Learning is crucial for automated negotiation, and recent years have witnessed a remarkable achievement in application of reinforcement learning (RL) for various negotiation tasks. Conventional RL methods focus generally on learning from active interactions with opposing negotiators. However, collecting online data is expensive in many realistic negotiation scenarios. While previous studies partially mitigate this problem through the use of opponent simulators (i.e., agents following known strategies), in reality it is usually hard to fully capture an opponent’s negotiation strategy. Moreover, a further challenge lies in an agent’s capability of adapting to dynamic variations of an opponent’s preferences or strategies, which may happen from time to time for different reasons in subsequent negotiations. In response to these challenges, this article proposes a novel Deep Offline Reinforcement learning Negotiating Agent framework that allows to learn an effective strategy using previously collected negotiation datasets without requiring interaction with an opponent. This is in contrast to existing RL-based negotiation approaches that all rely on active interaction with opponents. Furthermore, the strategy fine-tuning mechanism is included to adjust the learned strategy in response to the preferences or strategy changes of the opponent. The performance of the proposed framework is evaluated based on a diverse set of state-of-the-art baselines under different settings. Experimental results show that the framework allows to learn effective strategies exclusively with offline datasets, and is also capable of effectively adapting to changes of an opponent’s preferences or strategy.

Detecting false information in the form of fake news has become a bigger challenge than anticipated. There are multiple promising ways of approaching such a problem, ranging from source-based detection, linguistic feature extraction, and sentiment analysis of articles. While analyzing the sentiment of text has produced some promising results, this paper explores a rather more fine-grained strategy of classifying news as fake or real, based solely on the emotion profile of an article's title. A RoBERTa model was first trained to perform Emotion Classification, achieving test accuracy of about 90%. Six basic emotions were used for the task, based on the prominent psychologist Paul Ekman - fear, joy, anger, sadness, disgust and surprise. A seventh emotional category was also added to represent neutral text. Model performance was also validated by comparing classification results to other state-of-the-art models, developed by other groups. The model was then used to make inference on the emotion profile of news titles, returning a probability vector, which describes the emotion that the title conveys. Having the emotion probability vectors for each article's title, another Binary Random Forest classifier model was trained to evaluate news as either fake or real, based solely on their emotion profile. The model achieved up to 88% accuracy on the Kaggle Fake and Real News Dataset, showing there is a connection present between the emotion profile of news titles and if the article is fake or real.

Analyzing the relationships among various drugs is an essential issue in the field of computational biology. Different kinds of informative knowledge, such as drug repurposing, can be extracted from drug-drug relationships. Scientific literature represents a rich source for the retrieval of knowledge about the relationships between biological concepts, mainly drug-drug, disease-disease, and drug-disease relationships. In this paper, we propose DDREL as a general-purpose method that applies deep learning on scientific literature to automatically extract the graph of syntactic and semantic relationships among drugs. DDREL remarkably outperforms the existing human drug network method and a random network respected to average similarities of drugs’ anatomical therapeutic chemical (ATC) codes. DDREL is able to shed light on the existing deficiency of the ATC codes in various drug groups. From the DDREL graph, the history of drug discovery became visible. In addition, drugs that had repurposing score 1 (diflunisal, pargyline, fenofibrate, guanfacine, chlorzoxazone, doxazosin, oxymetholone, azathioprine, drotaverine, demecarium, omifensine, yohimbine) were already used in additional indication. The proposed DDREL method justifies the predictive power of textual data in PubMed abstracts. DDREL shows that such data can be used to 1- Predict repurposing drugs with high accuracy, and 2- Reveal existing deficiencies of the ATC codes in various drug groups.

Much of the focus on emotion recognition has gone into the face and voice as expressive channels, whereas bodily expressions of emotions are understudied. Moreover, current studies lack the explainability of computational features of body movements related to emotional expressions. Perceptual research on body parts' movements shows that features related to the arms' movements are correlated the most with human perception of emotions. In this paper, our research aims at presenting an explainable approach for bodily expressed emotion recognition. It utilizes the body joints of the human skeleton, representing them as a graph, which is used in Graph Convolutional Networks (GCNs). We improve the modelling of the GCNs by using spatial attention mechanisms based on body parts, i.e. arms, legs and torso. Our study presents a state-of-the-art explainable approach supported by experimental results on two challenging datasets. Evaluations show that the proposed methodology offers accurate performance and explainable decisions. The methodology demonstrates which body part contributes the most in its inference, showing the significance of arm movements in emotion recognition.

Harmony in visual compositions is a concept that cannot be defined or easily expressed mathematically, even by humans. The goal of the research described in this paper was to find a numerical representation of artistic compositions with different levels of harmony. We ask humans to rate a collection of grayscale images based on the harmony they convey. To represent the images, a set of special features were designed and extracted. By doing so, it became possible to assign objective measures to subjectively judged compositions. Given the ratings and the extracted features, we utilized machine learning algorithms to evaluate the efficiency of such representations in a harmony classification problem. The best performing model (SVM) achieved 80% accuracy in distinguishing between harmonic and disharmonic images, which reinforces the assumption that concept of harmony can be expressed in a mathematical way that can be assessed by humans.

Classifier combination through ensemble systems is one of the most effective approaches to improve the accuracy of classification systems. Ensemble systems are generally used to combine classifiers; However, selecting the best combination of individual classifiers is a challenging task. In this paper, we propose an efficient assembling method that employs both meta-learning and a genetic algorithm for the selection of the best classifiers. Our method is called MEGA, standing for using MEta-learning and a Genetic Algorithm for algorithm recommendation. MEGA has three main components: Training, Model Interpretation and Testing. The Training component extracts meta-features of each training dataset and uses a genetic algorithm to discover the best classifier combination. The Model Interpretation component interprets the relationships between meta-features and classifiers using a priori and multi-label decision tree algorithms. Finally, the Testing component uses a weighted k-nearest-neighbors algorithm to predict the best combination of classifiers for unseen datasets. We present extensive experimental results that demonstrate the performance of MEGA. MEGA achieves superior results in a comparison of three other methods and, most importantly, is able to find novel interpretable rules that can be used to select the best combination of classifiers for an unseen dataset.

To extract structured knowledge from unstructured text sources we need to understand the semantic relationships between entities. State-of-the-art relation extraction techniques take advantage of the abundance of data on the web. However, in domains with sparse data such as social networks which ha ve limited occurrences of entities and relationship patterns, bootstrapping techniques and pattern detection methods are inefficient and inaccurate. In this paper, we introduce REDS, a Relation Extraction approach based on Distant Supervision. REDS extracts the named entities from text documents and assigns a fingerprint to each potential relationship among the named entities. Then, it queries a knowledge repository for similar matches to each fingerprint. An assessor uses the query results and the data statistics to measure the validity of the relationships corresponding to the queried fingerprints, and labels each potential relationship with the predicted type. In addition to handling the relation extraction in presence of data sparsity, REDS uses an information retrieval framework that makes it scalable and capable of dealing with noisy data. We implement and test REDS on a non-English historical archive consisting of unstructured notarial acts and structured civil registers; By means of manual evaluations REDS achieves precision of 0.90.

Affective state recognition has recently attracted a notable amount of attention in the research community, as it can be directly linked to a student's performance during learning. Consequently, being able to retrieve the affect of a student can lead to more personalized education, targeting higher degrees of engagement and, thus, optimizing the learning experience and its outcomes. In this paper, we apply Machine Learning (ML) and present a novel approach for affect recognition in Technology-Enhanced Learning (TEL) by understanding learners' experience through tracking their interactions with a serious game as a learning platform. We utilize a variety of interaction parameters to examine their potential to be used as an indicator of the learner's affective state. Driven by the Theory of Flow model, we investigate the correspondence between the prediction of users' self-reported affective states and the interaction features. Cross-subject evaluation using Support Vector Machines (SVMs) on a dataset of 32 participants interacting with the platform demonstrated that the proposed framework could achieve a significant precision in affect recognition. The subject-based evaluation highlighted the benefits of an adaptive personalized learning experience, contributing to achieving optimized levels of engagement.

The rise of internet and mobile technologies (such as smartphones) provide a harness of data and an opportunity to learn about peoples’ states, behavior, and context in regard to several application areas such as health. Eating behavior is an area that can benefit from the development of effective e-coaching applications which utilize psychological theories and data science techniques. In this paper, we propose a framework of how machine learning techniques can effectively be used in order to fully exploit data collected from a mobile application (“Think Slim”) which is designed to assess eating behavior using experience sampling methods. The overall goal is to analyze individual states of a person status (emotions, location, activity, etc.) and assess their impact on unhealthy eating. Building on data collected from different participants, a classification algorithm (decision tree tailored to longitudinal data) is used to warn people prior to a possible unhealthy eating event and a clustering algorithm (hierarchical agglomerative clustering) is used for profiling the participants and generalize for new users of the application. Finally, a framework to offer feedback via adaptive messages (intervention) and recommendations prior to possible unhealthy eating events is presented. Results from applying our methods reveal that participants can be clustered to six robust groups based on their eating behavior and that there are specific rules that discriminate which conditions lead to healthy versus unhealthy eating. Consequently, these rules can be utilized to provide adaptive semi-tailored feedback to users who, through this method, are assisted in learning under which conditions are more prone to unhealthy eating. Effectiveness of the approach is confirmed by observing a decreasing trend in rule activation towards the end of intervention period.

Ecological Momentary Assessment (EMA) data is organized in multiple levels (per-subject, per-day, etc.) and this particular structure should be taken into account in machine learning algorithms used in EMA like decision trees and its variants. We propose a new algorithm called BBT (standing for Bagged Boosted Trees) that is enhanced by a over/under sampling method and can provide better estimates for the conditional class probability function. Experimental results on a real-world dataset show that BBT can benefit EMA data classification and performance.

Self-Organizing Map (SOM) is a neural network model which is used to obtain a topology-preserving mapping from the (usually high dimensional) input/feature space to an output/map space of fewer dimensions (usually two or three in order to facilitate visualization). Neurons in the output space are connected with each other but this structure remains fixed throughout training and learning is achieved through the updating of neuron reference vectors in feature space. Despite the fact that growing variants of SOM overcome the fixed structure limitation they increase computational cost and also do not allow the removal of a neuron after its introduction. In this paper, a variant of SOM is proposed called AMSOM (Adaptive Moving Self-Organizing Map) that on the one hand creates a more flexible structure where neuron positions are dynamically altered during training and on the other hand tackles the drawback of having a predefined grid by allowing neuron addition and/or removal during training. Experiments using multiple literature datasets show that the proposed method improves training performance of SOM, leads to a better visualization of the input dataset and provides a framework for determining the optimal number and structure of neurons.

Ecological Momentary Assessment (EMA) techniques gain more ground in studies and data collection among different disciplines. Decision tree algorithms and their ensemble variants are widely used for classifying this type of data, since they are easy to use and provide satisfactory results. However, most of these algorithms do not take into account the multiple levels (per-subject, per-day, etc.) in which EMA data are organized. In this paper we explore how the EMA data organization can be taken into account when dealing with decision trees and specifically how a combination of bagging and boosting can be utilized in a classification task. A new algorithm called BBT (standing for Bagged Boosted Trees) is proposed which is enhanced by an over/under sampling method leading to better estimates of the conditional class probability function. BBT's necessity and effects are demonstrated using both simulated datasets and real-world EMA data collected using a mobile application following the eating behavior of 100 people. Experimental analysis shows that BBT leads to clear improvements with respect to prediction error reduction and conditional class probability estimation.

Entity Resolution (ER) is the process of identifying references referring to the same entity from one or more data sources. In the ER process, most existing approaches exploit the content information of references, categorized as content-based ER, or additionally consider linkage information among references, categorized as context-based ER. However, in new applications of ER, such as in the genealogical domain, the very limited linkage information among references results in a disjoint graph in which the existing content-/context-based ER techniques have very limited applicability. Therefore, in this paper we propose first, to use the homophily principle for augmentation of the original input graph by connecting the potential similar references, and second, to use a Random Walk based approach to consider contextual information available for each reference in the augmented graph. We evaluate the proposed method by applying it to a large genealogical dataset and we succeed to predict 420,000 reference matches with precision 92% and discover six novel and informative patterns among them which can not be detected in the original disjoint graph.

In this paper, a prestige-based evolution process is introduced, which provides a formal framework for the study of linear hierarchies seen in human societies. Due to the deterministic characteristics of the proposed model, we are capable of determining equilibria in closed form. Surprisingly, these stationary points recover the power-law degree distribution as the shared property of the resulting hierarchal networks, explaining the prevalence of hierarchies in societies. This result sheds light on the evolutionary advantages of hierarchies.

Negotiations among autonomous agents have gained a mass of attention from a variety of communities in the past decade. This paper deals with a prominent type of automated negotiations, namely, multilateral multi-issue negotiation that runs under real-time constraints and in which the negotiating agents have no prior knowledge about their opponents' preferences over the space of negotiation outcomes. We propose a novel negotiation approach which enables an agent to reach an efficient agreement with multiple opponents. The proposed approach achieves that goal by, 1) employing sparse pseudo-input Gaussian processes to model the behavior of opponents, 2) learning fuzzy opponent preferences to increase the satisfaction of other parties, and 3) adopting an adaptive decision-making mechanism to handle uncertainty in negotiation. The experimental results show, both from the standard mean-score perspective and the perspective of empirical game theory, that the agent applying the proposed approach outperforms the state-of-the-art negotiation agents from the recent Automated Negotiating Agents Competition (ANAC) in a variety of negotiation domains.

This paper presents a theoretical as well as empirical study on the evolution of cooperation on complex social networks, following the continuous action iterated prisoner's dilemma (CAIPD) model. In particular, convergence to network-wide agreement is proven for both evolutionary networks with fixed interaction dynamics, as well as for coevolutionary networks where these dynamics change over time. Moreover, an extension to the CAIPD model is proposed that allows to model influence on the evolution of cooperation in social networks. As such, this work contributes to a better understanding of behavioral change on social networks, and provides a first step towards their active control.

In this paper we investigate coordination principles inspired by the behaviour of honeybees and ants for coordination purposes in multi-robot systems. Specifically, we study the problem instances of bee-inspired robot Foraging and ant-inspired robot Coverage, where Foraging is the problem of exploring the environment in search of food or provisions and Coverage is the problem of deploying a robotic swarm in the environment with the task of maximising the sensor coverage of the environment. To effectively and efficiently solve both problems, distributed multi-robot coordination is required. For the first problem we investigate a bee-inspired solution method. The second problem is studied using a stigmergic approach. In an extensive set of experiments we first study the feasibility of the proposed multi-robot coordination for robotic swarms with extended resources and discuss the benefits and limitations of using these swarms. Furthermore, as the downsizing in swarm robotics becomes increasingly important with ongoing miniaturization in various applications, the feasibility of the proposed coordination techniques for robotic swarms with limited resources is studied in detail; the practical requirements for overcoming the limitations of these swarms are introduced and the main need to incorporate these robots in real world experiments is discussed.

The evolution of cooperation in social networks, and the emergence of these networks using simple rules of attachment, have both been studied extensively although mostly in separation. In real-world scenarios, however, these two fields are typically intertwined, where individuals’ behavior affect the structural emergence of the network and vice versa. Although much progress has been made in understanding each of the aforementioned fields, many joint characteristics are still unrevealed. In this paper we propose the Simultaneous Emergence and Evolution (SEE) model, aiming at unifying the study of these two fields. The SEE model combines the continuous action prisoner’s dilemma (modeling the evolution of cooperation) with preferential attachment (used to model network emergence), enabling the simultaneous study of both structural emergence and behavioral evolution of social networks. A set of empirical experiments show that the SEE model is capable of generating realistic complex networks, while at the same time allowing for the study of the impact of initial conditions on the evolution of cooperation.

We study the evolution of cooperation in social networks, aiming in particular at ways of influencing the behavior in such networks using methods and techniques from optimal control theory. This is of importance to many scenarios where politicians or policy makers strive to push consensus on some topic that may seem suboptimal from individuals' perspectives. To this end, we employ the Continuous Action Iterated Prisoner's Dilemma (CAIPD) as model for the interactions in a social network. This model describes how neighboring nodes influence each other, and in effect determines how different strategies may spread through the network. We extend this model, incorporating a mechanism for external influence on the behavior of individual nodes. Next we prove reachability of an arbitrary network-wide agreement using the Lyapunov's Direct Method. Based on the theory of Linear-Quadratic Trackers we propose a step-wise iterative control algorithm, and show the effectiveness of the proposed controller in various Small World and Scale Free social networks.

The complexity of automated negotiation in a multi-issue, incomplete-information and continuous-time environment poses severe challenges, and in recent years many strategies have been proposed in response to this challenge. For the traditional evolution, strategies are studied in games assuming that “globally” negotiates with all other participates. This evaluation, however, is not suited for negotiation settings that are primarily characterized by “local” interactions among the participating agents, that is, settings in which each of possibly many participating agents negotiates only with its local neighbors rather than all other agents. A new class of negotiation games is therefore introduced that take negotiation locality (hence spatial information about the agents) into consideration. It is shown how spatial evolutionary game theory can be used to interpret bilateral negotiation results among state-of-the-art strategies.

Automated negotiation among self-interested autonomous agents has gained tremendous attention due to the diversity of its broad range of potential real-world applications. This article deals with a prominent type of such negotiations, namely, multiissue negotiation that runs under continuous-time constraints and in which the negotiating agents have no prior knowledge about their opponents’ preferences and strategies. A negotiation strategy called Dragon is described that employs sparse pseudoinput Gaussian processes. Specifically, Dragon enables an agent (1) to precisely model the behavior of its opponents with comparably low computational load and (2) to make decisions effectively and adaptively in very complex negotiation settings. Extensive experimental results, based on a number of negotiation scenarios and state-of-the-art negotiating agents from Automated Negotiating Agents Competitions, are provided. Moreover, the robustness of our strategy is evaluated through both empirical game-theoretic and spatial evolutionary game-theoretic analysis.

In open environments like the Web, and open multiagent and Peer2Peer systems, consent among the autonomous, self-interested knowledge sources and users very often cannot be established, and the estimation of trustability and truthfulness of knowledge sources may not be possible. Moreover, competing viewpoints and their communicative contexts even provide valuable meta-knowledge about the intentions of the participants and their social relationships. As a foundational approach to semantically heterogeneous knowledge perspectives, we introduce a formal framework for the computational representation and integration of multi-source knowledge, which makes explicit heterogeneous viewpoints, and conflicting opinions and their social contexts, and allows for the rating, generalization and optional fusion of knowledge by social choice.

Classifier systems constitute a general model of low-level rule-based systems that are capable of environmental interaction and learning. A central characteristic and drawback of the traditional approaches to learning in such systems is that they exclusively work on the rule level, without taking into consideration that the individual rules possess a very complex activity behaviour. This article investigates an alternative, action-oriented perspective of learning in classifier systems which does not suffer from this drawback. According to this perspective learning is realized on the finer action level instead of the coarser rule level. Comparative theoretical and experimental results are presented that show the advantages of the action-oriented over the traditional perspective.

One of the problems of autonomous mobile systems is the continuous tracking of position and orientation. In most cases, this problem is solved by dead reckoning, based on measurement of wheel rotations or step counts and step width. Unfortunately dead reckoning leads to accumulation of drift errors and is very sensitive to slipping. In this paper an algorithm for tracking position and orientation is presented, it is nearly independent from odometry and its problems with slipping. To achieve these results, a rotating range-finder is used, delivering scans of the environmental structure. The properties of this structure are used to match the scans from different locations in order to find their translational and rotational displacement. For this purpose derivatives of range-finder scans are calculated which can be used to find position and orientation by crosscorrelation.>

Learning in multiagent systems is a new research field in distributed artificial intelligence. The author investigates an action-oriented approach to delayed reinforcement learning in reactive multiagent systems and focuses on the question of how the agents can learn to coordinate their actions. Two basic algorithms, the ACE algorithm and the AGE algorithm (ACE and AGE stand for Action Estimation and Action Group Estimation, respectively), for the collective learning of appropriate action sequences are introduced. Both algorithms explicitly take into consideration that (i) each agent typically knows only a fraction of its environment, (ii) the agents typically have to cooperate in solving tasks, and (iii) actions carried out by the agents can be incompatible. The experiments described illustrate these algorithms and their learning capacities.>