244,747 research outputs found
Modeling Belief in Dynamic Systems, Part II: Revision and Update
The study of belief change has been an active area in philosophy and AI. In
recent years two special cases of belief change, belief revision and belief
update, have been studied in detail. In a companion paper (Friedman & Halpern,
1997), we introduce a new framework to model belief change. This framework
combines temporal and epistemic modalities with a notion of plausibility,
allowing us to examine the change of beliefs over time. In this paper, we show
how belief revision and belief update can be captured in our framework. This
allows us to compare the assumptions made by each method, and to better
understand the principles underlying them. In particular, it shows that Katsuno
and Mendelzon's notion of belief update (Katsuno & Mendelzon, 1991a) depends on
several strong assumptions that may limit its applicability in artificial
intelligence. Finally, our analysis allow us to identify a notion of minimal
change that underlies a broad range of belief change operations including
revision and update.Comment: See http://www.jair.org/ for other files accompanying this articl
Fully statistical neural belief tracking
This paper proposes an improvement to the existing data-driven Neural Belief Tracking (NBT) framework for Dialogue State Tracking (DST). The existing NBT model uses a hand-crafted belief state update mechanism which involves an expensive manual retuning step whenever the model is deployed to a new dialogue domain. We show that this update mechanism can be learned jointly with the semantic decoding and context modelling parts of the NBT model, eliminating the last rule-based module from this DST framework. We propose two different statistical update mechanisms and show that dialogue dynamics can be modelled with a very small number of additional model parameters. In our DST evaluation over three languages, we show that this model achieves competitive performance and provides a robust framework for building resource-light DST models
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Tell Me Everything You Know: A Conversation Update System for the Rational Speech Acts Framework
The Rational Speech Acts (RSA) framework has been applied to an increasing number of linguistic phenomena. Despite its promise as a model of conversational reasoning, it has rarely been used to model more than a single conversation turn. I propose a system for conversation update in the RSA framework that allows iterative simulations of production and comprehension. I explore three key issues: how to simulate the Common Ground; how to update the Common Ground and participant belief states; and how to select observations
Updating probabilistic epistemic states in persuasion dialogues
In persuasion dialogues, the ability of the persuader to model the persuadee allows the persuader to make better choices of move. The epistemic approach to probabilistic argumentation is a promising way of modelling the persuadee’s belief in arguments, and proposals have been made for update methods that specify how these beliefs can be updated at each step of the dialogue. However, there is a need to better understand these proposals, and moreover, to gain insights into the space of possible update functions. So in this paper, we present a general framework for update functions in which we consider existing and novel update functions
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