Alejandro Guerra-HernándezUniversity of Veracruz | UV · Instituto de Investigaciones en Inteligencia Artificial
Alejandro Guerra-Hernández
Docteur en Informatique
About
76
Publications
48,214
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484
Citations
Introduction
I have been interested in intentional learning in BDI models of rationality, i.e., the way an agent learns to form and abandon intentions based on her/his experience; and recently in agent mining, or more precisely, in the way learning is performed by a multi-agent system where training examples are distributed. I’m also interested in formal methods for expressing theories about learning and rational agency. More info: http://www.uv.mx/personal/aguerra
Additional affiliations
August 2014 - July 2020
October 2004 - July 2014
September 2001 - December 2003
Education
September 1998 - December 2003
September 1996 - December 1997
Publications
Publications (76)
This work proposes an agent-based approach to study the effect of extortion on macroeconomic aggregates, despite the fact that there is little data on this criminal activity given its hidden nature. We develop a Bottom-up Adaptive Macroeconomics (BAM) model that simulates a healthy economy, including a moderate inflation and a reasonable unemployme...
This article proposes an extension for the Agents and Artifacts meta-model to enable modularization. We adopt the Belief-Desire-Intention (BDI) model of agency to represent independent and reusable units of code by means of modules. The key idea behind our proposal is to take advantage of the syntactic notion of namespace, i.e., a unique symbol ide...
Agent-based modeling (ABM) has become popular since it allows a direct representation of heterogeneous individual entities, their decisions, and their interactions, in a given space. With the increase in the amount of data in different domains, an opportunity to support the design, implementation, and analysis of these models, using Machine Learnin...
Agent‐based models have diversified their applications across various domains due to the ease with which different phenomena can be represented and simulated. These models incorporate heterogeneous, autonomous agents, local interactions, bounded rationality, and often feature explicit spatial representations. However, certain challenges have been i...
Although the negative consequences of noise during induction have been widely studied, previous work often lacks the use of validated data to measure its impact. We propose a framework based on Bayesian Networks for modeling class noise and generating synthetic data sets where the kind and amount of class noise are under control. The benefits of th...
La resolución de problemas es un objetivo prioritario a nivel internacional que resulta transversal a todas las áreas STEAM. En este trabajo se valora la puesta en práctica de un método que emplea estrategias de lectura y representaciones gráficas para el planteamiento y la resolución de problemas de matemáticas.
Windowing is a guided sub-sampling method conceived to reduce the number of training examples when inducing decision trees, while preserving accuracy. However, its use has been discouraged when dealing with noisy datasets because of a lack of observed benefits. This work proposes modeling such domains probabilistically to study the performance of W...
Appropriate teaching–learning strategies lead to student engagement during learning activities. Scientific progress and modern technology have made it possible to measure engagement in educational settings by reading and analyzing student physiological signals through sensors attached to wearables. This work is a review of current student engagemen...
Knowing student emotions allows teachers to efficiently adapt or redirect educational resources, activities, learning environments, and learning procedures within a particular educational community, where age, learning styles, and skills are already challenging factors. This book chapter introduces a literature review of text-based emotion detectio...
This work proposes Differential Evolution (DE) to train parameters of Bayesian Networks (BN) for optimizing the Conditional Log-Likelihood (Discriminative Learning) instead of the log-likelihood (Generative Learning). Any given BN structure encodes assumptions about conditional independencies among the attributes and will result in error if they do...
This work proposes Differential Evolution (DE) to train parameters of Bayesian Networks (BN) for optimizing the Conditional Log-Likelihood (Discriminative Learning) instead of the log-likelihood (Generative Learning). Although Discriminative Parameter Learning algorithms have been proposed, to the best of the authors' knowledge, a metaheuristic app...
![Configuration of the counter strategy. Adopted from Limón et al. [10].](profile/Alejandro-Guerra-Hernandez/publication/342562976/figure/tbl1/AS:908318306488320@1593571368360/Configuration-of-the-counter-strategy-Adopted-from-Limon-et-al-10_Q320.jpg)
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...
Windowing is a sub-sampling method that enables the induction of decision trees with large datasets. Using a small sample of the available training examples, the method can achieve levels of accuracy comparable or better than those obtained using the full available dataset. More relevant is the fact that Windowing-based strategies for Distributed D...
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 d...
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 output...
![The agents and artifacts meta-model, adapted from Ricci et al. [41]](publication/325879443/figure/fig1/AS:960349117354004@1605976480872/The-agents-and-artifacts-meta-model-adapted-from-Ricci-et-al41_Q320.jpg)
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...
La arquitectura de microservicios ha surgido como respuesta al r´apido cambio tecnol´ogico, la preocupaci´on de mayor extensibilidad, escalabilidad y a la necesidad de ciclos de entrega de software cada vez m´as cortos. En esta arquitectura, el sistema, usualmente distribuido, es descompuesto en una serie de servicios altamente cohesivos e independ...
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...
This paper introduces an optimized Windowing based strategy for inducing decision trees in Distributed Data Mining scenarios. Windowing consists in selecting a sample of the available training examples (the window) to induce a decision tree with an usual algorithm, e.g., J48; finding instances not covered by this tree (counter examples) in the rema...
![Fig. 1. Clasificación de la población [4].](profile/Alejandro-Guerra-Hernandez/publication/309285148/figure/fig1/AS:418962723622912@1476899905009/Clasificacion-de-la-poblacion-4_Q320.jpg)
In Mexico, informal employment is a social phenomenon of interest, given that about 60 % of the workers are in this situation. This paper presents an analysis of informal employment based a bayesian network model obtained from the data from the National Survey of Occu- pation and Employment, obtained by the National Institute of Statistics and Geog...
In this paper we propose a model for designing Belief-Desire-Intention (BDI) agents under the principles of modularity. We aim to encapsulate agent functionalities expressed as BDI abstractions into independent , reusable and easier to maintain units of code, which agents can dynamically load. The general idea of our approach is to exploit the noti...
When inducing Decision Trees, Windowing consists in selecting a random subset of the available training instances (the window) to induce a tree, and then enhance it by adding counter examples, i.e., instances not covered by the tree, to the window for inducing a new tree. The process iterates until all instances are well classified or no accuracy i...
This paper introduces three protocols that define Social Learning on Jason, the Java-based implementation of AgentSpeak(L). The implementation of these protocols is based on the Instructive stage of a classification correlated with the developmental stages of a child. Agents defined in these protocols are able to share both the inputs and the outpu...
This paper presents a collaborative learning protocol dealing with vertical partitions in training data, i.e., The attributes of the instances are distributed in different data sources. The protocol has been modeled and implemented following the Agents and Artifacts paradigm. The artifacts provide Weka based learning tools to induce and evaluate De...
The bias-variance dilemma is a well-known and important problem in Machine Learning. It basically relates the generalization capability (goodness of fit) of a learning method to its corresponding complexity. When we have enough data at hand, it is possible to use these data in such a way so as to minimize overfitting (the risk of selecting a comple...
In this paper we propose and implement a modularization framework for Jason that enables developers to decompose agents into separate code units called modules, and by fulfilling an agent-module design contract to conceive agents behaviour design into different levels of abstraction – from a software engineering perspective. Thus, we promote code r...
This paper proposes a novel Distributed Data Mining (DDM) approach based on the Agents and Artifacts paradigm, as implemented in CArtAgO [9], where artifacts encapsulate data mining tools, inherited from Weka, that agents can use while engaged in collaborative, distributed learning processes. Target hypothesis are currently constrained to decision...
This chapter argues that Lisp has a duality: it is a tool for doing Artificial Intelligence; but also is a subject of this discipline. Then the arguments are illustrated with the use of this programming language in the book Pericia Artificial, an introduction to the Expert Systems (Universidad Veracruzana, 1996).
This paper introduces an operational semantics for defining Intentional Learning on Jason, the well known Java-based implementation of AgentSpeak(L). This semantics enables Jason to define agents capable of learning the reasons for adopting intentions based on their own experience. In this work, the use of the term Intentional Learning is strictly...
Breast cancer is one of the leading causes of death among women worldwide. There are a number of techniques used for diagnosing this disease: mammography, ultrasound, and biopsy, among others. Each of these has well-known advantages and disadvantages. A relatively new method, based on the temperature a tumor may produce, has recently been explored:...
Metropolitan mobility models, mainly based on the massive use of the car instead of the public transportation, will soon become unsustainable unless there is a change of citizens’ minds and transport policies. The main challenge related to urban mobility is that of getting free-flowing greener cities, which are provided with a smarter and accessibl...
Intentional reasoning is also logical reasoning. Since it is a dynamic process that involves reasoning from beliefs, goals and time, it requires both a temporal semantics and a non-monotonic behavior. In this work we propose a model of intentional reasoning as a case of non- monotonic reasoning. We also show the consistency and soundness of the sys...
The mobility models followed within metropolitan areas, mainly based on the massive use of the car instead of the public transportation, will soon become unsustainable unless there is a change of citizens’ minds and transport policies. The main challenge related to urban mobility is that of getting free-flowing greener cities, which are provided wi...
This paper presents JILDT (Jason Induction of Logical Decision Trees), a library that defines two learning agent classes for
Jason, the well known java-based implementation of AgentSpeak(L). Agents defined as instances of JILDT can learn about their reasons to adopt intentions performing first-order induction
of decision trees. A set of plans and a...
By recovering some insights from a philosophical analysis about intentions we focus on intentions as plans writ large. Using this assumption we suggest proof of how an existing implementation of BDI learning procedures works as an intention revision mechanism as suggested in an abstract specification. Finally, we translate the abstract postulates i...
Resumen Los avances que se han hecho sobre la propuesta filosófica de Bratman para representar intenciones son sumamente complejos e interesantes; sin embargo, aún existen detalles de su análisis que no han sido explorados completamente. Siguiendo este estudio fundacional sobre intenciones comenzamos a formalizar algunos de los resultados de dicha...
A review of the spanish edition of The Sciences of Artificial (Simon, 2006), translated by Pablo Noriega. Komputer Sapiens is published by the Mexican Society of Artificial Intelligence
This work introduces CTL AgentSpeak(L), a logic to specify and verify expected properties of rational agents implemented in the well-known agent oriented programming language AgentSpeak(L). Our approach is closely related to the BDICTL multi-modal logic, used to reason about agents in terms of their beliefs (B), desires (D), intentions (I), and the...
This work is about the commitment strategies used by rational agents programmed in AgentSpeak(L) and the relationship between single-minded commitment and intentional learning. Although agent oriented languages were proposed
to reduce the gap between theory and practice of Multi-Agent Systems, it has been difficult to prove BDI properties of the
ag...
Sapient agents have been characterized as systems that learn their cognitive state and capabilities through experience, considering
social environments and interactions with other agents or humans. The BDI (belief, desire, intention) model of cognitive agency
offers philosophical grounds on intentionality and practical reasoning, as well as an eleg...
Sapient agents have been characterized as a subclass of intelligent agents capable of “insight” and “sound judgment.” Although
several engineering issues have been established to characterize sapient agents, biological referents also seem necessary
to understand the cognitive functionality of such systems. Small-world and scale-free networks, the s...
Knowledge Discovery in Databases (KDD) is the process of nding valid, novel, useful and understandable patterns in data, to ver- ify hypothesis of the user or to describe/predict the future behavior of some event. The KDD process involves diverse techniques provided by tools like the Waikato Environment for Knowledge Analysis (WEKA), but usually wi...
This work deals with the problem of intentional learning in a multi-agent system (MAS). Smile (sound multi-agent incremental learning), a collaborative learning protocol which shows interesting results in the distributed learning of well known complex boolean formulae, is adopted here by a MAS of BDI agents to update their practical reasons while k...
Small World and Scale Free network properties characterize many real complex phenomena. We assume that low level connectivity with such topological properties, e.g., anatomical or functional connectivity in brains, is compulsory to achieve high level cognitive functionality, as language. The study of these network properties provides tools to appro...
Any attempt to include learning competences in agent technologies, involves the problem of integrating such competences in the rationality of the agents. This work discusses the design of learning intentional agents in a multiagent system (MAS). Learning intentionally means that these agents learn to adopt their intentions under practical reasoning...
The main goal of this work is to introduce plasticity in the autonomous adaptive behavior displayed by agents based on behavior networks. In order to do this, reinforcement learning (RL) is used to learn about the preferences for satisfying selected consummatory behaviors in the compe- tence of the agent. Learnt preferences are then considered by t...
This paper deals with the issue of learning in multi-agent systems (MAS). Particularly, we are interested in BDI (Belief,
Desire, Intention) agents. Despite the relevance of the BDI model of rational agency, little work has been done to deal with
its two main limitations: i)The lack of learning competences; and ii)The lack of explicit multi-agent f...
A method to induce Bayesian networks from data to overcome some limitations of other learning algorithms is proposed. One of the main features of this method is a metric to evaluate Bayesian networks combining different quality criteria. A fuzzy system is proposed to enable the combination of different quality metrics. In this fuzzy system a metric...
Despite the relevance of the belief-desire-intention (BDI) model of rational agency, little work has been done to deal with its two main limitations: the lack of learning competences and explicit multi-agent functionality. Our work deals with the problem of designing BDI learning agents situated in a multi-agent system (MAS). From the MAS learning...
A method to induce bayesian networks from data to over-come some limitations of other learning algorithms is proposed. One of the main features of this method is a metric to evaluate bayesian networks combining different quality criteria. A fuzzy system is proposed to enable the combination of different quality metrics. In this fuzzy system a metri...
The main purpose of this work is to explore the application of Memory Based Reasoning (MBR) to adaptive behavior in agents. We discuss the design of an interface agent for e-mail management assistance as a prototype for the experimental assesment of an MBR learning algorithm performance. Results are discussed and a brief summary of conclusions foll...
Questions
Question (1)
I wonder if someone has extended the J48 classifier builder implemented in Weka to get an incremental version of the algorithm? For some classifiers, naive bayes among them, Weka provides a batch and and incremental version, but I think this is not the case for J48. I also wonder if rebuilding the classifier with each new training example would be an alternative to the incremental version? I'd appreciate your help in these issues.
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