Traditionally, an agent's beliefs would come from what the agent can see, hear, or sense. In the modern world, beliefs are often based on the data available to the agents. In this work, we investigate a dynamic logic of such beliefs that incorporates public announcements of data. The main technical contribution is a sound and complete axiomatisation of the interplay between data-informed beliefs and data announcement modalities. We also describe a non-trivial polynomial model checking algorithm for this logical system.

We formalise the notion of an anonymous public announcement in the tradition of public announcement logic. Such announcements can be seen as in-between a public announcement from ``the outside" (an announcement of $ϕ$) and a public announcement by one of the agents (an announcement of $K_aϕ$): we get more information than just $ϕ$, but not (necessarily) about exactly who made it. Even if such an announcement is prima facie anonymous, depending on the background knowledge of the agents it might reveal the identity of the announcer: if I post something on a message board, the information might reveal who I am even if I don't sign my name. Furthermore, like in the Russian Cards puzzle, if we assume that the announcer's intention was to stay anonymous, that in fact might reveal more information. In this paper we first look at the case when no assumption about intentions are made, in which case the logic with an anonymous public announcement operator is reducible to epistemic logic. We then look at the case when we assume common knowledge of the intention to stay anonymous, which is both more complex and more interesting: in several ways it boils down to the notion of a ``safe" announcement (again, similarly to Russian Cards). Main results include formal expressivity results and axiomatic completeness for key logical languages.

It's a moment that's been depicted countless times in science fiction -- but what would actually happen when extraterrestrials make contact via a signal picked up on Earth? The moment could come as early as the end of this decade: if aliens receive signals sent by NASA's Deep Space Network (DSN) to the Pioneer 10 satellite in the 70s, for example. When the moment comes, the signal is most likely to be received by large ground-based telescopes such as FAST in China, the Very Large Array (VLA) in New Mexico and the Parkes Telescope in Australia, says former NASA expert Sylvester Kaczmarek. There is no universally agreed rule on how scientists or governments would respond - or on questions such as whether aliens would have rights. But extraterrestrial-focused organisations including the Search for Extraterrestrial Intelligence (SETI) drew up a framework in 2010.

It's been an eventful weekend at OpenAI's headquarters in San Francisco. In a surprise move Friday, the company's board of directors fired co-founder and CEO Sam Altman, which set off an institutional crisis that has seen senior staff resign in protest with nearly 700 rank-and-file employees threatening to do the same. Now the board is facing calls for its own resignation, even after Microsoft had already swooped in to hire Altman's cohort away for its own AI projects. Here's everything you need to know about the situation to hold your own at Thanksgiving on Thursday. This saga began forever ago by internet standards, or last Thursday in the common parlance.

We propose communication pattern logic. A communication pattern describes how processes or agents inform each other, independently of the information content. The full-information protocol in distributed computing is the special case wherein all agents inform each other. We study this protocol in distributed computing models where communication might fail: an agent is certain about the messages it receives, but it may be uncertain about the messages other agents have received. In a dynamic epistemic logic with distributed knowledge and with modalities for communication patterns, the latter are interpreted by updating Kripke models. We propose an axiomatization of communication pattern logic, and we show that collective bisimilarity (comparing models on their distributed knowledge) is preserved when updating models with communication patterns. We can also interpret communication patterns by updating simplicial complexes, a well-known topological framework for distributed computing. We show that the different semantics correspond, and propose collective bisimulation between simplicial complexes.

Epistemic logics model how agents reason about their beliefs and the beliefs of other agents. Existing logics typically assume the ability of agents to reason perfectly about propositions of unbounded modal depth. We present DBEL, an extension of S5 that models agents that can reason about epistemic formulas only up to a specific modal depth. To support explicit reasoning about agent depths, DBEL includes depth atoms Ead (agent a has depth exactly d) and Pad (agent a has depth at least d). We provide a sound and complete axiomatization of DBEL. We extend DBEL to support public announcements for bounded depth agents and show how the resulting DPAL logic generalizes standard axioms from public announcement logic. We present two alternate extensions and identify two undesirable properties, amnesia and knowledge leakage, that these extensions have but DPAL does not. We provide axiomatizations of these logics as well as complexity results for satisfiability and model checking. Finally, we use these logics to illustrate how agents with bounded modal depth reason in the classical muddy children problem, including upper and lower bounds on the depth knowledge necessary for agents to successfully solve the problem.

Named entity recognition (NER) is an essential task in natural language processing, but the internal mechanism of most NER models is a black box for users. In some high-stake decision-making areas, improving the interpretability of an NER method is crucial but challenging. In this paper, based on the existing Deterministic Talmudic Public announcement logic (TPK) model, we propose a novel binary tree model (called BTPK) and apply it to two widely used Bi-RNNs to obtain BTPK-based interpretable ones. Then, we design a counterfactual verification module to verify the BTPK-based learning method. Experimental results on three public datasets show that the BTPK-based learning outperform two classical Bi-RNNs with self-attention, especially on small, simple data and relatively large, complex data. Moreover, the counterfactual verification demonstrates that the explanations provided by the BTPK-based learning method are reasonable and accurate in NER tasks. Besides, the logical reasoning based on BTPK shows how Bi-RNNs handle NER tasks, with different distance of public announcements on long and complex sequences.

To a multi-agent logic of knowledge or belief we can add public announcements to model publicly observed information change, or action models to model information change that is differently observed by different agents, but also modalities representing quantification over such information change, such as quantifiers over announcements or quantifiers over actions models. Such additions may result in more complex or undecidable logics, and create a very open landscape of relative expressivity. The survey [88] of such logics focused on open problems. Some such open problems have since then been resolved, and yet others have come to the fore. In this updated survey we review what is known about such logics with quantification over information change, including digressions into what are known as relation changing modal(but often not epistemic) logics. Again we focus on open problems.

This paper studies logics of unknown truths and false beliefs under neighborhood semantics. Intuitively, if p is true but you do not know that p, then you have an unknown truth that p; if p is false but you believe thatp, then you have a false belief thatp, or you are wrong aboutp. The notion of unknown truths is important in philosophy and formal epistemology. For instance, it is related to Verificationism, or'verification thesis' [31]. Verificationism says that all truths can be known. However, from the thesis, the unknown truth of p, formalized p Kp, gives us a consequence that all truths are actually known. In other words, the notion gives rise to a well-known counterexample to Verificationism. This is the so-called Fitch's'paradox of knowability' [13]. 1 To take another example: it gives rise to an important type of Moore sentences, which is essential to Moore's paradox, which says that one cannot claim the paradoxical sentence "p but I do not know it" [23, 18]. It is known that such a Moore sentence is unsuccessful and self-refuting (see, e.g.

From marketing to politics, exploitation of incomplete information through selective communication of arguments is ubiquitous. In this work, we focus on development of an argumentation-theoretic model for manipulable multi-agent argumentation, where each agent may transmit deceptive information to others for tactical motives. In particular, we study characterisation of epistemic states, and their roles in deception/honesty detection and (mis)trust-building. To this end, we propose the use of intra-agent preferences to handle deception/honesty detection and inter-agent preferences to determine which agent(s) to believe in more. We show how deception/honesty in an argumentation of an agent, if detected, would alter the agent's perceived trustworthiness, and how that may affect their judgement as to which arguments should be acceptable. 1 Introduction To adequately characterise multi-agent argumentation, it is important to model what an agent sees of other agents' argumentations ( Epistemic Aspect). It is also important to model how agents interact with others ( Agent-to-Agent Interaction). These two factors determine dynamics of multi-agent argumentation, and are thus central to: argumentation-based negotiations (Cf.