913,734 research outputs found
Geospatial Narratives and their Spatio-Temporal Dynamics: Commonsense Reasoning for High-level Analyses in Geographic Information Systems
The modelling, analysis, and visualisation of dynamic geospatial phenomena
has been identified as a key developmental challenge for next-generation
Geographic Information Systems (GIS). In this context, the envisaged
paradigmatic extensions to contemporary foundational GIS technology raises
fundamental questions concerning the ontological, formal representational, and
(analytical) computational methods that would underlie their spatial
information theoretic underpinnings.
We present the conceptual overview and architecture for the development of
high-level semantic and qualitative analytical capabilities for dynamic
geospatial domains. Building on formal methods in the areas of commonsense
reasoning, qualitative reasoning, spatial and temporal representation and
reasoning, reasoning about actions and change, and computational models of
narrative, we identify concrete theoretical and practical challenges that
accrue in the context of formal reasoning about `space, events, actions, and
change'. With this as a basis, and within the backdrop of an illustrated
scenario involving the spatio-temporal dynamics of urban narratives, we address
specific problems and solutions techniques chiefly involving `qualitative
abstraction', `data integration and spatial consistency', and `practical
geospatial abduction'. From a broad topical viewpoint, we propose that
next-generation dynamic GIS technology demands a transdisciplinary scientific
perspective that brings together Geography, Artificial Intelligence, and
Cognitive Science.
Keywords: artificial intelligence; cognitive systems; human-computer
interaction; geographic information systems; spatio-temporal dynamics;
computational models of narrative; geospatial analysis; geospatial modelling;
ontology; qualitative spatial modelling and reasoning; spatial assistance
systemsComment: ISPRS International Journal of Geo-Information (ISSN 2220-9964);
Special Issue on: Geospatial Monitoring and Modelling of Environmental
Change}. IJGI. Editor: Duccio Rocchini. (pre-print of article in press
Deconstructing climate misinformation to identify reasoning errors
Misinformation can have significant societal consequences. For example, misinformation about climate change has confused the public and stalled support for mitigation policies. When people lack the expertise and skill to evaluate the science behind a claim, they typically rely on heuristics such as substituting judgment about something complex (i.e. climate science) with judgment about something simple (i.e. the character of people who speak about climate science) and are therefore vulnerable to misleading information. Inoculation theory offers one approach to effectively neutralize the influence of misinformation. Typically, inoculations convey resistance by providing people with information that counters misinformation. In contrast, we propose inoculating against misinformation by explaining the fallacious reasoning within misleading denialist claims. We offer a strategy based on critical thinking methods to analyse and detect poor reasoning within denialist claims. This strategy includes detailing argument structure, determining the truth of the premises, and checking for validity, hidden premises, or ambiguous language. Focusing on argument structure also facilitates the identification of reasoning fallacies by locating them in the reasoning process. Because this reason-based form of inoculation is based on general critical thinking methods, it offers the distinct advantage of being accessible to those who lack expertise in climate science. We applied this approach to 42 common denialist claims and find that they all demonstrate fallacious reasoning and fail to refute the scientific consensus regarding anthropogenic global warming. This comprehensive deconstruction and refutation of the most common denialist claims about climate change is designed to act as a resource for communicators and educators who teach climate science and/or critical thinking
Handling Defeasibilities in Action Domains
Representing defeasibility is an important issue in common sense reasoning.
In reasoning about action and change, this issue becomes more difficult because
domain and action related defeasible information may conflict with general
inertia rules. Furthermore, different types of defeasible information may also
interfere with each other during the reasoning. In this paper, we develop a
prioritized logic programming approach to handle defeasibilities in reasoning
about action. In particular, we propose three action languages {\cal AT}^{0},
{\cal AT}^{1} and {\cal AT}^{2} which handle three types of defeasibilities in
action domains named defeasible constraints, defeasible observations and
actions with defeasible and abnormal effects respectively. Each language with a
higher superscript can be viewed as an extension of the language with a lower
superscript. These action languages inherit the simple syntax of {\cal A}
language but their semantics is developed in terms of transition systems where
transition functions are defined based on prioritized logic programs. By
illustrating various examples, we show that our approach eventually provides a
powerful mechanism to handle various defeasibilities in temporal prediction and
postdiction. We also investigate semantic properties of these three action
languages and characterize classes of action domains that present more
desirable solutions in reasoning about action within the underlying action
languages.Comment: 49 pages, 1 figure, to be appeared in journal Theory and Practice
Logic Programmin
The 'Fake News' Effect: Experimentally Identifying Motivated Reasoning Using Trust in News
Motivated reasoning posits that people distort how they process information
in the direction of beliefs they find attractive. This paper creates a novel experimental design to identify motivated reasoning from Bayesian updating when
people have preconceived beliefs. It analyzes how subjects assess the veracity
of information sources that tell them the median of their belief distribution is
too high or too low. Bayesians infer nothing about the source veracity, but
motivated beliefs are evoked. Evidence supports politically-motivated reasoning about immigration, income mobility, crime, racial discrimination, gender,
climate change, and gun laws. Motivated reasoning helps explain belief biases,
polarization, and overconfidence
Trust-based belief change
International audienceWe propose a modal logic that supports reasoning about trust-based belief change. The term trust-based belief change refers to belief change that depends on the degree of trust the receiver has in the source of information
Events, time and argumentative systems
The accomplishment of systems with abilities to reason about actions and change and systems that can manage incomplete or not very reliable information with abilities to discuss or argument has been of great importance for artificial intelligence community. These two ways of reasoning were attacked independently, but they are complementary, since a lot of applications need of both, since all dynamic systems (dynamic on information) counts with uncomplete information and information that depends on events and time.
The line of investigation suggested on this present work tries to achieve a system that can reason about action and at the same time can elaborate a discussion, i. e. intends to conciliate argumentative systems with reasoning about actions and change or temporal reasoning.Eje: Agentes y Sistemas InteligentesRed de Universidades con Carreras en Informática (RedUNCI
Information-Sharing and Privacy in Social Networks
We present a new model for reasoning about the way information is shared
among friends in a social network, and the resulting ways in which it spreads.
Our model formalizes the intuition that revealing personal information in
social settings involves a trade-off between the benefits of sharing
information with friends, and the risks that additional gossiping will
propagate it to people with whom one is not on friendly terms. We study the
behavior of rational agents in such a situation, and we characterize the
existence and computability of stable information-sharing networks, in which
agents do not have an incentive to change the partners with whom they share
information. We analyze the implications of these stable networks for social
welfare, and the resulting fragmentation of the social network
Action Theory Revision in Dynamic Logic
!2th Workshop on Nonmonotonic Reasoning (NMR'08)Like any other logical theory, action theories in reasoning about actions may evolve, and thus need revision methods to adequately accommodate new information about the behavior of actions. Here we give a semantics that complies with minimal change for revising action theories stated in a version of PDL. We give algorithms that are proven correct w.r.t. the semantics for those theories that are modular
- …