Dr. Alejandro Guerra Hernández

I did my doctorate at the Université de Paris 13, where I worked on the problem of learning in a BDI Multi-Agent System. My master degree, at the Universidad Veracruzana, was about a simulator for behavior networks. I've been working on intentionality and learning in the context of rational agents, agent based simulations, as well as agent based data mining. I also teach artificial intelligence, logic programming, functional programming, and agent oriented programming.

Dr. Alejandro Guerra Hernández ha publicado 31 entradas:

New article in Mathematical and Computational Applications

D.Martínez Galicia, A. Guerra-Hernández, N. Cruz-Ramírez, X. Limón, F. Grimaldo. Windowing as a Sub-Sampling Method for Distributed Data Mining. Mathematical and Computational Applications,  25(3)1:39. June 2020. ISSN 2297-8747.

Abstract: Windowing is a sub-sampling method, originally proposed to cope with large datasets when inducing decision trees with the ID3 and C4.5 algorithms. The method exhibits a strong negative correlation between the accuracy of the learned models and the number of examples used to induce them, i.e., the higher the accuracy of the obtained model, the fewer examples used to induce it. This paper contributes to a better understanding of this behavior in order to promote windowing as a sub-sampling method for Distributed Data Mining. For this, the generalization of the behavior of windowing beyond decision trees is established, by corroborating the observed negative correlation when adopting inductive algorithms of different nature. Then, focusing on decision trees, the windows (samples) and the obtained models are analyzed in terms of Minimum Description Length (MDL), Area Under the ROC Curve (AUC), Kulllback–Leibler divergence, and the similitude metric Sim1; and compared to those obtained when using traditional methods: random, balanced, and stratified samplings. It is shown that the aggressive sampling performed by windowing, up to 3% of the original dataset, induces models that are significantly more accurate than those obtained from the traditional sampling methods, among which only the balanced sampling is comparable in terms of AUC. Although the considered informational properties did not correlate with the obtained accuracy, they provide clues about the behavior of windowing and suggest further experiments to enhance such understanding and the performance of the method, i.e., studying the evolution of the windows over time.

Categories: Blog personal

New Article in Studies in Computational Intelligence

A. Platas-López, A. Guerra-Hernández, N. Cruz-Ramírez, M. Quiroz-Castellanos, F. Grimaldo, M. Paolucci, and F. Cecconi. Intuitionistic and Type-2 Fuzzy Logic Ehancements in Neural and Op- timization Algorithms: Theory and Applications, volume 862 of Studies in Computational Intelligence, chapter Towards an Ageng-Based Model for Analysis of Macroeconomic Signals, pages 551–565. Springer, Cham, Switzerland, 2020.

 

Abstract. This work introduces an agent-based model for the analysis of macroeconomic signals. The Bottom-up Adaptive Model (BAM) deploys a closed Walrasian economy where three types of agents (households, firms and banks) interact in three markets (goods, labor and credit) producing some signals of interest, e.g., unemployment rate, GDP, inflation, wealth distribution, etc. Agents are bounded rational, i.e., their behavior is defined in terms of simple rules finitely searching for the best salary, the best price, and the lowest interest rate in the corresponding markets, under incomplete information. The markets define fixed protocols of interaction adopted by the agents. The observed signals are emergent properties of the whole system. All this contrasts with the traditional macroeconomic approach based on the general equilibrium model, where perfect rationality and/or full information availability are assumed. The model is defined following the Overview, Design concepts, and Details Protocol and implemented in NetLogo. BAM is promoted as a toolbox for studying the macroeconomic effects of the agent activities at the service of the elaboration of public policies.

Categories: Blog personal

New paper accepted at CCIA 2019

Our paper “Micro-foundations of macroeconomic dynamics: the agent-based BAM model” has been accepted for presentation at CCIA 2019, to be held in Colonia de Sant Jordi. Congratulations to the co-authors: Alejandro Platas López, Federico Cecconi, Mario Paolucci, and Francisco Grimaldo.

Abstract: This paper presents an open-source agent-based implementation of the BAM model, a micro-founded simulation of macroeconomic basic dynamics defined in the reference book Macroeconomics from the Bottom-up. By exploring the parameter space of our simulation we show that: i) BAM reproduces numerous stylized facts and its parameters influence the outputs plausibly; and ii) the effects of changing the size of markets and introducing shocks of different sizes are as expected. The outcomes are measured in terms of gross domestic product, inflation and unemployment rate, using monthly payments as time scale. These results confirm the fidelity and usability of our implementation, as well as the feasibility of the BAM model.

Categories: Blog personal

New paper at NEO 2019

Our paper “Analysis of Differential Evolution variants for parameter tuning of Decision Trees Inductive Algorithms” was accepted for its presentation at NEO 2019, to be held in Saltillo, México. The full program of the event will be available on August, 20th. Congratulations to the co-authors David Martínez Galicia y Efrén Mezura Montes.

Extended abstract: This paper presents an empirical comparison of some Differential Evolution variants to solve the parameter tuning of Decision Tree induction algorithms. The aim of this analysis is to identify which one of the variants is more competitive to this problem. In this work, an EVolutionary Agents \& Artifacts approach for the induction of Decision Trees (eva2dt), is adopted to maximize the classification accuracy.

The Agents & Artifacts paradigm naturally accommodates the concept of an evolving population of agents inducing and evaluating decision trees, using different tools implemented as Weka-based artifacts. The use of different inductive algorithms, instead of a single one, is a novelty with respect to related work. Eva2dt agents represent potential solutions for the Differential Evolution algorithm, which are mapped to parameters for inductive algorithms, e.g., j48, SimpleCART, RepTree and RandomTree. Each agent is able to induce decisions trees and to also compute their accuracy through a 10-folds cross-validation process.

The assessed Differential Evolution variants vary in the recombination operator adopted and in some self-adaptive mechanisms. A set of statistical tests were performed to validate the obtained results. All variants were tested on 16 public datasets, obtaining competitive results, particularly for the datasets with the fewer number of training instances. Preliminary results suggest that DE/rand/1/bin variant outperform the others, even when more generations are added. These observations will help to develop other mechanisms to improve the current performance (convergence and accuracy).

Categories: Blog personal

New article in CONISOFT 2018

X. Limón Riaño, A. Guerra-Hernández, A. J. Sánchez-García, J. C. Pérez Arriaga. SagaMAS: a software framework for distributed transactions in the microservice architecture. In: CONISOFT 2018 International Conference in Software Engineering Research and Innovation, San Luis Potosí, México, October 24th-26th, 2018. IEEE Computer Society, Los Alamitos, CA, USA, 2018.

Abstract. This paper introduces SagaMAS: a Multi-Agent based framework on development, dealing with distributed transactions in the microservices architecture. Microservices are an architectural style where the distributed system is decomposed in a series of highly cohesive and independent services. Each microservice can have different implementations and data persistence technologies, resulting in heterogeneous distributed systems. Given its heterogeneity and distributed nature, an open challenge in this architecture is the proper management of distributed transactions that expand through several microservices. MultiAgent Systems are by definition distributed systems suited for complex coordination tasks, such as this. The proposed framework can be seen as a decoupled autonomous layer that coordinates the distributed transactions of the system, relieving the microservice developer from such tasks, and simplifying microservice interactions. Unlike existing approaches, our proposal is conceived and usable at an abstraction level appropriate to express reliability and robustness issues in terms of agent coordination.

Keywords. Microservices, Software Architectures, Distributed Transactions, Multi-Agent Systems

Categories: Blog personal

Nuevo artículo KAIS

X. Limón, A. Guerra-Hernández, N. Cruz-Ramírez, F. Grimaldo. Modeling and implementing distributed data mining strategies in JaCa-DDM. Knowledge and Information Systems  (2018). ISSN: 0239-3116 KAIS

Abstract. This work introduces JaCa-DDM, a novel distributed data mining system founded on the agents and artifacts paradigm, conceived to design, implement, deploy, and evaluate learning strategies. Jason rational agents conform to such strategies to cope with distributed computing environments, where CArtAgO artifacts encapsulate learning algorithms, data sources, evaluation tools, and other services implemented in Weka for data mining tasks. The set of strategies presented in this paper aims at encouraging the use of JaCa-DDM to develop new ones, suited to different needs. For this, our system provides tools to evaluate the resulting models in terms of accuracy, number of instances employed to learn, time of convergence, and volume of communications. Although the emphasis in decision trees, JaCa-DDM can be easily extended by adopting new artifacts, e.g., for meta-learning. The main contributions of the paper are as follows: (i) From the multi-agent systems perspective, our approach illustrates how to exploit the so-called “agentification” of Weka for the sake of code reusability, while preserving the benefits of reasoning at the Belief–Desire–Intention level with Jason; (ii) from the data mining perspective, JaCa-DDM is promoted as an extensible tool to define and test distributed strategies; and (iii) a set of strategies including centralizing, meta-learning and Windowing-based approaches, is carefully analyzed to provide comparisons among them.

Categories: Blog personal

Nuevo capítulo en LNCS 10738

H. X. Limón Riaño, A. Guerra-Hernández, A. Ricci. Distributed Transparency in Endogeneous Environments: the JaCaMo Case. In: EMAS 2017 International Workshop on Engineering Multi-Agent Systems, Sao Paulo, Brasil, May 8th and 9th, 2017. Lecture Notes in Computer Science, vol 10738, pp. 109-124, Springer Verlag, Berlin Heidelberg, 2018. | Springer

Abstract. This paper deals with distribution aspects of endogenous environments, in this case, distribution refers to the deployment in several machines across a network. A recognized challenge is the achievement of distributed transparency, a mechanism that allows the agent working in a distributed environment to maintain the same level of abstraction as in local contexts. In this way, agents do not have to deal with details about network connections, which hinders their abstraction level, and the way they work in comparison with locally focused environments, reducing flexibility. This work proposes a model based on hierarchical workspaces, creating a distinctive layer for environment distribution, which the agents do not manage directly but can exploit as part of infrastructure services. The proposal is in the context of JaCaMo, the Multi-Agent Programming framework that combines the Jason, CArtAgO, and MOISE technologies, specially focusing on CArtAgO, which provides the means to program and organize the environment in terms of workspaces.

 Keywords. Distributed environments, Endogenous environments, Environment programming, JaCaMo framework 
Categories: Blog personal

Nueva Conferencia Magistral

He sido invitado muy amablemente a participar en el VI Foro de Divulgación en Ciencias de la Computación, celebrado en la Facultad de Estadística e Informática de la Universidad Veracruzana los días 6–7 de noviembre 2017,, con una conferencia magistral. El tema de la misma fue, como no, el Aprendizaje en los Sistemas Multi-Agentes BDI. La presentación de la conferencias la pueden consular aquí: 2017-uv-foro-fei.

Agradezco mucho la invitación de esta comunidad que ha aportado tantos buenos estudiantes a nuestros proyectos de investigación.

Xalapa, Ver., 6 de Noviembre de 2017

A. Guerra-Hernández

Categories: Blog personal

Nueva tesis doctoral: Héctor Xavier Limón Riaño

Muchas felicidades a Héctor Xavier Limón Riaño, quien obtuvo el Doctorado en Inteligencia Artificial con mención honorífica, por su tesis titulada “JaCa-DDM: a framework for Distributed Data Mining based on the Agents & Artifacts paradigm”, bajo mi dirección y la co-dirección del Dr. Nicandro Cruz Ramírez.

La tesis propone un marco conceptual para el diseño, implementación y despliegue de sistemas de minería de datos distribuidos, basado en agentes y artefactos. Los agentes deciden el flujo de trabajo a seguir, mientras que los artefactos encapsulan herramientas de minería de datos que estos pueden usar. JaCa-DDM (X. Limón, A. Guerra-Hernández, N. Cruz-Ramírez, and F. Grimaldo. An agents & artifacts approach to distributed data mining. In F. Castro, A. Gelbukh, and M. G. Mendoza, editors, MICAI 2013: Eleventh Mexican International Conference on Artificial Intelligence, volume 8266 of Lecture Notes in Artificial Intelligence, pages 338–349, Berlin Heidelberg, 2013. Springer Verlag) es la implementación en Jason y CArtAgO, de este marco conceptual. En este sistema, los artefactos encapsula objetos de Weka para que puedan ser usados por los agentes BDI implementados en Jason.

Se ha usado JaCa-DDM para implementar diversas estrategias bien conocidas en la minería de datos distribuida, como las basadas en centralizar datos y las basadas en meta-aprendizaje, p. ej., bosques de árboles aleatorios, demostrando así su pertinencia. Se ha usado también para explorar nuevas estrategias en torno a la inducción de árboles de decisión y la técnica conocida como Windowing, demostrando así su flexibilidad.

En este contexto, el Windowing se explota aprendiendo en un sitio una hipótesis inicial, para luego refinarla con los contra ejemplos que se puedan encontrar en los demás sitios. Diferentes estrategias son posibles dependiendo de qué se comunica en el sistema, p. ej., la hipótesis ó los contra ejemplos y en qué orden. La tesis presenta un análisis exhaustivo de todas estas posibilidades.

La tesis identifica un cuello de botella en el Windowing, al momento de buscar los contra ejemplos de una hipótesis. Esta limitante es superada usando GPUs para optimizar esta tarea (X. Limón, A. Guerra-Hernández, N. Cruz-Ramírez, H. G. Acosta-Mesa, and F. Grimaldo. A windowing based GPU optimized strategy for the induction of decision trees. In  volume 277 of Frontiers in Artificial Intelligence and Applications, pages 100-109, Amsterdam, NL, 2015. IOS Press). También se mejora el Windowing intentando  mantener el tamaño de la ventana lo más pequeño posible.

La estrategia que incorpora estas mejoras, fue probada en un problema de segmentación de imágenes para la detección de cáncer cérvico-uterino vía colposcopia (X. Limón, A. Guerra-Hernández, N. Cruz-Ramírez, H. G. Acosta-Mesa, and F. Grimaldo. A windowing strategy for distributed data mining optimized through gpus. Pattern Recognition Letters, 93:23-30, 2017).

La tesis también explora mejora a CArtAgO, la implementación usada del modelo de Artefactos, para hacer más transparente la implementación de las estrategias de aprendizaje distribuido (X. Limón, A. Guerra-Hernández, and A. Ricci. Distributed transparency in endogeneous environments: The JaCaMo case. In EMAS@AAMAS 2017 Workshop Notes. Engineering Multi Agent Systems, Fifth International Workshop, Sao Paulo, Brasil, May 8th and 9th, 2017., pages 142–157, 2017).

Por lo anterior, podemos afirmar que la tesis presenta avances significativos para las áreas de minería de datos y sistemas multi-agente.

Categories: Blog personal

Nuevo artículo aceptado en EMAS@AAMAS 2017

Xavier Limón, Alejandro Guerra-Hernández, Alessandro Ricci. Distribuye Transparency in Endogenous Environments: The JaCaMo Case. EMAS@AAMAS, Sao Paulo, Brasil, May 8-9, 2017.

Abstract. This paper deals with distribution aspects of endogenous en-vironments, in this case, distribution refers to the deployment in several machines across a network. A recognized challenge is the achievement of distributed transparency, a mechanism that allows the agent working in a distributed environment to maintain the same level of abstraction as in local contexts. In this way, agents do not have to deal with details about network connections, which hinders their abstraction level, and the way they work in comparison with locally focused environments, reducing flexibility. This work proposes a model that creates a distinctive layer for environment distribution, which the agents do not manage directly but can exploit as part of infrastructure services. The proposal is in the context of JaCaMo, the Multi-Agent Programming framework that combines the Jason, CArtAgO, and MOISE technologies, specially focusing on CArtAgO, which provides the means to program the environment. The proposal makes an extensive use of the concept of workspace to organize the environment and transparently manage different distributed sites.

Categories: Blog personal

contacto

Dr. Alejandro Guerra Hernández