In this paper, we present a general framework for ranking sets of arguments in abstract argumentation based on their plausibility of acceptance. We present a generalisation of Dung's extension semantics as extension-ranking semantics, which induce a preorder over the power set of all arguments, allow ing us to state that one set is "closer" to being acceptable than another . To evaluate the extension-ranking semantics, we introduce a number of p rinciples that a well-behaved extension-ranking semantics should satisfy. W e consider several simple base relations, each of which models a single central a spect of argumentative reasoning. The combination of these base relations provides us with a family of extension-ranking semantics. We also adapt a numb er of approaches from the literature for ranking extensions to be us able in the context of extension-ranking semantics, and evaluate their beha viour. Keywords: Abstract Argumentation, Ranking Sets of Objects, Extension-ranking semantics 1. Introduction Formal argumentation [7] is concerned with models of rational decis ion-making based on representations of arguments and their relations.

Sam Altman is set to make a return as chief executive of OpenAI after the ChatGPT developer said it had "reached an agreement in principle" for his reinstatement. The San Francisco-based company made the announcement after days of corporate drama in the wake of Altman's surprise sacking on Friday. Nearly all of OpenAI's 750-strong workforce had threatened to quit unless the board overseeing the business brought back Altman and then quit immediately afterwards. As part of the agreement reached overnight, the deal includes a new-look board led by Bret Taylor, the former co-CEO of software firm Salesforce. It will include Larry Summers, the former US treasury secretary, and Adam D'Angelo, the tech entrepreneur and current board member who played a role in Altman's firing.

In the 1991 movie "Terminator 2: Judgment Day," a sentient killer robot travels back in time to stop the rise of artificial intelligence. The robot locates the computer scientist whose work will lead to the creation of Skynet, a computer system that will destroy the world, and convinces him that A.I. development must be stopped immediately. Together, they travel to the headquarters of Cyberdyne Systems, the company behind Skynet, and blow it up. The A.I. research is destroyed, and the course of history is changed--at least, for the rest of the film. In the sci-fi world of "Terminator 2," it's crystal clear what it means for an A.I. to become "self-aware," or to pose a danger to humanity; it's equally obvious what might be done to stop it.

Newly ousted OpenAI CEO Sam Altman and former president Greg Brockman are meeting with executives at the company's San Francisco headquarters now as discussions about possibly reinstating their positions continue, The Information reports. Per The Information, interim CEO Mira Murati and others have been leading the push to get Altman reinstated as CEO, and invited the two to HQ on Sunday. Altman and Brockman showed up for talks this afternoon, sources told The Information. Around the time of the report's publication, Altman tweeted a photo of himself wearing a guest badge for entry into the building, writing, "first and last time i ever wear one of these (sic)" -- which could be interpreted several different ways, at this point. Sources told The Verge that Altman has set a 5PM PT deadline for board members to reach an agreement that could ultimately determine whether he walks away from OpenAI, or they do. After Altman was fired without warning on Friday, Brockman stepped down in solidarity, along with a slew of senior researchers.

Sam Altman is being lined up for a surprise return as the chief executive of the ChatGPT developer OpenAI amid pressure from investors to reverse his surprise ousting. Altman was fired by the company board on Friday, citing a failure to be "candid in his communications", in a move that shocked Silicon Valley. However, OpenAI's investors – who include Microsoft – are pushing for his reinstatement, according to reports. On Saturday, the Information, a tech news website, reported that OpenAI was "optimistic" it could bring back Altman. The report quoted a memo from the company's chief strategy officer, Jason Kwon, telling staff that an effort was under way to bring back Altman and other senior colleagues who had left. "We are still working towards a resolution and we remain optimistic," Kwon wrote, according to the Information.

We study invariant local expansion operators for conflict-free and admissible sets in Abstract Argumentation Frameworks (AFs). Such operators are directly applied on AFs, and are invariant with respect to a chosen "semantics" (that is w.r.t. each of the conflict free/admissible set of arguments). Accordingly, we derive a definition of robustness for AFs in terms of the number of times such operators can be applied without producing any change in the chosen semantics.

Using a novel model of neural event dynamics, we investigate how cortical structures generate event representations during continuous narratives, and how these events are stored and retrieved from long-term memory. Our data-driven approach enables identification of event boundaries and event correspondences across datasets without human-generated stimulus annotations, and reveals that different regions segment narratives at different timescales. We also provide the first direct evidence that narrative event boundaries in high-order areas (overlapping the default mode network) trigger encoding processes in the hippocampus, and that this encoding activity predicts pattern reinstatement during recall. Finally, we demonstrate that these areas represent abstract, multimodal situation models, and show anticipatory event reinstatement as subjects listen to a familiar narrative. Our results provide strong evidence that brain activity is naturally structured into semantically meaningful events, which are stored in and retrieved from long-term memory.

We address the issue, in cognitive agents, of possible loss of previous information, which later might turn out to be correct when new information becomes available. To this aim, we propose a framework for changing the agent's mind without erasing forever previous information, thus allowing its recovery in case the change turns out to be wrong. In this new framework, a piece of information is represented as an argument which can be more or less accepted depending on the trustworthiness of the agent who proposes it. We adopt possibility theory to represent uncertainty about the information, and to model the fact that information sources can be only partially trusted. The originality of the proposed framework lies in the following two points: (i) argument reinstatement is mirrored in belief reinstatement in order to avoid the loss of previous information; (ii) new incoming information is represented under the form of arguments and it is associated with a plausibility degree depending on the trustworthiness of the information source.

Argumentation is now a very fertile area of research in Artificial Intelligence. Yet, most approaches to reasoning with arguments in AI are based on a normative perspective, relying on intuition as to what constitutes correct reasoning, sometimes aided by purpose-built hypothetical examples. For these models to be useful in agent-human argumentation, they can benefit from an alternative, positivist perspective that takes into account the empirical reality of human reasoning. To give a flavour of the kinds of lessons that this methodology can provide, we report on a psychological study exploring simple reinstatement in argumentation semantics. Empirical results show that while reinstatement is cognitively plausible in principle, it does not yield full recovery of the argument status, a notion not captured in Dung's classical model. This result suggests some possible avenues for research relevant to making formal models of argument more useful.

Dung's abstract theory of argumentation has become established as a general framework for various species of non-monotonic reasoning, and reasoning in the presence of conflict. A Dung framework consists of arguments related by attacks, and the extensions of a framework, and so the status of arguments, are defined under different semantics. Developments of Dung's work have also defined argument labellings as an alternative way of characterising extensions, and dialectical argument game proof theories for establishing the status of individual arguments. Recently, Extended Argumentation Frameworks extend Dung's theory so that arguments not only attack arguments, but attacks themselves. In this way, the extended theory provides an abstract framework for principled integration of meta-level argumentation about defeasible preferences applied to resolve conflicts between object level arguments. In this paper we formalise labellings and argument games for a selection of Dung's semantics defined for the extended frameworks.