We present a logic to model the behaviour of an agent trusting or not trusting messages sent by another agent. The logic formalizes trust as a consistency checking function with respect to currently available information. Negative trust is modeled in two forms: distrust as the rejection of incoming inconsistent information; mistrust, as revision of previously held information becoming undesirable in view of new incoming inconsistent information, which the agent wishes to accept. We provide a natural deduction calculus, a relational semantics and prove soundness and completeness results. We overview a number of applications which have been investigated for the proof-theoretical formulation of the logic.
G.Primiero, A Logic of Negative Trust, Journal of Applied Non-Classical Logic,
ASPIC+ is an established general framework for argumentation and non-monotonic reasoning. However ASPIC+ does not satisfy the non-contamination rationality postulates, and moreover, tacitly assumes unbounded resources when demonstrating satisfaction of the consistency postulates. In this paper we present a new version of ASPIC+ – Dialectical ASPIC+ – that is fully rational under resource bounds.
M. D’Agostino and S. Modgil. A Fully Rational Account of Structured Argumentation Under Resource Bounds. Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI-20)
This paper introduces and investigates Depth-bounded Belief functions, a logic-based representation of quantified uncertainty. Depth-bounded Belief functions are based on the framework of Depth-bounded Boolean logics, which provide a hierarchy of approximations to classical logic. Similarly, Depth-bounded Belief functions give rise to a hierarchy of increasingly tighter lower and upper bounds over classical measures of uncertainty. This has the rather welcome consequence that “higher logical abilities” lead to sharper uncertainty quantification. In particular, our main results identify the conditions under which Dempster-Shafer Belief functions and probability functions can be represented as a limit of a suitable sequence of Depth-bounded Belief functions.
KEYWORDS: Belief functions; Uncertain reasoning; Depth-bounded logics; Probability.
P. Baldi and H. Hosni. (2020). “Depth-bounded Belief Functions” Internatonal Journal of Approximate Reasoning, Volume 123, August 2020, Pages 26-40. doi.org/10.1016/j.ijar.2020.05.001 (Open Access)
Paolo Baldi has published a paper on fuzzy quantifiers in the journal Fuzzy Sets and System, https://doi.org/10.1016/j.fss.2019.12.009
A new paper by Hykel Hosni and co-author Enrico Marchioni titled “Possibilistic randomisation in strategic-form games” has been published. Details here.