Learning Action Models: Qualitative Approach

In dynamic epistemic logic, actions are described using action models. In this paper we introduce a framework for studying learnability of action models from observations. We present first results concerning propositional action models. First we check two basic learnability criteria: finite identifiability (conclusively inferring the appropriate action model in finite time) and identifiability in the limit (inconclusive convergence to the right action model). We show that deterministic actions are finitely identifiable, while non-deterministic actions require more learning power-they are identifiable in the limit. We then move on to a particular learning method, which proceeds via restriction of a space of events within a learning-specific action model. This way of learning closely resembles the well-known update method from dynamic epistemic logic. We introduce several different learning methods suited for finite identifiability of particular types of deterministic actions.Comment: 18 pages, accepted for LORI-V: The Fifth International Conference on Logic, Rationality and Interaction, October 28-31, 2015, National Taiwan University, Taipei, Taiwa

Cognitive Bias and Belief Revision

In this paper we formalise three types of cognitive bias within the framework of belief revision: confirmation bias, framing bias, and anchoring bias. We interpret them generally, as restrictions on the process of iterated revision, and we apply them to three well-known belief revision methods: conditioning, lexicographic revision, and minimal revision. We investigate the reliability of biased belief revision methods in truth tracking. We also run computer simulations to assess the performance of biased belief revision in random scenarios.Comment: In Proceedings TARK 2023, arXiv:2307.0400

A note on a generalization of the muddy children puzzle

ABSTRACT We study a generalization of the Muddy Children puzzle by allowing public announcements with arbitrary generalized quantifiers. We propose a new concise logical modeling of the puzzle based on the number triangle representation of quantifiers. Our general aim is to discuss the possibility of epistemic modeling that is cut for specific informational dynamics. Moreover, we show that the puzzle is solvable for any number of agents if and only if the quantifier in the announcement is positively active (satisfies a form of variety)

The Problem of Learning the Semantics of Quantifiers

This paper is concerned with a possible mechanism for learning the meanings of quantifiers in natural language. The meaning of a natural language construction is identified with a procedure for recognizing its extension. Therefore, acquisition of natural language quantifiers is supposed to consist in collecting procedures for computing their denotations. A method for encoding classes of finite models corresponding to given quantifiers is shown. The class of finite models is represented by appropriate languages. Some facts describing dependencies between classes of quantifiers and classes of devices are presented. In the second part of the paper examples of syntax-learning models are shown. According to these models new results in quantifier learning are presented. Finally, the question of the adequacy of syntax-learning tools for describing the process of semantic learning is stated.

Interactive semantic alignment model : social influence and local transmission bottleneck

Bibliogr. s. 252-253We provide a computational model of semantic alignment among communicating agents constrained by social and cognitive pressures. We use our model to analyze the effects of social stratification and a local transmission bottleneck on the coordination of meaning in isolated dyads. The analysis suggests that the traditional approach to learning - understood as inferring prescribed meaning from observations - can be viewed as a special case of semantic alignment, manifesting itself in the behaviour of socially imbalanced dyads put under mild pressure of a local transmission bottleneck. Other parametrizations of the model yield different long-term effects, including lack of convergence or convergence on simple meanings only

Inductive Inference and Epistemic Modal Logic (Invited Talk)

This paper is concerned with a link between inductive inference and dynamic epistemic logic. The bridge was first introduced in [Gierasimczuk, 2009; Nina Gierasimczuk, 2009; Gierasimczuk, 2010]. We present a synthetic view on subsequent contributions: inductive truth-tracking properties of belief revision policies seen as belief upgrade methods; topological interpretation and characterisation of inductive inference; discussion of the adequacy of the topological semantics of modal logic for characterising inductive inference. We briefly present the topological Dynamic Logic for Learning Theory. Finally, we discuss several surprising results obtained in computational inductive inference that challenge the usual understanding of certainty, and of rational inquiry as consistent and conservative learning