Decision Making Intelligent Agent on SOX Compliance over the Imports Process

The objective of this work is to define a decision support system over SOX (Sarbanes-Oxley Act) compatibility  of the Imports Process based on Artificial Intelligence and Theory of Argumentation knowledge and techniques measuring at the same time the quality of how things were done on this specific process of the analyzed business case. SOX Law in effect nowadays is worldwide facto standard for financial and economical operations of private sector with the main objective to protect investors of private sector and promote the financial health of private companies. In this framework we have developed a decision support intelligent expert model to help SOX control bodies, companies and auditors to support their SOX compliance decisions based on well founded bases like Artificial Intelligence and Theory of Argumentation. The model here presented incorporates several key concepts like pre-existing expert knowledge base, a formalized and structure way to evaluate an existing business case focusing on the Imports Process, a semi automated fuzzy dynamic knowledge learning protocol and an structure method to evolve based on the facts of the business case and suggest an specific decision about the SOX compatibility of the specific business case. Keywords: Multiagent Systems (MAS), Expert Systems (ES), Business Intelligence (BI), Decision Support Systems (DSS), Sarbanes-Oxley Act (SOX), Argumentation, Artificial Intelligence

Revolutionizing Pharmaceuticals: Generative Artificial Intelligence as a bibliographic assistant

[EN]Artificial Generative Intelligence (AGI) has exploded into biomedical and pharmaceutical research, fundamentally transforming the way scientists approach literature review, experiment design, and reagent and antibody selection. This article explores how IAG, supported by advanced machine learning and natural language processing models, has revolutionized these processes. The IAG streamlines literature review, extracting relevant information, identifying emerging patterns and trends in the scientific literature, and generating innovative hypotheses. It also acts as an advanced search tool, allowing researchers to quickly access accurate information in an ocean of data. A prominent example of this application is BenchSci, a platform that uses the IAG to recommend reagents and antibodies based on real experimental data and scientific literature. This integration of IAG into experimental design promises to accelerate research, reduce costs, and improve the precision of experiments. Together, the IAG is presented as a catalyst for discoveries in pharmaceutical and biomedical research, offering unprecedented potential to advance the understanding and treatment of diseases, and improve decision-making in the industry

Decision Making Intelligent Agent on SOX Compliance over the Goods Receipt Processs

The objective of this work is to define a decision support system over SOX (Sarbanes-Oxley Act) compatibility  of the Goods Receipt Process based on Artificial Intelligence and Theory of Argumentation knowledge and techniques measuring at the same time the quality of how things were done on this specific process of the analyzed business case. SOX Law in effect nowadays is worldwide facto standard for financial and economical operations of private sector with the main objective to protect investors of private sector and promote the financial health of private companies. In this framework we have developed a decision support intelligent expert model to help SOX control bodies, companies and auditors to support their SOX compliance decisions based on well founded bases like Artificial Intelligence and Theory of Argumentation. The model here presented incorporates several key concepts like pre-existing expert knowledge base, a formalized and structure way to evaluate an existing business case focusing on the Goods Receipt Process, a semi automated fuzzy dynamic knowledge learning protocol and an structure method to evolve based on the facts of the business case and suggest an specific decision about the SOX compatibility of the specific business case. Keywords: Multiagent Systems (MAS), Expert Systems (ES), Business Intelligence (BI), Decision Support Systems (DSS), Sarbanes-Oxley Act (SOX), Argumentation, Artificial Intelligence

Bilateral contract prices estimation using a Q-learning based approach

The electricity markets restructuring process encouraged the use of computational tools in order to allow the study of different market mechanisms and the relationships between the participating entities. Automated negotiation plays a crucial role in the decision support for energy transactions due to the constant need for players to engage in bilateral negotiations. This paper proposes a methodology to estimate bilateral contract prices, which is essential to support market players in their decisions, enabling adequate risk management of the negotiation process. The proposed approach uses an adaptation of the Q-Learning reinforcement learning algorithm to choose the best from a set of possible contract prices forecasts that are determined using several methods, such as artificial neural networks (ANN), support vector machines (SVM), among others. The learning process assesses the probability of success of each forecasting method, by comparing the expected negotiation price with the historic data contracts of competitor players. The negotiation scenario identified as the most probable scenario that the player will face during the negotiation process is the one that presents the higher expected utility value. This approach allows the supported player to be prepared for the negotiation scenario that is the most likely to represent a reliable approximation of the actual negotiation environment.This work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 703689 (project ADAPT) and No 641794 (project DREAM-GO); NetEfficity Project (P2020 − 18015); and UID/EEA/00760/2013 funded by FEDER Funds through COMPETE pro-gram and by National Funds through FCT.info:eu-repo/semantics/publishedVersio

Context aware Q-Learning-based model for decision support in the negotiation of energy contracts

[EN] Automated negotiation plays a crucial role in the decision support for bilateral energy transactions. In fact, an adequate analysis of past actions of opposing negotiators can improve the decision-making process of market players, allowing them to choose the most appropriate parties to negotiate with in order to increase their outcomes. This paper proposes a new model to estimate the expected prices that can be achieved in bilateral contracts under a specific context, enabling adequate risk management in the negotiation process. The proposed approach is based on an adaptation of the Q-Learning reinforcement learning algorithm to choose the best scenario (set of forecast contract prices) from a set of possible scenarios that are determined using several forecasting and estimation methods. The learning process assesses the probability of occurrence of each scenario, by comparing each expected scenario with the real scenario. The final chosen scenario is the one that presents the higher expected utility value. Besides, the learning method can determine which is the best scenario for each context, since the behaviour of players can change according to the negotiation environment. Consequently, these conditions influence the final contract price of negotiations. This approach allows the supported player to be prepared for the negotiation scenario that is the most probable to represent a reliable approximation of the actual negotiation environme

Inteligencia Artificial Generativa en Farmacología: Revolucionando la Interacción Fármaco-Proteína

[ES]En la era moderna de la medicina, la inteligencia artificial (IA) está desempeñando un papel cada vez más prominente en la farmacología. Este artículo destaca la influencia transformadora de la IA generativa en el estudio y optimización de interacciones fármacoproteína. A través del análisis detallado de modelos como las Redes Generativas Adversariales (GANs) y los Transformers, se ilustra cómo la IA puede predecir y refinar las interacciones entre medicamentos y proteínas, llevando a terapias más eficientes y seguras. Además, se discute la capacidad de la IA generativa para prever interacciones cruzadas entre fármacos, reduciendo así el potencial de efectos secundarios y complicaciones relacionadas con la polifarmacia. La conclusión subraya la sinergia entre la investigación farmacológica tradicional y la IA, delineando un futuro optimista para el diseño y optimización de medicamentos en la medicina del siglo XXI

Improving the revision stage of a CBR system with belief revision techniques.

This paper presents a method for automating the revise phase of case-based reasoning systems. The revision is carried out using a rule -based system, which adapts its rules or knowledge base as the working environment, to which the system is applied, evolves in time. This actualisation is carried out by a belief revision technique. An example has been introduced to illustrate the working mode of the revision techniqu

Revolucionando la farmacéutica: aplicaciones y potencial de la Inteligencia Artificial Generativa en el descubrimiento de medicamentos

[ES]La inteligencia artificial (IA) ha emergido como una herramienta transformadora en la industria farmacéutica, revolucionando el proceso tradicional de descubrimiento y desarrollo de medicamentos. Mediante técnicas generativas avanzadas, como las Redes Generativas Adversariales (GANs) y los Autocodificadores Variacionales (VAEs), se ha potenciado la exploración y diseño de moléculas terapéuticas novedosas y viables. Adicionalmente, la IA facilita la optimización de estas moléculas garantizando propiedades deseables y acelera la identificación de objetivos terapéuticos a través del análisis profundo de conjuntos de datos biomédicos y genómicos. Uno de los avances más significativos ha sido el repurposing de medicamentos, donde la IA desbloquea el potencial oculto de fármacos conocidos para nuevas indicaciones terapéuticas. Este artículo revisa el impacto y las aplicaciones de la IA generativa en la industria farmacéutica, subrayando cómo esta tecnología promete acelerar la entrega de soluciones terapéuticas más efectivas y seguras