40 research outputs found
Epistemology Normalized
We offer a general framework for theorizing about the structure of knowledge and belief in terms of the comparative normality of situations compatible with one's evidence. The guiding idea is that, if a possibility is sufficiently less normal than one's actual situation, then one can know that that possibility does not obtain. This explains how people can have inductive knowledge that goes beyond what is strictly entailed by their evidence. We motivate the framework by showing how it illuminates knowledge about the future, knowledge of lawful regularities, knowledge about parameters measured using imperfect instruments, the connection between knowledge, belief, and probability, and the dynamics of knowledge and belief in response to new evidence
Reifying default reasons in justification logic
The main goal of this paper is to argue that justification logic advances the formal study of default reasons. After introducing a variant of justification logic with default reasons, we first show how the logic can be used to model undercutting attacks and exclusionary reasons. Then we compare this logic to Reiterâs default logic interpreted as an argumentation framework. The comparison is done by analyzing differences in the way in which process trees are built for the two logics
Bisimulation and expressivity for conditional belief, degrees of belief, and safe belief
Plausibility models are Kripke models that agents use to reason about
knowledge and belief, both of themselves and of each other. Such models are
used to interpret the notions of conditional belief, degrees of belief, and
safe belief. The logic of conditional belief contains that modality and also
the knowledge modality, and similarly for the logic of degrees of belief and
the logic of safe belief. With respect to these logics, plausibility models may
contain too much information. A proper notion of bisimulation is required that
characterises them. We define that notion of bisimulation and prove the
required characterisations: on the class of image-finite and preimage-finite
models (with respect to the plausibility relation), two pointed Kripke models
are modally equivalent in either of the three logics, if and only if they are
bisimilar. As a result, the information content of such a model can be
similarly expressed in the logic of conditional belief, or the logic of degrees
of belief, or that of safe belief. This, we found a surprising result. Still,
that does not mean that the logics are equally expressive: the logics of
conditional and degrees of belief are incomparable, the logics of degrees of
belief and safe belief are incomparable, while the logic of safe belief is more
expressive than the logic of conditional belief. In view of the result on
bisimulation characterisation, this is an equally surprising result. We hope
our insights may contribute to the growing community of formal epistemology and
on the relation between qualitative and quantitative modelling
Knowledge from Probability
We give a probabilistic analysis of inductive knowledge and belief and
explore its predictions concerning knowledge about the future, about laws of
nature, and about the values of inexactly measured quantities. The analysis
combines a theory of knowledge and belief formulated in terms of relations of
comparative normality with a probabilistic reduction of those relations. It
predicts that only highly probable propositions are believed, and that many
widely held principles of belief-revision fail.Comment: In Proceedings TARK 2021, arXiv:2106.1088
Role Assignment Adaptation: An Intentional Forgetting Approach
In organizations the distribution of tasks is a rising challenge in complex and dynamic environments. By structuring responsibilities and expectations for task processing in roles, organizations provide a transparent approach for collaboration. However, if tasks are being generated unexpectedly, actors who enact multiple roles might be overloaded in dynamic environments. By focusing on relevant information in terms of an intentional forgetting mechanism, actors could overcome these overload situations. Therefore, we provide an agent-based simulation to model and analyze effects of intentional forgetting by adapting role assignments in dynamic environments. The agent architecture utilizes separated revision functions to control an agentâs perception and belief acquisition to focus on relevant information. The model is tested using a case-study in a simulated emergency response scenario. The simulation results show that adapting role assignments at runtime improves team performance significantly