omniscience renders classical models of agents' informational states unsuitable for modelling this role of deliberation. Building on recent insights from psychology, linguistics and philosophy about the role of questions in speech and thought, we propose a different model in which beliefs are treated as answers directed at specific questions. Here, questions are formally represented as partitions of the space of possibilities and individuals’ information states as sets of and corresponding partial answers to them. The state of conversation is then characterised by individuals’ information together with the questions under discussion, which can be steered by various deliberative inputs. Using this model, deliberation is then shown to shape collective decisions in ways that classical models cannot capture, allowing for novel explanations of how group consensus is achieved

What ‘must’ adds

There is a difference between the conditions in which one can felicitously use a ‘must’-claim like and those in which one can use the corresponding claim without the ‘must’, as in 'It must be raining out' versus 'It is raining out. It is difficult to pin down just what this difference amounts to. And it is difficult to account for this difference, since assertions of 'Must p' and assertions of p alone seem to have the same basic goal: namely, communicating that p is true. In this paper I give a new account of the conversational role of ‘must’. I begin by arguing that a ‘must’-claim is felicitous only if there is a shared argument for the proposition it embeds. I then argue that this generalization, which I call Support, can explain the more familiar generalization that ‘must’-claims are felicitous only if the speaker’s evidence for them is in some sense indirect. Finally, I propose a pragmatic derivation of Support as a manner implicature

Planning while Believing to Know

Over the last few years, the concept of Artificial Intelligence (AI) has become essential in our daily life and in several working scenarios. Among the various branches of AI, automated planning and the study of multi-agent systems are central research fields. This thesis focuses on a combination of these two areas: that is, a specialized kind of planning known as Multi-agent Epistemic Planning. This field of research is concentrated on all those scenarios where agents, reasoning in the space of knowledge/beliefs, try to find a plan to reach a desirable state from a starting one. This requires agents able to reason about her/his and others’ knowledge/beliefs and, therefore, capable of performing epistemic reasoning. Being aware of the information flows and the others’ states of mind is, in fact, a key aspect in several planning situations. That is why developing autonomous agents, that can reason considering the perspectives of their peers, is paramount to model a variety of real-world domains. The objective of our work is to formalize an environment where a complete characterization of the agents’ knowledge/beliefs interactions and updates are possible. In particular, we achieved such a goal by defining a new action-based language for Multi-agent Epistemic Planning and implementing epistemic planners based on it. These solvers, flexible enough to reason about various domains and different nuances of knowledge/belief update, can provide a solid base for further research on epistemic reasoning or real-base applications. This dissertation also proposes the design of a more general epistemic planning architecture. This architecture, following famous cognitive theories, tries to emulate some characteristics of the human decision-making process. In particular, we envisioned a system composed of several solving processes, each one with its own trade-off between efficiency and correctness, which are arbitrated by a meta-cognitive module

How to do things with modals

Mind &Language, Volume 35, Issue 1, Page 115-138, February 2020