1,503 research outputs found
Towards an efficient prover for the C1 paraconsistent logic
The KE inference system is a tableau method developed by Marco Mondadori
which was presented as an improvement, in the computational efficiency sense,
over Analytic Tableaux. In the literature, there is no description of a theorem
prover based on the KE method for the C1 paraconsistent logic. Paraconsistent
logics have several applications, such as in robot control and medicine. These
applications could benefit from the existence of such a prover. We present a
sound and complete KE system for C1, an informal specification of a strategy
for the C1 prover as well as problem families that can be used to evaluate
provers for C1. The C1 KE system and the strategy described in this paper will
be used to implement a KE based prover for C1, which will be useful for those
who study and apply paraconsistent logics.Comment: 16 page
Believe It or Not: Adding Belief Annotations to Databases
We propose a database model that allows users to annotate data with belief
statements. Our motivation comes from scientific database applications where a
community of users is working together to assemble, revise, and curate a shared
data repository. As the community accumulates knowledge and the database
content evolves over time, it may contain conflicting information and members
can disagree on the information it should store. For example, Alice may believe
that a tuple should be in the database, whereas Bob disagrees. He may also
insert the reason why he thinks Alice believes the tuple should be in the
database, and explain what he thinks the correct tuple should be instead.
We propose a formal model for Belief Databases that interprets users'
annotations as belief statements. These annotations can refer both to the base
data and to other annotations. We give a formal semantics based on a fragment
of multi-agent epistemic logic and define a query language over belief
databases. We then prove a key technical result, stating that every belief
database can be encoded as a canonical Kripke structure. We use this structure
to describe a relational representation of belief databases, and give an
algorithm for translating queries over the belief database into standard
relational queries. Finally, we report early experimental results with our
prototype implementation on synthetic data.Comment: 17 pages, 10 figure
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