Computers have already changed the way that humans do mathematics: they enable us to compute efficiently. But will they soon be helping us to reason? And will they one day start reasoning themselves? We give an overview of recent developments in neural networks, computer theorem provers and large language models.
Apostolis Zarras, Athanasia Kollarou, Aristeidis Farao
et al.
The rising adoption of artificial intelligence and machine learning in critical sectors underscores the pressing need for robust systems capable of withstanding adversarial threats. While deep learning architectures have revolutionized tasks such as image recognition, their susceptibility to adversarial techniques remains an open challenge. This article evaluates the impact of various adversarial methods, including the fast gradient sign method, projected gradient descent, DeepFool, and Carlini & Wagner, on five neural network models: a fully connected neural network, LeNet, Simple convolutional neural network (CNN), MobileNetV2, and VGG11. Using the E V AI SION tool explicitly developed for this research, these attacks were implemented and analyzed based on accuracy, F1-score, and misclassification rate. The results revealed varying levels of vulnerability across the tested models, with simpler architectures occasionally outperforming more complex ones. These findings emphasize the importance of selecting the most appropriate adversarial technique for a given architecture and customizing the associated attack parameters to achieve optimal results in each scenario.
Filipa Campos, Angelica Sharma, Bijal Patel
et al.
Liver dysfunction can occur in patients presenting with thyrotoxicosis, due to several different aetiologies. A 42-year-old man had mild liver dysfunction on presentation with hyperthyroidism due to Graves’ disease (GD): ALT 65 (0–45 IU/L), fT4 41.2 (9–23 pmol/L), fT3 > 30.7 (2.4–6 pmol/L), and TSH < 0.01 (0.3–4.2 mIU/L). His liver dysfunction worsened following the initiation of the antithyroid drug (ATD) carbimazole (CBZ), with ALT reaching a zenith of 263 IU/L at 8 weeks following presentation. Consequently, CBZ was stopped, and he was managed with urgent radioiodine therapy. His liver function tests (LFTs) improved within 1 week of stopping carbimazole (ALT 74 IU/L). Thionamide-induced liver dysfunction is more typically associated with a ‘cholestatic’ pattern, although he had a ‘hepatitic’ pattern of liver dysfunction. The risk of liver dysfunction in GD increases with older age and higher titres of thyroid-stimulating hormone receptor antibody (TRAb). This review of the literature seeks to explore the possible causes of liver dysfunction in a patient presenting with hyperthyroidism, including thyrotoxicosis-induced liver dysfunction, ATD-related liver dysfunction, and the exacerbation of underlying unrelated liver disease.
Bayesian networks (BNs) are widely used for modeling complex systems with uncertainty, yet repositories of pre-built BNs remain limited. This paper introduces bnRep, an open-source R package offering a comprehensive collection of documented BNs, facilitating benchmarking, replicability, and education. With over 200 networks from academic publications, bnRep integrates seamlessly with bnlearn and other R packages, providing users with interactive tools for network exploration.
We provide a semantic characterization of AGM belief contraction based on frames consisting of a Kripke belief relation and a Stalnaker-Lewis selection function. The central idea is as follows. Let K be the initial belief set and K-A be the contraction of K by the formula A; then B belongs to the set K-A if and only if, at the actual state, the agent believes B and believes that if not-A is (were) the case then B is (would be) the case.
Over the past two decades, PKAW has provided a forum for researchers and practitioners to discuss the state-of-the-arts in the area of knowledge acquisition and machine intelligence (MI, also Artificial Intelligence, AI). PKAW2022 will continue the above focus and welcome the contributions on the multi-disciplinary approach of human and big data-driven knowledge acquisition, as well as AI techniques and applications.
Taeyoung Kim, Luiz Felipe Vecchietti, Kyujin Choi
et al.
In multi-agent reinforcement learning, the cooperative learning behavior of agents is very important. In the field of heterogeneous multi-agent reinforcement learning, cooperative behavior among different types of agents in a group is pursued. Learning a joint-action set during centralized training is an attractive way to obtain such cooperative behavior; however, this method brings limited learning performance with heterogeneous agents. To improve the learning performance of heterogeneous agents during centralized training, two-stage heterogeneous centralized training which allows the training of multiple roles of heterogeneous agents is proposed. During training, two training processes are conducted in a series. One of the two stages is to attempt training each agent according to its role, aiming at the maximization of individual role rewards. The other is for training the agents as a whole to make them learn cooperative behaviors while attempting to maximize shared collective rewards, e.g., team rewards. Because these two training processes are conducted in a series in every time step, agents can learn how to maximize role rewards and team rewards simultaneously. The proposed method is applied to 5 versus 5 AI robot soccer for validation. The experiments are performed in a robot soccer environment using Webots robot simulation software. Simulation results show that the proposed method can train the robots of the robot soccer team effectively, achieving higher role rewards and higher team rewards as compared to other three approaches that can be used to solve problems of training cooperative multi-agent. Quantitatively, a team trained by the proposed method improves the score concede rate by 5% to 30% when compared to teams trained with the other approaches in matches against evaluation teams.
Implicational bases are objects of interest in formal concept analysis and its applications. Unfortunately, even the smallest base, the Duquenne-Guigues base, has an exponential size in the worst case. In this paper, we use results on the average number of minimal transversals in random hypergraphs to show that the base of proper premises is, on average, of quasi-polynomial size.
El presente artículo indaga las maneras en que la comunicación transmedia aunada a la participación ciudadana se constituye en una estrategia para generar y fortalecer procesos de movilización social. Plantea un recorrido teórico y conceptual sobre el concepto de narrativas transmedia en marco de la movilización social y toma como ejemplo para el análisis, por su representatividad y reconocimiento en las dinámicas sociales y culturales, al colectivo de Hip- Hop Casa Kolacho, cuyas acciones se llevan a cabo en la Comuna 13 de Medellín, territorio referente en la producción del Hip-hop en la ciudad. Se investiga además la perspectiva de éstos jóvenes involucrados en la producción cultural y creativa, relacionada con el Hip-Hop en la ciudad de Medellín. Para lograrlo, se llevó a cabo un análisis de redes sociales en el que se presentan contenidos relacionados con las producciones asociadas al Centro Cultural Casa Kolacho, la aplicación de entrevistas y seguimiento de las actividades de algunos graffiteros y miembros del Colectivo Casa Kolacho.
This paper is concerned with the apparent greatest weakness of the Mathematical Theory of Evidence (MTE) of Shafer \cite{Shafer:76}, which has been strongly criticized by Wasserman \cite{Wasserman:92ijar} - the relationship to frequencies. Weaknesses of various proposals of probabilistic interpretation of MTE belief functions are demonstrated. A new frequency-based interpretation is presented overcoming various drawbacks of earlier interpretations.
With the advent of modern computer networks, fault diagnosis has been a focus of research activity. This paper reviews the history of fault diagnosis in networks and discusses the main methods in information gathering section, information analyzing section and diagnosing and revolving section of fault diagnosis in networks. Emphasis will be placed upon knowledge-based methods with discussing the advantages and shortcomings of the different methods. The survey is concluded with a description of some open problems.
This article discusses how the automation of tensor algorithms, based on A Mathematics of Arrays and Psi Calculus, and a new way to represent numbers, Unum Arithmetic, enables mechanically provable, scalable, portable, and more numerically accurate software.
In this paper, we reexamine the Movie Graph Argument, which demonstrates a basic incompatibility between computationalism and materialism. We discover that the incompatibility is only manifest in singular classical-like universes. If we accept that we live in a Multiverse, then the incompatibility goes away, but in that case another line of argument shows that with computationalism, the fundamental, or primitive materiality has no causal influence on what is observed, which must must be derivable from basic arithmetic properties.
Stochastic local search (SLS) algorithms have exhibited great effectiveness in finding models of random instances of the Boolean satisfiability problem (SAT). As one of the most widely known and used SLS algorithm, WalkSAT plays a key role in the evolutions of SLS for SAT, and also hold state-of-the-art performance on random instances. This work proposes a novel implementation for WalkSAT which decreases the redundant calculations leading to a dramatically speeding up, thus dominates the latest version of WalkSAT including its advanced variants.
The article looks at mass market artificial intelligence tools in the context of their ever-growing sophistication, availability and market penetration. The subject is especially relevant today for these exact reasons - if a few years ago AI was the subject of high tech research and science fiction novels, today, we increasingly rely on cloud robotics to cater to our daily needs - to trade stock, predict weather, manage diaries, find friends and buy presents online.
Various properties of relative entropy have led to its widespread use in information theory. These properties suggest that relative entropy has a role to play in systems that attempt to perform inference in terms of probability distributions. In this paper, I will review some basic properties of relative entropy as well as its role in probabilistic inference. I will also mention briefly a few existing and potential applications of relative entropy to so-called artificial intelligence (AI).
In this work, we present definition of intuitionistic fuzzy parameterized (IFP) intuitionistic fuzzy soft set and its operations. Then we define IFP-aggregation operator to form IFP-intuitionistic fuzzy soft-decision-making method which allows constructing more efficient decision processes.
We describe a viewpoint on the Dempster/Shafer 'Theory of Evidence', and provide an interpretation which regards the combination formulas as statistics of the opinions of "experts". This is done by introducing spaces with binary operations that are simpler to interpret or simpler to implement than the standard combination formula, and showing that these spaces can be mapped homomorphically onto the Dempster/Shafer theory of evidence space. The experts in the space of "opinions of experts" combine information in a Bayesian fashion. We present alternative spaces for the combination of evidence suggested by this viewpoint.
In this paper, we continue our research on the algorithmic aspects of Halpern and Pearl's causes and explanations in the structural-model approach. To this end, we present new characterizations of weak causes for certain classes of causal models, which show that under suitable restrictions deciding causes and explanations is tractable. To our knowledge, these are the first explicit tractability results for the structural-model approach.
We introduce a family of new equational semantics for argumentation networks which can handle odd and even loops in a uniform manner. We offer one version of equational semantics which is equivalent to CF2 semantics, and a better version which gives the same results as traditional Dung semantics for even loops but can still handle odd loops.