Search

Gamification is well-known for increasing users’ motivation. This is especially important for users of web and mobile applications. However, most of the current approaches do not consider the characteristics and needs of each user. In online small communities, where the number of users and interactions is quite limited, motivation is even more important to avoid any risk of standstill and personalization may be helpful. In this paper, we propose a general framework for adaptive gamification for small online communities. The framework considers specific characteristics of users and their interactions to provide which specific game mechanics may be the most suitable for each user. As a key component of this framework, different algorithms can be used for adaptive gamification. Therefore, we tested the performance of different algorithms under simulated conditions to assess the adequateness for small online communities.

[EN] Product bundling is a strategy conducted by marketing decisionmakers to combine items or services for targeted sales in today¿s competitive business environment. Targeted sales can be in various forms, like increasing the likelihood of a purchase, promoting some products among a specific customer segment, or improving user experience. In this study, we propose an evolutionary product bundle generation strategy that is based on the NSGA-II algorithm. The proposed approach is designed as a multi-objective optimization procedure where the objectives are designed in terms of desired bundle feature distributions. The designed genetic algorithm is flexible and allows decision-makers to specify objectives such as price, season, item similarity and association with bundle size constraints. In the experiments,we showthat the evolutionary approach enables us to generate Pareto solutions compared to the initial population.

Cities face many challenges concerning their management, security, transportation, public health, the distribution of resources, sustainability, energy efficiency, and many more. As cities grow larger, it is only expected that these problems become more acute and, therefore, they will need solutions to tackle or smooth these problems. With the rise of technologies such as artificial intelligence and the increasing number of social applications that allow citizens to participate in the urban digital ecosystem, researchers and policymakers have seen an opportunity in the application of these technologies to tackle urban challenges. In this editorial article, we review some relevant contributions to this special issue to social and intelligent applications for future cities., (undefined)

[EN] Bullying is defined as the act of repeatedly and intentionally causing harm to another person who feels helpless (i.e., the victim) against the perpetrator or group of perpetrators. These series of acts of physical and/or psychological violence have been reported to cause a negative impact on the victim on several aspects of his/her well-being and daily life. Recently, multidisciplinary research teams have noticed the potential of ICT tools as catalysts for fighting bullying in schools. A range of ICT-based tools such as elearning systems, intelligent tutoring systems, gamified applications, analytics, or even artificial intelligence have been proposed as technological assets against bullying. In this paper, we identify, categorize, and analyse the use of these ICT tools against bullying under a wide range of criteria such as their role (e.g., prevention, mitigation, detection, etc.), the potential investment that is required, the target age of users, and strengths and weaknesses of each tool. Then, we discuss on potential areas of expansion for the use of ICT tools in the fight bullying, and we identify new potential areas of research., This work has been partially funded by the Generalitat Valenciana (GV/2019/012).

[EN] In the last few years, bullying and has emerged as one of the main issues to be addressed by the Educational community. The problem of bullying in primary and secondary schools is particularly important, as students are in a sensitive age and consequences may negatively affect their adult lives too. This problem has become an active are for researchers of different disciplines. Specifically, different studies have focused this problem by means of ICT tools in order to fight against bullying in schools. According to this, in the literature we can find a wide range of ICT-based tools such as e-learning systems, intelligent tutoring systems, gamified applications, analytics, or even artificial intelligence have been proposed as technological assets against bullying. Most of these proposals focus on bullying prevention by means of educational applications. However, there is a need of ICT tools that support teachers and other educational staff in the fight against bullying, both at the detection and mitigation stage. In this paper, we propose a novel ICT tool focused on bullying mitigation and detection. This tool is currently composed by two different components. One the one hand, this tool integrates several validated tests in order to determine the potential risk of an individual for becoming a victim or a bully. On the other hand, it also provides a notification mechanism for anonymously reporting situations related to bullying. By considering both components, the tool is able to determine those actions that should be considered by teachers and educational staff in order to mitigate potential problems and prevent future situations related to bullying., This work has been partially funded by the Generalitat Valenciana (GV/2019/012).

Nowadays, transportation companies look for smart solutions in order to improve quality of their services. Accordingly, an intercity bus company in Istanbul aims to improve their shuttle schedules. This paper proposes revising scheduling of the shuttles based on their estimated travel time in the given timeline. Since travel time varies depending on the date of travel, weather, distance, we present a prediction model using both travel history and additional information such as distance, holiday, and weather. The results showed that Random Forest algorithm outperformed other methods and adding additional features increased its accuracy rate.

In this article, we introduce a new paradigm to achieve Pareto optimality in group decision-making processes: bottom-up approaches to Pareto optimality. It is based on the idea that, while resolving a conflict in a group, individuals may trust some members more than others; thus, they may be willing to cooperate and share more information with those members. Therefore, one can divide the group into subgroups where more cooperative mechanisms can be formed to reach Pareto optimal outcomes. This is the first work that studies such use of a bottom-up approach to achieve Pareto optimality in conflict resolution in groups. First, we prove that an outcome that is Pareto optimal for subgroups is also Pareto optimal for the group as a whole. Then, we empirically analyze the appropriate conditions and achievable performance when applying bottom-up approaches under a wide variety of scenarios based on real-life datasets. The results show that bottom-up approaches are a viable mechanism to achieve Pareto optimality with applications to group decision-making, negotiation teams, and decision making in open environments.

[EN] The problem of allocating students to supervisors for the development of a personal project or a dissertation is a crucial activity in the higher education environment, as it enables students to get feedback on their work from an expert and improve their personal, academic, and professional abilities. In this article, we propose a multi-objective and near Pareto optimal genetic algorithm for the allocation of students to supervisors. The allocation takes into consideration the students and supervisors¿ preferences on research/project topics, the lower and upper supervision quotas of supervisors, as well as the workload balance amongst supervisors. We introduce novel mutation and crossover operators for the student¿supervisor allocation problem. The experiments carried out show that the components of the genetic algorithm are more apt for the problem than classic components, and that the genetic algorithm is capable of producing allocations that are near Pareto optimal in a reasonable time., This work is partially supported by funds of the Faculty of Engineering and Computing at Coventry University, United Kingdom, and funds from EU ICT-20-2015 Project SlideWiki granted by the European Commission.

In the last few years there has been a growing interest in the use of artificial intelligence to improve different areas of education such as student team formation, learning analytics, intelligent tutoring systems, or the recommendation of learning resources. This paper presents a genetic algorithm that aims to improve the allocation of students to supervisors while taking both the students’ and supervisors’ preferences with regards to research topics, and by providing a balanced allocation for supervisors’ workload. A Pareto optimal genetic algorithm has been designed and tested for the resolution of this problem.

Urban transportation involves a number of common problems: air and acoustic pollution, traffic jams, and so forth. This has become an important topic of study due to the interest in solving these issues in different areas (economical, social, ecological, etc.). Nowadays, one of the most popular urban transport systems are the shared vehicles systems. Among these systems there are the shared bicycle systems which have an special interest due to its characteristics. While solving some of the problems mentioned above, these systems also arise new problems such as the distribution of bicycles over time and space. Traditional approaches rely on the service provider to balancing the system, thus generating extra costs. Our proposal consists on an multi-agent system that includes user actions as a balancing mechanism, taking advantage of their trips to optimize the overall balance of the system. With this goal in mind the user is persuaded to deviate slightly from its origin/destination by providing appropriate arguments and incentives. This article presents the prediction module that will enable us to create such persuasive system. This module allow us to predict the demand for bicycles in the stations, forecasting the number of available parking spots (or available bikes). With this information the multi-agent system is capable of scoring alternative stations and routes and making offers to balance bikes across the stations. In order to achieve this, the most proper offers for the user will be predicted and used to persuade her.

Due to copyright restrictions, the access to the full text of this article is only available via subscription. Frequency opponent modeling is one of the most widely used opponent modeling techniques in automated negotiation, due to its simplicity and its good performance. In fact, it outperforms even more complex mechanisms like Bayesian models. Nevertheless, the classical frequency model does not come without its own assumptions, some of which may not always hold in many realistic settings. This paper advances the state of the art in opponent modeling in automated negotiation by introducing a novel frequency opponent modeling mechanism, which soothes some of the assumptions introduced by classical frequency approaches. The experiments show that our proposed approach outperforms the classic frequency model in terms of evaluation of the outcome space, estimation of the Pareto frontier, and accuracy of both issue value evaluation estimation and issue weight estimation.

Urban transportation systems have received a special interest in the last few years due to the necessity to reduce congestion, air pollution and acoustic contamination in today’s cities. Bike sharing systems have been proposed as an interesting solution to deal with these problems. Nevertheless, shared vehicle schemes also arise problems that must be addressed such as the vehicle distribution along time and across space in the city. Differently to classic approaches, we propose the architecture for a muti-agent system that tries to improve the efficiency of bike sharing systems by introducing user-driven balancing in the loop. The rationale is that of persuading users to slightly deviate from their origins/destinations by providing appropriate arguments and incentives, while optimizing the overall balance of the system. In this paper we present two of the proposed system’s modules. The first will allow us to predict bike demand in different stations. The second will score stations and alternative routes. This modules will be used to predict the most appropriate offers for users and try to persuade them.

Ambient Intelligence aims to offer personalized services and easier ways of interaction between people and systems. Since several users and systems may coexist in these environments, it is quite possible that entities with opposing preferences need to cooperate to reach their respective goals. Automated negotiation is pointed as one of the mechanisms that may provide a solution to this kind of problems. In this article, a multi-issue bilateral bargaining model for Ambient Intelligence domains is presented where it is assumed that agents have computational bounded resources and do not know their opponents' preferences. The main goal of this work is to provide negotiation models that obtain efficient agreements while maintaining the computational cost low. A niching genetic algorithm is used before the negotiation process to sample one's own utility function (self-sampling). During the negotiation process, genetic operators are applied over the opponent's and one's own offers in order to sample new offers that are interesting for both parties. Results show that the proposed model is capable of outperforming similarity heuristics which only sample before the negotiation process and of obtaining similar results to similarity heuristics which have access to all of the possible offers. (C) 2010 Elsevier Inc. All rights reserved., This work is supported by TIN2008-04446, PROMETEO/2008/051, TIN2009-13839-C03-01, CSD2007-00022 of the Spanish government, and FPU Grant AP2008-00600 awarded to V.Sanchez-Anguix.

Traditionally, researchers in decision making have focused on attempting to reach Pareto Optimality using horizontal approaches, where optimality is calculated taking into account every participant at the same time. Sometimes, this may prove to be a difficult task (e.g., conflict, mistrust, no information sharing, etc.). In this paper, we explore the possibility of achieving Pareto Optimal outcomes in a group by using a bottom-up approach: discovering Pareto optimal outcomes by interacting in subgroups. We analytically show that Pareto optimal outcomes in a subgroup are also Pareto optimal in a supergroup of those agents in the case of strict, transitive, and complete preferences. Then, we empirically analyze the prospective usability and practicality of bottom-up approaches in a variety of decision making domains., Comment: 2nd Workshop on Conflict Resolution in Decision Making (COREDEMA@ECAI2016)

[EN] Nowadays, there is increasing interest in the development of teamwork skills in the educational context. This growing interest is motivated by its pedagogical effectiveness and the fact that, in labour contexts, enterprises organise their employees in teams to carry out complex projects. Despite its crucial importance in the classroom and industry, there is a lack of support for the team formation process. Not only do many factors influence team performance, but the problem becomes exponentially costly if teams are to be optimised. In this article, we propose a tool whose aim it is to cover such a gap. It combines artificial intelligence techniques such as coalition structure generation, Bayesian learning, and Belbin s role theory to facilitate the generation of working groups in an educational context. This tool improves current state of the art proposals in three ways: i) it takes into account the feedback of other teammates in order to establish the most predominant role of a student instead of self-perception questionnaires; ii) it handles uncertainty with regard to each student s predominant team role; iii) it is iterative since it considers information from several interactions in order to improve the estimation of role assignments. We tested the performance of the proposed tool in an experiment involving students that took part in three different team activities. The experiments suggest that the proposed tool is able to improve different teamwork aspects such as team dynamics and student satisfaction., This work is supported by the following projects: TIN2014-55206-R, TIN2012-36586-C03-01, PROMETEOII/2013/019, TIN2015-65515-C4-1-R, H2020-ICT-2015-688095.

An agent-based negotiation team is a group of interdependent agents that join together as a single negotiation party due to their shared interests in the negotiation at hand. The reasons to employ an agent-based negotiation team may vary: (i) more computation and parallelization capabilities, (ii) unite agents with different expertise and skills whose joint work makes it possible to tackle complex negotiation domains, (iii) the necessity to represent different stakeholders or different preferences in the same party (e.g., organizations, countries, and married couple). The topic of agent-based negotiation teams has been recently introduced in multi-agent research. Therefore, it is necessary to identify good practices, challenges, and related research that may help in advancing the state-of-the-art in agent-based negotiation teams. For that reason, in this article we review the tasks to be carried out by agent-based negotiation teams. Each task is analyzed and related with current advances in different research areas. The analysis aims to identify special challenges that may arise due to the particularities of agent-based negotiation teams., Comment: Engineering Applications of Artificial Intelligence, 2013

One of the most important problems faced by teachers is grouping students into proper teams. The task is complex, as many technical and interpersonal factors could affect team dynamics, with no clear indication of which factors may be more relevant. Not only the problem is conceptually complex, but its computational complexity is also exponential, which precludes teachers from optimally applying strategies by hand. The tool presented in this paper aims to cover both gaps: first, it provides a range of grouping strategies for testing, and second, it provides artificial intelligence mechanisms that in practice tone down the computational cost of the problem.

Previous work has shown that the prediction of meteorological conditions through methods based on artificial intelligence can get satisfactory results. Forecasts of meteorological time series can help decision-making processes carried out by organizations responsible of disaster prevention. We introduce an architecture based on Deep Learning for the prediction of the accumulated daily precipitation for the next day. More specifically, it includes an autoencoder for reducing and capturing non-linear relationships between attributes, and a multilayer perceptron for the prediction task. This architecture is compared with other previous proposals and it demonstrates an improvement on the ability to predict the accumulated daily precipitation for the next day.

A negotiation team is a set of agents with common and possibly also conflicting preferences that forms one of the parties of a negotiation. A negotiation team is involved in two decision making processes simultaneously, a negotiation with the opponents, and an intra-team process to decide on the moves to make in the negotiation. This article focuses on negotiation team decision making for circumstances that require unanimity of team decisions. Existing agent-based approaches only guarantee unanimity in teams negotiating in domains exclusively composed of predictable and compatible issues. This article presents a model for negotiation teams that guarantees unanimous team decisions in domains consisting of predictable and compatible, and alsounpredictable issues. Moreover, the article explores the influence of using opponent, and team member models in the proposing strategies that team members use. Experimental results show that the team benefits if team members employ Bayesian learning to model their teammates’ preferences. 2014 Elsevier B.V. All rights reserved., This research is partially supported by TIN2012-36586-C03-01 of the Spanish government and PROMETEOII/2013/019 of Generalitat Valenciana. Other part of this research is supported by the Dutch Technology Foundation STW, applied science division of NWO and the Technology Program of the Ministry of Economic Affairs; the Pocket Negotiator Project with Grant No. VICI-Project 08075.

Under some circumstances, a group of individuals may need to negotiate together as a negotiation team against another party. Unlike bilateral negotiation between two individuals, this type of negotiations entails to adopt an intra-team strategy for negotiation teams in order to make team decisions and accordingly negotiate with the opponent. It is crucial to be able to negotiate successfully with heterogeneous opponents since opponents' negotiation strategy and behavior may vary in an open environment. While one opponent might collaborate and concede over time, another may not be inclined to concede. This paper analyzes the performance of recently proposed intra-team strategies for negotiation teams against different categories of opponents: competitors, matchers, and conceders. Furthermore, it provides an extension of the negotiation tool Genius for negotiation teams in bilateral settings. Consequently, this work facilitates research in negotiation teams.

Teamwork is a critical competence in the higher education area, and it has become a critical task in educational and management environments. Unfortunately, looking for optimal or near optimal teams is a costly task for humans due to the exponential number of outcomes. In this paper, we present a web application that facilitates the task of automatic team generation of near optimal teams based on collective intelligence, coalition structure generation, and Bayesian learning. This tool has been used in real classroom scenario and the data collected from the experience has been used as input for synthetic simulations. The experiments show that the tool is able of converging towards the optimal solution (team formation) as long as students do not have great difficulties evaluating others.

[EN] This paper addresses energy-aware room allocation management where the system aims to satisfy individuals¿ needs as much as possible while concerning total energy consumption in a building. In the problem, there are a several rooms having varied settings resulting in different energy consumption. The main objective of the system is not only finding the right allocations for user¿s need, but also minimizing energy consumption. However, the users of the system may have conflicting preferences over the rooms to be allocated for them. This paper pursues how the system can increase user satisfaction while achieving its goals. For that purpose, an adaptation of the mediated single text negotiation model is introduced. The proposal seeks to guarantee an upper bound on energy consumption by pruning the negotiation space via a genetic algorithm, and to take advantage of the negotiation for increasing user satisfaction. Experiments suggest that the adaptations improve the performance, This work is supported by TIN2012-36586-C03-01, TIN2011-27652-C03-01, and TIN2009-13839-C03-01 projects of the Spanish government, the FPU grant AP2008-00600 awarded to V¿¿ctor S¿anchez-Anguix, and the New Governance Models for Next Generation Infrastructures project with NGI grant number 04.17. We would also like to thank anonymous reviewers for their valuable feedback.

[EN] In this article we study the impact of the negotiation environment on the performance of several intra-team strategies (team dynamics) for agent-based negotiation teams that negotiate with an opponent. An agent-based negotiation team is a group of agents that joins together as a party because they share common interests in the negotiation at hand. It is experimentally shown how negotiation environment conditions like the deadline of both parties, the concession speed of the opponent, similarity among team members, and team size affect performance metrics like the minimum utility of team members, the average utility of team members, and the number of negotiation rounds. Our goal is identifying which intra-team strategies work better in different environmental conditions in order to provide useful knowledge for team members to select appropriate intra-team strategies according to environmental conditions., This work is supported by TIN2011-27652-C03-01, TIN2009-13839-C03-01, CSD2007-00022 of the Spanish Government, and FPU Grant AP2008-00600 awarded to Victor Sanchez-Anguix. We would also like to thank anonymous reviewers and assistants of AAMAS 2011 who helped us to improve our previous work, making this present work possible.

Teamwork is now a critical competence in the higher education area, and it has become a critical task in educational and management environments. Unfortunately, looking for optimal or near optimal teams is a costly task for humans due to the exponential number of outcomes. For this reason, in this paper we present a computer-aided policy that facilitates the automatic generation of near optimal teams based on collective intelligence, coalition structure generation, and Bayesian learning. We carried out simulations in hypothetic classroom scenarios that show that the policy is capable of converging towards the optimal solution as long as students do not have great difficulties evaluating others.

Los sistemas multi-agente (SMA) son sistemas distribuidos donde entidades autónomas llamadasagentes, ya sean humanos o software, persiguen sus propios objetivos. El paradigma de SMA hasido propuesto como la aproximación de modelo apropiada para aplicaciones como el comercioelectrónico, los sistemas multi-robot, aplicaciones de seguridad, etc. En la comunidad de SMA, lavisión de sistemas multi-agente abiertos, donde agentes heterogéneos pueden entrar y salir delsistema dinámicamente, ha cobrado fuerza como paradigma de modelado debido a su relaciónconceptual con tecnologías como la Web, la computación grid, y las organizaciones virtuales.Debido a la heterogeneidad de los agentes, y al hecho de dirigirse por sus propios objetivos, elconflicto es un fenómeno candidato a aparecer en los sistemas multi-agente.En los últimos años, el término tecnologías del acuerdo ha sido usado para referirse a todos aquellosmecanismos que, directa o indirectamente, promueven la resolución de conflictos en sistemascomputacionales como los sistemas multi-agente. Entre las tecnologías del acuerdo, la negociaciónautomática ha sido propuesta como uno de los mecanismos clave en la resolución de conflictosdebido a su uso análogo en la resolución de conflictos entre humanos. La negociación automáticaconsiste en el intercambio automático de propuestas llevado a cabo por agentes software en nombrede sus usuarios. El objetivo final es conseguir un acuerdo con todas las partes involucradas.Pese a haber sido estudiada por la Inteligencia Artificial durante años, distintos problemas todavíano han sido resueltos por la comunidad científica todavía. El principal objetivo de esta tesis esproponer modelos de negociación para escenarios complejos donde la complejidad deriva de (1) laslimitaciones computacionales o (ii) la necesidad de representar las preferencias de múltiplesindividuos. En la primera parte de esta tesis proponemos un modelo de negociación bilateral para elproblema de, Sánchez Anguix, V. (2013). Complex negotiations in multi-agent systems [Tesis doctoral no publicada]. Universitat Politècnica de València. doi:10.4995/Thesis/10251/21570., Palancia

It has been documented in the social sciences that cultural factors affect how people negotiate and behave in negotiations. Despite the importance of culture in the business world and politics, there is a lack of computational models that help to analyze how cultural factors affect negotiation. Moreover, while many negotiations take place between teams, there is a dearth of computational models for team negotiations. In this paper we present the first attempt to provide a computational model which takes into account cultural factors in a team negotiation setting. The model considers how two important cultural dimensions, power distance and individualism/collectivism, affect team negotiation dynamics and negotiation outcomes. We conducted experiments in high/low intra-team conflict scenarios. The results are compatible with social sciences findings from team decision making.

[EN] In this article, an agent-based negotiation model for negotiation teams that negotiate a deal with an opponent is presented. Agent-based negotiation teams are groups of agents that join together as a single negotiation party because they share an interest that is related to the negotiation process. The model relies on a trusted mediator that coordinates and helps team members in the decisions that they have to take during the negotiation process: which offer is sent to the opponent, and whether the offers received from the opponent are accepted. The main strength of the proposed negotiation model is the fact that it guarantees unanimity within team decisions since decisions report a utility to team members that is greater than or equal to their aspiration levels at each negotiation round. This work analyzes how unanimous decisions are taken within the team and the robustness of the model against different types of manipulations. An empirical evaluation is also performed to study the impact of the different parameters of the model., This work is supported by TIN2008-04446, PROMETEO/2008/051, TIN2009-13839-C03-01, CSD2007-00022 of the Spanish government, and FPU Grant AP2008-00600 awarded to Victor Sanchez-Anguix. This paper was recommended by Associate Editor X. Wang.

Agent-based negotiation teams are groups of two or more interdependent agents with their own and possibly conflicting goals that join together as a single negotiating party because they share a common goal that is related to the negotiation. In this paper we present a general workflow of tasks for agents that participate in a negotiation team. Furthermore, we focus on the study of intra-team organizations, which define the roles played by team members, what decisions are taken by the team, and how and when they are taken, and the negotiation strategy carried out with the opponent. The performance of the proposed organizations is tested in environmental scenarios where team diversity is different.

Agreement technologies and adaptive systems have arisen as promising mechanisms to design open dynamic multi-agent systems where there may be complex interactions and environmental conditions may frequently change. In this paper, we propose a development framework whose goal is to ease the deployment of adaptive techniques and agreement technologies in the soccer simulation domain. The framework provides a middleware that abstracts researchers from simulation details, thus they can focus on the particular study of their algorithms. Moreover, we analyze the soccer domain to identify how the types of adaptation and agreement technologies found in the literature are also present in the domain.

In this paper we present an agent-based add-on for the Social-Net Tourism Recommender System that uses information extraction and natural language processing techniques in order to automatically extract and classify information from the Web. Its goal is to maintain the system updated and obtain information about third parties services that are not offered by service providers inside the system.

Recommender systems face some problems On the one hand information needs to be maintained updated, which can result in a costly task if it is not performed automatically On the other hand, it may be interesting to include third party services in the recommendation since they improve its quality In this paper, we present an add-on for the Social-Net Tourism Recommender System that uses information extraction and natural language processing techniques in order to automatically extract and classify information from the Web Its goal is to maintain the system updated and obtain information about third party services that are not offered by service providers inside the system.

Multi-agent systems may pose a real challenge to management since resources like hosts, agents or agent societies may have different owners and thus different interests. Management tools for multi-agent systems should take into account these inconveniences in order to apply a proper management. We detected in our study that most current management tools only show information about current state. We also think that information about the history of the system can supply a wealth of additional information to system administrators. In this paper we present MAMSY, a management tool for multi-agent systems aimed at showing and controlling current state and past related information.

Cloud and Fog computing has emerged as a promising paradigm for the Internet of things (IoT) and cyber–physical systems (CPS). One characteristic of CPS is the reciprocal feedback loops between physical processes and cyber elements (computation, software and networking), which implies that data stream analytics is one of the core components of CPS. The reasons for this are: (i) it extracts the insights and the knowledge from the data streams generated by various sensors and other monitoring components embedded in the physical systems; (ii) it supports informed decision making; (iii) it enables feedback from the physical processes to the cyber counterparts; (iv) it eventually facilitates the integration of cyber and physical systems. There have been many successful applications of data streams analytics, powered by machine learning techniques, to CPS systems. Thus, it is necessary to have a survey on the particularities of the application of machine learning techniques to the CPS domain. In particular, we explore how machine learning methods should be deployed and integrated in Cloud and Fog architectures for better fulfilment of the requirements of mission criticality and time criticality arising in CPS domains. To the best of our knowledge, this paper is the first to systematically study machine learning techniques for CPS data stream analytics from various perspectives, especially from a perspective that leads to the discussion and guidance of how the CPS machine learning methods should be deployed in a Cloud and Fog architecture.