15,710 research outputs found
ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning
We present ATOMIC, an atlas of everyday commonsense reasoning, organized
through 877k textual descriptions of inferential knowledge. Compared to
existing resources that center around taxonomic knowledge, ATOMIC focuses on
inferential knowledge organized as typed if-then relations with variables
(e.g., "if X pays Y a compliment, then Y will likely return the compliment").
We propose nine if-then relation types to distinguish causes vs. effects,
agents vs. themes, voluntary vs. involuntary events, and actions vs. mental
states. By generatively training on the rich inferential knowledge described in
ATOMIC, we show that neural models can acquire simple commonsense capabilities
and reason about previously unseen events. Experimental results demonstrate
that multitask models that incorporate the hierarchical structure of if-then
relation types lead to more accurate inference compared to models trained in
isolation, as measured by both automatic and human evaluation.Comment: AAAI 2019 C
Belief Revision in Structured Probabilistic Argumentation
In real-world applications, knowledge bases consisting of all the information
at hand for a specific domain, along with the current state of affairs, are
bound to contain contradictory data coming from different sources, as well as
data with varying degrees of uncertainty attached. Likewise, an important
aspect of the effort associated with maintaining knowledge bases is deciding
what information is no longer useful; pieces of information (such as
intelligence reports) may be outdated, may come from sources that have recently
been discovered to be of low quality, or abundant evidence may be available
that contradicts them. In this paper, we propose a probabilistic structured
argumentation framework that arises from the extension of Presumptive
Defeasible Logic Programming (PreDeLP) with probabilistic models, and argue
that this formalism is capable of addressing the basic issues of handling
contradictory and uncertain data. Then, to address the last issue, we focus on
the study of non-prioritized belief revision operations over probabilistic
PreDeLP programs. We propose a set of rationality postulates -- based on
well-known ones developed for classical knowledge bases -- that characterize
how such operations should behave, and study a class of operators along with
theoretical relationships with the proposed postulates, including a
representation theorem stating the equivalence between this class and the class
of operators characterized by the postulates
Annotated revision programs
Revision programming is a formalism to describe and enforce updates of belief
sets and databases. That formalism was extended by Fitting who assigned
annotations to revision atoms. Annotations provide a way to quantify the
confidence (probability) that a revision atom holds. The main goal of our paper
is to reexamine the work of Fitting, argue that his semantics does not always
provide results consistent with intuition, and to propose an alternative
treatment of annotated revision programs. Our approach differs from that
proposed by Fitting in two key aspects: we change the notion of a model of a
program and we change the notion of a justified revision. We show that under
this new approach fundamental properties of justified revisions of standard
revision programs extend to the annotated case.Comment: 30 pages, to appear in Artificial Intelligence Journa
Featherweight VeriFast
VeriFast is a leading research prototype tool for the sound modular
verification of safety and correctness properties of single-threaded and
multithreaded C and Java programs. It has been used as a vehicle for
exploration and validation of novel program verification techniques and for
industrial case studies; it has served well at a number of program verification
competitions; and it has been used for teaching by multiple teachers
independent of the authors. However, until now, while VeriFast's operation has
been described informally in a number of publications, and specific
verification techniques have been formalized, a clear and precise exposition of
how VeriFast works has not yet appeared. In this article we present for the
first time a formal definition and soundness proof of a core subset of the
VeriFast program verification approach. The exposition aims to be both
accessible and rigorous: the text is based on lecture notes for a graduate
course on program verification, and it is backed by an executable
machine-readable definition and machine-checked soundness proof in Coq
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