A Logic of Negative Trust

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,
https://doi.org/10.1080/11663081.2020.1789404

A Fully Rational Account of Structured Argumentation Under Resource Bounds

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)

IJCAI, the International Joint Conference on Artificial Intelligence, is the premier conference for the international AI community to communicate the advances and achievements of AI research. 
IJCAI-PRICAI20 received  4717 valid submissions, of which only 592 have been accepted, for a record-low overall acceptance rate of 12.6%.

Depth-Bounded Approximations of Probability

Logica Milano

We introduce measures of uncertainty that are based on Depth-Bounded Logics and resemble belief functions. We show that our measures can be seen as approximation of classical probability measures over classical logic, and that a variant of the PSAT problem for them is solvable in polynomial time.

Baldi P., D’Agostino M., Hosni H. (2020) “Depth-Bounded Approximations of Probability”. In: Lesot MJ. et al. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2020. Communications in Computer and Information Science, vol 1239. Springer, Cham DOI: 10.1007/978-3-030-50153-2_45

Rasmus Rendsvig – Logics for Social Networks

11 June 2020 – 5pm on Teams

In this talk, I will discuss logics for social networks, their epistemic extensions, and dynamics in such structure, including diffusion as modeled by threshold models. I will present a selection of recent models for social networks and their epistemics, with a focus on how these may be represented using dynamic term-modal logic (DTML)—a dynamic, quantified
modal/epistemic logic, where the subscripts of operators are first-order terms, allowing formulas such as $\exist x K_x N(x,b)$: there exists and agent that knows that it is networked with agent b. DTML is based on an enriched version of action models of dynamic epistemic logic fame, and comes with a complete set of reduction axioms. Modelling social network dynamics in DTML thus directly provide sound and complete logics. Additionally, such logics are decidable when only a finite set of agents is considered.

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Rasmus K. Rendsvig is Postdoctoral researcher at the Center for Information and Bubble Studies,University of Copenhagen

Depth-bounded Belief Functions

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 FunctionsInternatonal Journal of Approximate Reasoning, Volume 123, August 2020, Pages 26-40. doi.org/10.1016/j.ijar.2020.05.001 (Open Access)