The Impact of Implied Constraints on MaxSAT B2B Instances

The B2B scheduling optimization problem consists of finding a schedule of a set of meetings between pairs of participants, minimizing their number of idle time periods. Recent works have shown that SAT-based approaches are state-of-the-art on this problem. One interesting feature of such approaches is the use of implied constraints. In this work, we provide an experimental setting to study the impact of using these implied constraints in MaxSAT B2B instances. To this purpose and due to the reduced number of existing real-world B2B instances, we propose a random B2B instance generation model, which reproduces certain features of these problems. In our experimental analysis, we show that the impact of using some implied constraints in the MaxSAT encodings depends on the characteristics of the problem, and we also analyze the benefits of combining them. Finally, we give some insights on how a MaxSAT solver is able to exploit these implied constraints.Spanish Government RTI2018-095609-B-I00French National Research Agency (ANR) ANR-19-CHIA-0013-01Juan de la Cierva program - MCIN IJC2019040489-IAE

Characterizing the Temperature of SAT Formulas

The remarkable advances in SAT solving achieved in the last years have allowed to use this technology to solve many real-world applications, such as planning, formal verification and cryptography, among others. Interestingly, these industrial SAT problems are commonly believed to be easier than classical random SAT formulas, but estimating their actual hardness is still a very challenging question, which in some cases even requires to solve them. In this context, realistic pseudo-industrial random SAT generators have emerged with the aim of reproducing the main features of these application problems to better understand the success of those SAT solving techniques on them. In this work, we present a model to estimate the temperature of real-world SAT instances. This temperature represents the degree of distortion into the expected structure of the formula, from highly structured benchmarks (more similar to real-world SAT instances) to the complete absence of structure (observed in the classical random SAT model). Our solution is based on the popularity–similarity random model for SAT, which has been recently presented to reproduce two crucial features of application SAT benchmarks: scale-free and community structures. This model is able to control the hardness of the generated formula by introducing some randomizations in the expected structure. Using our regression model, we observe that the estimated temperature of the applications benchmarks used in the last SAT Competitions correlates to their hardness in most of the cases.Juan de la Cierva program, fellowship IJC2019-040489-I, funded by MCIN and AE

Analyzing the extremization of opinions in a general framework of bounded confidence and repulsion

In the bounded confidence framework, agents’ opinions evolve as a result of interactions with other agents having similar opinions. Thus, consensus or fragmentation of opinions can be reached, but not extremization (the evolution of opinions towards an extreme value). In contrast, when repulsion mechanisms are at work, agents with distant opinions interact and repel each other, leading to extremization. This work proposes a general opinion dynamics framework of bounded confidence and repulsion, which includes social network interactions and agent-independent time-varying rationality. We extensively analyze the performance of our model to show that the degree of extremization among a population can be controlled by the repulsion rule, and social networks promote extreme opinions. Agent-based rationality and time-varying adaptation also bear a strong impact on opinion dynamics. The high accuracy of our model is determined in a real-world social network well referenced in the literature, the Zachary Karate Club (with a known ground truth). Finally, we use our model to analyze the extremization of opinions in a real-world scenario, in Spain: a marketing action for the Netflix series “Narcos”

Community Structure in Industrial SAT Instances

Modern SAT solvers have experienced a remarkable progress on solving industrial instances. Most of the techniques have been developed after an intensive experimental process. It is believed that these techniques exploit the underlying structure of industrial instances. However, there are few works trying to exactly characterize the main features of this structure. The research community on complex networks has developed techniques of analysis and algorithms to study real-world graphs that can be used by the SAT community. Recently, there have been some attempts to analyze the structure of industrial SAT instances in terms of complex networks, with the aim of explaining the success of SAT solving techniques, and possibly improving them. In this paper, inspired by the results on complex networks, we study the community structure, or modularity, of industrial SAT instances. In a graph with clear community structure, or high modularity, we can find a partition of its nodes into communities such that most edges connect variables of the same community. In our analysis, we represent SAT instances as graphs, and we show that most application benchmarks are characterized by a high modularity. On the contrary, random SAT instances are closer to the classical Erd\"os-R\'enyi random graph model, where no structure can be observed. We also analyze how this structure evolves by the effects of the execution of a CDCL SAT solver. In particular, we use the community structure to detect that new clauses learned by the solver during the search contribute to destroy the original structure of the formula. This is, learned clauses tend to contain variables of distinct communities

An Integrative Decision-Making Mechanism for Consumers’ Brand Selection using 2-Tuple Fuzzy Linguistic Perceptions and Decision Heuristics

Consumers perform decision-making (DM) processes to select their preferred brands during their entire consumer journeys. These DM processes are based on the multiple perceptions they have about the products available in the market they are aware of. These consumers usually perform different DM strategies and employ diverse heuristics depending on the nature of the purchase, ranging from more pure optimal choices to faster decisions. Therefore, the design of realistic DM approaches for modeling these consumer behaviors requires a good representation of consumer perceptions and a reliable process for integrating their corresponding heuristics. In this work, we use fuzzy linguistic information to represent consumer perceptions and propose four consumer DM heuristics to model the qualitative linguistic information for the consumer buying decision. In particular, we use 2-tuple fuzzy linguistic variables, which is a substantially more natural and realistic representation without falling in a loss of information. The set of selected heuristics differ in the degree of involvement the consumers give to their decisions. Additionally, we propose a heuristic selection mechanism to integrate the four heuristics in a single DM procedure by using a regulation parameter. Our experimental analysis shows that the combination of these heuristics in a portfolio manner improves the performance of our model with a realistic representation of consumer perceptions. The model’s outcome matches the expected behavior of the consumers in several real market scenarios

Agent-mediated shared conceptualizations in tagging services

Some of the most remarkable innovative technologies from the Web 2.0 are the collaborative tagging systems. They allow the use of folksonomies as a useful structure for a number of tasks in the social web, such as navigation and knowledge organization. One of the main deficiencies comes from the tagging behaviour of different users which causes semantic heterogeneity in tagging. As a consequence a user cannot benefit from the adequate tagging of others. In order to solve the problem, an agent-based reconciliation knowledge system, based on Formal Concept Analysis, is applied to facilitate the semantic interoperability between personomies. This article describes experiments that focus on conceptual structures produced by the system when it is applied to a collaborative tagging service, Delicious. Results will show the prevalence of shared tags in the sharing of common resources in the reconciliation process.Ministerio de Ciencia e Innovación TIN2009-09492Ministerio de Ciencia e Innovación TIN2010-20967-C04-0

Conceptual-based reasoning in mobile web 2.0 by means multiagent systems - knowledge engineering notes

Increasingly, users connect to the Internet by mobile devices and they are generating massive content through them. The lead-off projects in Mobile Web 2.0 offer the opportunity to add semantics in order to obtain structured knowledge. In this paper, we present specific challenges for tagging reasoning, into the SinNet project. SinNet is based on user generated content (UGC) by mobile devices, as well as how to solve them by means of combining multi-agent systems and formal concepts analysis.Ministerio de Ciencia e Innovación TIN2009-0949

Herramientas de gamificación en las enseñanzas y evaluación de las Técnicas de los Sistemas Inteligentes

Memoria del proyecto de innovación y buenas prácticas docentes titulado "Herramientas de gamificación en las enseñanzas y evaluación de las Técnicas de los Sistemas Inteligentes

Automated Completion of Partial Configurations as a Diagnosis Task Using FastDiag to Improve Performance

The completion of partial configurations might represent an expensive computational task. Existing solutions, such as those which use modern constraint satisfaction solvers, perform a complete search, making them unsuitable on large-scale configurations. In this work, we propose an approach to define the completion of a partial configuration like a diagnosis task to solve it by applying the FastDiag algorithm, an efficient solution for preferred minimal diagnosis (updates) in the analyzed partial configuration. We evaluate our proposed method in the completion of partial configurations of random medium and large-size features models and the completion of partial configurations of a feature model of an adapted version of the Ubuntu Xenial OS. Our experimental analysis shows remarkable improvements in our solution regarding the use of classical CSP-based approaches for the same tasks.Ministerio de Ciencia, Innovación y Universidades RTI2018-101204-B-C22Agencia Estatal de Investigación TIN2017-90644-RED

Herramientas de gamificación para la enseñanza de técnicas de búsqueda heurística en entornos dinámicos

La Búsqueda Heurística (BH) es uno de los campos clásicos y más estudiados de la Inteligencia Artificial, y una rama troncal de los estudios de Ingeniería Informática. BH consiste en encontrar la ruta óptima hacia un estado objetivo, dadas unas condiciones iniciales, y usando una función heurística que guía dicha búsqueda a través del espacio de posibles estados. Usualmente, este es un problema computacionalmente duro de resolver y, por tanto, de gran interés tanto para estudios académicos como industriales. En este trabajo, se describe una metodología para el aprendizaje de búsqueda heurística basada en gamificación. En concreto, esta estrategia usa el entorno de desarrollo de controladores de videojuegos GVG-AI, y ha sido aplicada en los estudios de Grado de Ingeniería Informática de la Universidad de Granada durante los cursos 2018-19, 2019-20 y 2020-21. Esta metodología, basada en el trabajo individual y el aprendizaje a través del juego, ha permitido adaptar la docencia de esta materia a la modalidad virtual sin apenas cambios en la organización docente. Además no se observan diferencias significativas en las calificaciones de ambas modalidades docentes, sugiriendo la robustez de la metodología propuesta en escenarios extraordinarios de docencia virtual.Heuristic Search (HS) is one of the classical and most studied fields in Artificial Intelligence, and a core subject of the degree in Computer Science. HS consists of finding the optimal path to a target state, given some initial conditions, and using a heuristic function that guides the search trough the space of possible states. Usually, this is a computationally hard problem, and thus of great interest for both academical and industrial purposes. In this work, it is described a new methodology to learn heuristic search based on gamification. In particular, this strategy uses GVG-AI, a video games controller development framework, and it has been applied in the degrees of Computer Science of the University of Granada (Spain) during the years 2018-19, 2019-20, and 2020-21. This methodology, based on the individual work and the learning through the game, has allowed to adapt the teaching of this subject to online courses with minimal changes in the teaching organization. Moreover, there is no significant differences in the grades of both teaching methods, suggesting the robustness of the proposed methodology in extraordinary scenarios of online teaching