With the volume of biomedical literature currently increasing at an unparalleled rate, researchers in biomedical sciences are quickly losing the capacity to single-handedly grasp the available information in a diversity of domains. However, owing to the growing body of available texts and open-access policies of an increasing number of publishers, Information Extraction (IE) in the biomedical domain is becoming an important task to aid in the condensation and systematization of scientific information in subject-specific areas and ideally reduce the burden of manual literature research in the biomedical scope. The present manuscript aims to provide a comprehensive survey on the tasks of Biomedical Named Entity Recognition (BioNER) and Biomedical Relation Extraction (BioRE), namely in terms of their evolution, current common methodologies, existing resources, and the challenges and opportunities they provide. Finally, a few possible routes for the development of Biomedical IE are also discussed.

Emotion regulation (ER) is a key competency in early childhood development, particularly between ages 3 and 5. While serious games (SGs) have proven effective in several educational contexts, few are designed for preschoolers' emotional needs. This study presents Ilhas das Emoções, a digital interactive SG developed in Unity to promote ER in preschool children. The game was co-designed with 5 psychologists. The SG presents psychoeducation on five core emotions (joy, sadness, anger, fear, and disgust). It offers playful, scenario-based learning across three stages: identifying emotions, understanding their emotional causes and consequences, and regulating emotions. This work details the theoretical rationale and technical development of the game, contributing a novel, developmentally appropriate tool to early childhood emotional education.

This study focuses on designing and developing a serious games (SG) platform for the cognitive and psychosocial rehabilitation following an acquired brain injury (ABI). The CogniSpace platform includes both multiplayer cooperative games designed to foster social skills and relationships, as well as single-player games that allow users to make individual progress. A co-design methodology was employed throughout the design and development process, with active involvement from patients with dementia, healthcare professionals, and family members. This approach has resulted in a user-friendly platform which aims to improve memory, executive function, language skills and mathematical reasoning through four SGs: Memory Tornado, Perfect Pair, Counting Change and Everything Ready. Future work will involve conducting a usability study with people with ABI. This research highlights the importance of co-design in the development of SGs for healthcare and demonstrates how involving end users ensures the creation of tools that meet their specific needs.

Adolescence is a crucial period for emotional development, often marked by challenges in emotion regulation (ER) that can contribute to mental health problems such as anxiety, depression and engagement in risky behaviors. Despite increasing independence during this period, parents remain crucial in shaping adolescents' emotional competencies. Recognizing this, the multiplayer serious game Emotion Odyssey was designed to teach and train young adolescents (aged 10-14) and their parents in ER strategies. Using the engaging concept of an escape room, Emotion Odyssey introduces the six basic emotions and ER strategies through immersive scenarios. The development process was participating involving an adolescent, her mother and psychologists ($n=4$). Through interactive gameplay, the game aims to increase emotional understanding and ER skills and encourage adolescents and their parents to communicate openly about emotions.

Abstract This study examines the challenge of defining quality in peer-review reports, a crucial yet underexplored aspect of academic publishing. Reviewers are vital gatekeepers of scientific knowledge, but unclear skills and a lack of standardized guidelines have led to inconsistent and subjective practices, weakening the overall efficacy of the peer-review process. To address this issue, the primary objective of this paper is to answer the research question: How has literature addressed guidance for producing quality peer-review reports? A scoping review was conducted, utilizing Scopus, Web of Science, SpringerLink, ScienceDirect, PubMed, and SAGE databases to search for records using keywords related to guidelines for scientific peer reviewing. The review identified 111 primary studies offering recommendations on how to review scientific articles. Extracted data were analysed thematically, focusing on approaches to reviewing articles, manuscript evaluation criteria, and report-writing guidelines. The findings revealed six key categories of review criteria for evaluating scientific manuscripts: structural components, research approach, style, ethical conduct, scientific value, and overall suitability. Additionally, the review provides 70 actionable recommendations for writing peer-review reports and highlights eight essential quality features expected in review texts: constructive, specific, fair, thorough, courteous, consistent, objective, and readable feedback. This study contributes to developing a standardized guide for scientific reviewing, with a particular emphasis on supporting early-career reviewers. The findings encourage academic publishers, journal editors, and professional organizations to adopt the proposed guidelines to enhance consistency, reduce bias, and improve the peer-review process. They also provide a foundation for developing new tools to support the reviewing.

Abstract The RoboCup 3D soccer simulation league serves as a competitive platform for showcasing innovation in autonomous humanoid robot agents through simulated soccer matches. Our team, FC Portugal, developed a new codebase from scratch in Python after RoboCup 2021. The team’s performance relies on a set of skills centered around novel unifying primitives and a custom, symmetry-extended version of the proximal policy optimization algorithm. Our methods have been thoroughly tested in official RoboCup matches, where FC Portugal has won the last two main competitions, in 2022 and 2023. This paper presents our training framework, as well as a timeline of skills developed using our skill-set-primitives, which considerably improve the sample efficiency and stability of skills, and motivate seamless transitions. We start with a significantly fast Sprint-Kick developed in 2021 and progress to the most recent skill set, including a multi-purpose omnidirectional walk, a dribble with unprecedented ball control, a solid kick, and a push skill. The push addresses low-level collision scenarios and high-level strategies to increase ball possession. We address the resource-intensive nature of this task through an innovative multi-agent learning approach. Finally, we release the team’s codebase to the RoboCup community, providing other teams with a robust and modern foundation upon which they can build new features.

Acquired brain injury (ABI) is a condition that causes damage to brain tissues and consequently leads to physical, behavioral and cognitive issues. One of the impairments observed after the injury is in the realm of social cognition (SC). This impairment leads these patients to lose their ability to interact socially in a positive and effective manner. Consequently, these patients may socially isolate themselves and lose interpersonal relationships. One of the methods used for cognitive rehabilitation that has proven to be effective is the use of serious games (SG). SG are video games with the purpose of learning and improving people’s live. Thus, in this work, a set of SG will be developed for the cognitive and psychosocial rehabilitation of people with ABI, focusing on socio-emotional skills training. To ensure their success, a co-design methodology will be carried out during the conception and implementation of the game. This approach will involve patients, healthcare professionals, and caregivers to obtain suggestions and feedback to ensure that the most suitable solutions are implemented in the game. At the end of this work, we hope to have developed a method for training socio-emotional skills that can motivate patients more effectively than traditional methods.

This paper introduces an ongoing project focused on co-designing a serious game (SG) to explore emotion regulation (ER) strategies with parents. The game’s main goal is for parents to learn to regulate their emotions, enabling them to support their adolescents’ emotional development. Semi-structured interviews were conducted to pinpoint the SG’s key motivating and learning features. The participants include four healthcare professionals with an average of 14.75 years of experience. The insights from these interviews show promising concepts that were integrated into the SG. The innovative nature of this project is highlighted by the fact that, to our knowledge, this will be the first SG focusing on training ER skills in parents.

Serious games (SGs) have emerged as an appropriate intervention for adolescents due to their significant use of video games. Although literature reviews and meta-analyses increasingly support the effectiveness and acceptability of SGs in addressing mental health problems and promoting healthy lifestyles in young people, there remains a significant gap in SGs specifically designed to improve emotion regulation (ER) skills. In response, we present the serious game GamEmotion, a 3D digital action-adventure game developed in Unity, designed to provide psychoeducation about six basic emotions (happiness, sadness, fear, anger, surprise, and disgust) and two ER strategies (cognitive reappraisal and expressive suppression). We believe that we have developed the first SG to improve emotional awareness and ER in Portuguese adolescents, based on the Gross process model, one of the most widely reported models of ER. We present the GamEmotion framework alongside our ongoing usability study protocol with young adolescents ($10-14$ years) and their parents. By proposing this innovative tool, we aim to broaden the application of SG and address specific challenges in adolescent ER to improve mental health.

Meta-game balance is a crucial task in game development, and automation of this process could assist game developers by vastly reducing time costs. We explore and evaluate a meta-game balance model over the recently proposed VGC AI Competition Framework. We propose an adversarial model where team builder agents try to maximize their win rate by narrowing to the most optimal team configurations, resulting in a reduction of the diversity of Pokémon employed, while a balancing agent re-adapts the Pokémon inner attributes to incentive the team builder agents to incorporate a greater variety of Pokémon into their teams increasing the meta-game's overall diversity and balance. one Furthermore, we developed multiple team builder agents divided into two groups: the first group assumes that individual Pokémon advantages are the primary factor to determine the outcome of game matches; the second group also exploits the implicit synergy between teammates. These agents make use of meta-gaming, linear optimization, and evolutionary search to find strong combinations against the current meta-game. The strongest team builder is faced against the team meta-game balance agent for its evaluation. Deep learning is also employed to predict the outcome of matches and recommend constructive elements of teams.

Regardless of the goal of a game, it should be a pleasant and fun experience for its players. For some games to be enjoyable, the level of difficulty must be carefully calibrated, otherwise, players will feel bored or frustrated. Multiplayer scenarios in particular, where one player’s satisfaction might not translate to the enjoyment of other players and poses extra challenges in balancing the difficulty. The performance of one player is relative to the opponent, versus single-player scenarios where we can fully control the environment. We propose an AI automation framework for difficulty balancing in two-player games, where balancing is seen as a Reinforcement Learning task. A Game Master (GM) agent learns how to use handicap game mechanics, signaled by a reward function that evaluates a weighted combination of aesthetic criteria that encourages dramatization and allows a player in the lead to go back and a player in the rear to catch up, creating the desired rubber banding effect that balances out skill gaps. The quality of the games with the trained GM embedded is examined by measuring the same aesthetic criteria on the resulting games, and by analyzing the resulting changes in the game.

Drowsy driving is one of the leading causes of traffic accidents. Some solution provides feedback when the driver is drowsy, however, few tackle the issue in a way that allows for portability and early prevision. This study focuses on drowsiness detection during driving. Wearable sensors are used, for a low-cost, portable, automated, and non-intrusive solution. The wearable sensors chosen for biosignal acquisition are Empatica's E4 wristband for heart activity acquisition and Brainlink Pro for brain activity. Features were mainly in the time domain and time-frequency, and algorithms, such as Nearest Neighbours, Radial Basis Function, Support Vector Machine, Decision Tree, Random Forest, Multi-layer Perceptron, Naive Bayes, and Logistic Regression were trained and validated through the use of a database developed for this study (11 adults with normal last-night sleep, and 2 without any last-night sleep). Participants answered Pittsburgh, and Satisfaction, Alertness, Timing, Efficiency and Duration questionnaires, after which photoplethysmography and electroencephalography physiological signals were acquired during driving in a simulation environment. The practice-run discrimination and individual classification had comparable results, both slightly above average (70 to 80%). The evaluation metric values showed that the discrimination of sleep-deprived exams yielded significantly better. This suggests that the proposed methodology is capable of classifying sleep deprivation and surpasses existing ones in its portability

The Rapid development and the emergence of technologies capable of producing real-time data opened new horizons to both planning and optimization of vehicle routes [4]. In this dissertation, the Autoridade de Segurança Alimentar e Económica (ASAE) operation's scenario will be explored and analyzed as a case study to the problem. ASAE is a Portuguese administrative authority specialized in food security and economic auditing and is responsible to regulate thousands of economic entities in the Portuguese territory. ASAE inspections are usually done by brigades using vehicles to inspect economic operators, taking into account their timetables. Previous work on this topic led to the implementation of an inspection route optimization module capable of defining and assigning routes to inspect economic operators, seeking to maximize a utility function. Using optimization algorithms, inspection routes are calculated for each brigade, with information regarding specific map paths and inspection schedules. The approach used does not take into consideration the dynamic properties of real-life scenarios, as the precalculated operation plan is not reviewed in real-time. This work aims to study the dynamic properties of ASAE's operational environment and proposes a solution to efficiently review the precalculated inspection routes and apply the required changes in an appropriate time frame. Vehicle routing problems (VRP) are optimization problems where the aim is to calculate the set of optimized routes for a vehicle fleet, from a starting point to several interesting locations. Dynamic vehicle routing problem (DVRP) is a variant of VRP that makes use of real-time information to calculate the most optimized set of routes at a certain moment [39]. DVRP is a challenging problem because its scope is real-time, meaning that decisions sometimes must be made in short time windows, preventing the use of complex algorithms that require long computational times [10]. The typical approach to this problem is to initially calculate the routes for the whole fleet and dynamically revise the defined operations plan in real-time, once a disruption occurs. This work will model the problem as a DVRP and will compare the performance of heuristics and other modern optimization techniques, proposing a solution that will reduce the impact of disruptions on inspection routes. An optimized operations plan will reduce the time required for inspections, allowing massive economic savings, while reducing a company's ecological footstep. The work can eventually be scaled and used in other institutions, such as GNR or PSP in Portugal, that operate similarly.

This work presents a framework for a new type of meta-game balance AI Competition based on Pokémon, Pokémon battles can be viewed as adversarial games played by AIs. Around these games, there is also a meta-game: which Pokémon to include in a team for battles, which moves to pick for every Pokémon in the team, etc. This meta-game is itself a game with a set of rules that govern which Pokémon and which moves are available in the roster that can be selected from, or which attributes (health points, damage, etc.) a Pokémon or moves should have. The aim of the framework is to facilitate competitions in creating the most balanced meta-game possible; one where there is a large variety of Pokémon and moves to choose from, and many possible combinations that are effective. AI agents could assist human designers in achieving strategically expressive meta-games, and this type of benchmark could incentivize game designers and researchers alike to advance knowledge on this type of domain.

Abstract Knowledge Engineering has become essential in the fields of Medical and Health Care with emphasis for helping citizens to improve their health and quality of life. This includes individual methods and techniques in health‐related knowledge acquisition and representation and their application in the construction of intelligent systems capable of using the acquired information to improve the patients' health and/or quality of life. Haemato‐oncological diseases can provide significant disability and suffering, with severe symptoms and psychological distress. They can create difficulties in fulfilling professional, family and social roles, affecting an individual's quality of life. Health related quality of life (HRQoL) is a subjective concept but there is also an objective component related to physiological indicators. Some of these physiological indicators can be easily assessed by wearable technology such heart rate variability (HRV). This paper introduces an intelligent system to assess, in real‐time, potential HRV indices, that can predict HRQoL in lymphoma patients throughout chemotherapy treatment and to account the individuals' variability. The system is based on wearable technology and intelligent processing of the patients' biometric information to assess some quality of life related parameters. A longitudinal study was conducted among 16 lymphoma patients using this intelligent system. Mixed‐effect regression models were performed to investigate predictors for and time effects on HRQoL. There were no significant changes in all HRQoL domains over time. Some quality of life domains revealed similar time trends as HRV indices. These HRV indices also have a significant effect on the domains of quality of life.

Over the last years, robotics has increased its interest in learning human-like behaviors and activities. One of the most common actions searched, as well as one of the most fun to replicate, is the ability to play sports. This has been made possible with the steady increase of automated learning, encouraged by the tremendous developments in computational power and improved reinforcement learning (RL) algorithms.This paper implements a beginner Robot player for precision ball sports like boccia and bocce. A new simulated environment (PrecisionBall) is created, and a seven degree-of-freedom (DoF) robotic arm, is able to learn from scratch how to win the game and throw different types of balls towards the goal (the jack), using deep reinforcement learning. The environment is compliant with OpenAI Gym, using the MuJoCo realistic physics engine for a realistic simulation. A brief comparison of the convergence of different RL algorithms is performed. Several ball weights and various types of materials correspondent to bocce and boccia are tested, as well as different friction coefficients. Results show that the robot achieves a maximum success rate of 92.7% and mean of 75.7% for the best case. While learning to play these sports with the DDPG+HER algorithm, the robotic agent acquired some relevant skills that allowed it to win.

Artificial Intelligence (AI) has seen numerous applications in the area of Education. Through the use of educational technologies such as Intelligent Tutoring Systems (ITS), learning possibilities have increased significantly. One of the main challenges for the widespread use of ITS is the ability to automatically generate questions. Bearing in mind that the act of questioning has been shown to improve the students learning outcomes, Automatic Question Generation (AQG) has proven to be one of the most important applications for optimizing this process. We present a tool for generating factual questions in Portuguese by proposing three distinct approaches. The first one performs a syntax-based analysis of a given text by using the information obtained from Part-of-speech tagging (PoS) and Named Entity Recognition (NER). The second approach carries out a semantic analysis of the sentences, through Semantic Role Labeling (SRL). The last method extracts the inherent dependencies within sentences using Dependency Parsing. All of these methods are possible thanks to Natural Language Processing (NLP) techniques. For evaluation, we have elaborated a pilot test that was answered by Portuguese teachers. The results verify the potential of these different approaches, opening up the possibility to use them in a teaching environment.

When compared with their single-agent counterpart, multi-agent systems have an additional set of challenges for reinforcement learning algorithms, including increased complexity, non-stationary environments, credit assignment, partial observability, and achieving coordination. Deep reinforcement learning has been shown to achieve successful policies through implicit coordination, but does not handle partial-observability. This paper describes a deep reinforcement learning algorithm, based on multi-agent actor-critic, that simultaneously learns action policies for each agent, and communication protocols that compensate for partial-observability and help enforce coordination. We also research the effects of noisy communication, where messages can be late, lost, noisy, or jumbled, and how that affects the learned policies. We show how agents are able to learn both high-level policies and complex communication protocols for several different partially-observable environments. We also show how our proposal outperforms other state-of-the-art algorithms that don't take advantage of communication, even with noisy communication channels.

Abstract Integration in the world of sport is one way for individuals with disabilities or motor disorders to feel more socially integrated, independent, and confident. Boccia is a Paralympic sport, which is increasingly getting more attention around the world. These facts have contributed to the objectives of this work. Including it in the serious games category enables to develop and rehabilitate the cognitive capabilities. The main focus was BC3 classification athletes (users with limited motor characteristics that require the use of an assistive device—a ramp, in this case). This paper describes a realistic Boccia game simulator adapted for people with disabilities that integrates a set of features that includes real physics and social features. These features can be used to enhance the interest of nonpractitioners of the sport and to improve the training conditions. The official Boccia regulation was added to the design of the simulator. The usability and approximation to the reality of the simulator were tested and validated based on the tests performed and data collected via a survey of users with no motor or psychological disorders. Realism and usability rating was almost excellent, and good results were achieved at the assessment of the game experience.

In recent years, the artificial intelligence community has taken big strides in the application of reinforcement learning to games or similar environments using deep learning. From Atari to board games, including motor control or riddle solving, fairly generic deep learning algorithms can now achieve great policies by simply learning to play from experience, and minimal knowledge of the specific domain. However, these algorithms are very demanding in terms of time and hardware in order to achieve the results reported in the literature. So much so, that some algorithms would take years to achieve state-of-the-art performance in commodity hardware. Not only that, but even the learning environments can hinder the speed of the learning process, if they have not been performance optimized. In this paper, we evaluate a complex existing environment, and propose a performance-oriented version, which we call GeoFriends2. We describe the motivation behind the creation of our version, and how it is suitable for both single- and multi-agent reinforcement learning. We then use Asynchronous Deep Learning to create complex policies that can act as baselines for future research on this environment. We also describe a set of techniques that speed up the learning process such that tests can be run with commodity hardware in hours, and not weeks, and using much simpler network architectures.

Biped robots have not achieved the efficient and harmonious locomotion of the human beings, capable of walking and running on unstructured terrains, with obstacles, holes and slopes. With this in mind, researchers started the development of biomimetic solutions to control the locomotion of biped models. This work presents a new solution of motion control of bipedal robots with adaptable stiffness, by exploring effects of joint stiffness in modulating walking behavior. Further, torque adjustment is achieved through a biomimetic controller that mimics and adjusts the natural dynamics of the robot to the environment. Specifically, the torque adjustment is made using AFOs (adaptive frequency oscillator) to generate the correct equilibrium positions that will be applied to the impedance control that computes the torque of each joint. Results show that the biped model is capable of walking in several types of terrain, including flat terrain, ramps, stairs and flat terrain with obstacles.

CMA-ES is one of the most popular stochastic search algorithms. It performs favourably in many tasks without the need of extensive parameter tuning. The algorithm has many beneficial properties, including automatic step-size adaptation, efficient covariance updates that incorporates the current samples as well as the evolution path and its invariance properties. Its update rules are composed of well established heuristics where the theoretical foundations of some of these rules are also well understood. In this paper we will fully derive all CMA-ES update rules within the framework of expectation-maximisation-based stochastic search algorithms using information-geometric trust regions. We show that the use of the trust region results in similar updates to CMA-ES for the mean and the covariance matrix while it allows for the derivation of an improved update rule for the step-size. Our new algorithm, Trust-Region Co-variance Matrix Adaptation Evolution Strategy (TR-CMA-ES) is fully derived from first order optimization principles and performs favourably in compare to standard CMA-ES algorithm.

Many stochastic search algorithms are designed to optimize a fixed objective function to learn a task, i.e., if the objective function changes slightly, for example, due to a change in the situation or context of the task, relearning is required to adapt to the new context. For instance, if we want to learn a kicking movement for a soccer robot, we have to relearn the movement for different ball locations. Such relearning is undesired as it is highly inefficient and many applications require a fast adaptation to a new context/situation. Therefore, we investigate contextual stochastic search algorithms that can learn multiple, similar tasks simultaneously. Current contextual stochastic search methods are based on policy search algorithms and suffer from premature convergence and the need for parameter tuning. In this paper, we extend the well known CMA-ES algorithm to the contextual setting and illustrate its performance on several contextual tasks. Our new algorithm, called contextual CMA-ES, leverages from contextual learning while it preserves all the features of standard CMA-ES such as stability, avoidance of premature convergence, step size control and a minimal amount of parameter tuning.

Stochastic search algorithms are black-box optimizer of an objective function. They have recently gained a lot of attention in operations research, machine learning and policy search of robot motor skills due to their ease of use and their generality. Yet, many stochastic search algorithms require relearning if the task or objective function changes slightly to adapt the solution to the new situation or the new context. In this paper, we consider the contextual stochastic search setup. Here, we want to find multiple good parameter vectors for multiple related tasks, where each task is described by a continuous context vector. Hence, the objective function might change slightly for each parameter vector evaluation of a task or context. Contextual algorithms have been investigated in the field of policy search, however, the search distribution typically uses a parametric model that is linear in the some hand-defined context features. Finding good context features is a challenging task, and hence, non-parametric methods are often preferred over their parametric counter-parts. In this paper, we propose a non-parametric contextual stochastic search algorithm that can learn a non-parametric search distribution for multiple tasks simultaneously. In difference to existing methods, our method can also learn a context dependent covariance matrix that guides the exploration of the search process. We illustrate its performance on several non-linear contextual tasks.

Stochastic search algorithms are general black-box optimizers. Due to their ease of use and their generality, they have recently also gained a lot of attention in operations research, machine learning and policy search. Yet, these algorithms require a lot of evaluations of the objective, scale poorly with the problem dimension, are affected by highly noisy objective functions and may converge prematurely. To alleviate these problems, we introduce a new surrogate-based stochastic search approach. We learn simple, quadratic surrogate models of the objective function. As the quality of such a quadratic approximation is limited, we do not greedily exploit the learned models. The algorithm can be misled by an inaccurate optimum introduced by the surrogate. Instead, we use information theoretic constraints to bound the `distance' between the new and old data distribution while maximizing the objective function. Additionally the new method is able to sustain the exploration of the search distribution to avoid premature convergence. We compare our method with state of art black-box optimization methods on standard uni-modal and multi-modal optimization functions, on simulated planar robot tasks and a complex robot ball throwing task.The proposed method considerably outperforms the existing approaches.

The creation of computer-based design tools to help teachers in designing learning scenarios, more precisely teaching-learning activities, has great importance in education. Those tools are most valuable if enriched with special features as, for example, templates, scripts or wizards used to guide the teacher through the design process. Recommendation mechanisms anchored in solid theoretical achievements are currently a huge challenge in the scientific research. This paper presents a proposal for teaching-learning techniques recommendation supported by an ontological modeling approach. The recommendation aims to assist educators in designing of teaching-learning activities. The recommendation process is part of the ACEM model which integrates an authoring design tool. We propose that the recommendation mechanism will help teachers preparing those activities and improving the use of different learning techniques.

In this paper we address the problem of musical genre recognition for a dancing robot with embedded microphones capable of distinguishing the genre of a musical piece while moving in a real-world scenario. For this purpose, we assess and compare two state-of-the-art musical genre recognition systems, based on Support Vector Machines and Markov Models, in the context of different real-world acoustic environments. In addition, we compare different preprocessing robot audition variants (single channel and separated signal from multiple channels) and test different acoustic models, learned a priori, to tackle multiple noise conditions of increasing complexity in the presence of noises of different natures (e.g., robot motion, speech). The results with six different musical genres suggest improved results, in the order of 43.6pp for the most complex conditions, when recurring to Sound Source Separation and acoustic models trained in similar conditions to the testing scenarios. A robot dance demonstration session confirms the applicability of the proposed integration for genre-adaptive dancing robots in real-world noisy environments.

How to improve the performance of a simulated soccer team using final game statistics? This is the question this research aims to answer using model-based collaborative techniques and a robotic team – FC Portugal – as a case study. After developing a

Intelligent wheelchairs can become an important solution to assist physically impaired individuals who find it difficult or impossible to drive regular powered wheelchairs. However, when designing the hardware architecture several projects compromise the user comfort and the wheelchair normal usability in order to solve robotic problems. In this paper we describe the main concepts regarding the design of the IntellWheels intelligent wheelchair. Our approach has a user-centered perspective, in which the needs and limitations of physically impaired users are given extensive attention at each stage of the design process. Finally, our design was evaluated through a public opinion assessment. A statistical analysis suggested that the design was effective to mitigate the visual and ergonomic impacts caused by the addition of sensorial and processing capabilities on the wheelchair.

The usage of data mining models has the main purpose of discovering new patterns from dataset analysis by extracting knowledge from data and converting it to information. The most challenging part of problem solving is not the generation of high number of instances in dataset, most often hard to understand, but the interpretation of all those instances to extrapolate information about it. Simulation of coastal ecosystems is used to replicate some real conditions related with physical, chemical and biological processes, and produces large datasets from which it could be deduced some information about attributes behaviors. This paper relates the use of Decision Tree models to analyze the growth of bivalve species in an ecosystem simulation. With a set of attributes that represents the water quality in certain modeled regions, the usage of Decision Tree is intended to identify the most significant attribute conditions, which could justify the growth behavior for each analyzed species. This approach aims the creation of new information about how water conditions should be to promote a healthy and fast growth of the analyzed species, being useful to know in which zones the bivalve should be seeded, and which are the conditions that aquaculture producers should afford to benefit the quality of its crops.

In a soccer match, a cooperative behavior emerges from the combined execution of simple actions by players. A cooperative behavior can be planned if players are previously committed to its execution prior to its start or unplanned otherwise. The ability to reproduce some of these behaviors can be useful to help a team achieve better performances. This work presents an approach to identify and extract cooperative behaviors that start from set-pieces and lead to a goal while ball possession is kept. The representation of these behaviors is abstracted using a set-play definition language to promote their reusability. A set of game log files generated with the FC Portugal team and collected from the RoboCup 2010 2D simulated soccer competition were analyzed. The results achieved showed that 25% of the total goals scored originated from set-pieces which attests to the importance of performing this analysis. Several guidelines for the definition of future set-plays were also inferred. In the future, these behaviors shall be tested to infer which are capable of neutralizing an opponent's team strategy and maximize the creation of goal opportunities.

In this paper we propose the integration of an online audio beat tracking system into the general framework of robot audition, to enable its application in musically-interactive robotic scenarios. To this purpose, we introduced a staterecovery mechanism into our beat tracking algorithm, for handling continuous musical stimuli, and applied different multi-channel preprocessing algorithms (e.g., beamforming, ego noise suppression) to enhance noisy auditory signals lively captured in a real environment. We assessed and compared the robustness of our audio beat tracker through a set of experimental setups, under different live acoustic conditions of incremental complexity. These included the presence of continuous musical stimuli, built of a set of concatenated musical pieces; the presence of noises of different natures (e.g., robot motion, speech); and the simultaneous processing of different audio sources on-the-fly, for music and speech. We successfully tackled all these challenging acoustic conditions and improved the beat tracking accuracy and reaction time to music transitions while simultaneously achieving robust automatic speech recognition.

In this paper we propose a general active audition framework for auditory-driven Human-Robot Interaction (HRI). The proposed framework simultaneously processes speech and music on-the-fly, integrates perceptual models for robot audition, and supports verbal and non-verbal interactive communication by means of (pro)active behaviors. To ensure a reliable interaction, on top of the framework a behavior decision mechanism based on active audition policies the robot's actions according to the reliability of the acoustic signals for auditory processing. To validate the framework's application to general auditory-driven HRI, we propose the implementation of an interactive robot dancing system. This system integrates three preprocessing robot audition modules: sound source localization, sound source separation, and ego noise suppression; two modules for auditory perception: live audio beat tracking and automatic speech recognition; and multi-modal behaviors for verbal and non-verbal interaction: music-driven dancing and speech-driven dialoguing. To fully assess the system, we set up experimental and interactive real-world scenarios with highly dynamic acoustic conditions, and defined a set of evaluation criteria. The experimental tests revealed accurate and robust beat tracking and speech recognition, and convincing dance beat-synchrony. The interactive sessions confirmed the fundamental role of the behavior decision mechanism for actively maintaining a robust and natural human-robot interaction.

In this paper we propose an audio beat tracking system, IBT, for multiple applications. The proposed system integrates an automatic monitoring and state recovery mechanism, that applies (re-)inductions of tempo and beats, on a multi-agent-based beat tracking architecture. This system sequentially processes a continuous onset detection function while propagating parallel hypotheses of tempo and beats. Beats can be predicted in a causal or in a non-causal usage mode, which makes the system suitable for diverse applications. We evaluate the performance of the system in both modes on two application scenarios: standard (using a relatively large database of audio clips) and streaming (using long audio streams made up of concatenated clips). We show experimental evidence of the usefulness of the automatic monitoring and state recovery mechanism in the streaming scenario (i.e., improvements in beat tracking accuracy and reaction time). We also show that the system performs efficiently and at a level comparable to state-of-the-art algorithms in the standard scenario. IBT is multi-platform, open-source and freely available, and it includes plugins for different popular audio analysis, synthesis and visualization platforms.

This paper presents the architecture that supports the collaborative model ACEM (Advanced Collaborative Educational Model) to assist educators in the collaborative design of learning activities, supported by a high-level graphical tool. ACEM embraces the research areas of Computer Supported Cooperative Work (CSCW) and Learning Design (LD). Some facilities are considered in order to implement the online interactions between educators, namely a shared whiteboard and a conversation room. A workflow descriptive model of the educators' teamwork is also introduced.

The generation of three-dimensional models of urban environments using procedural modelling is presented as being a solution that allows financial and temporal gains, maintaining an acceptable visual fidelity level. Nevertheless, the modelling of anchor buildings (or monumental), identifying certain urban areas, needs a more careful modelling, due to the elevated level of detail necessary, using, generally, manual modelling. This paper proposes a data model to be used on an architecture for high level modelling of monumental buildings, through the introduction of additional knowledge from textual information. The firsts results shows that the data model is flexible enough to build distinct models of churches and other buildings. This data model also provides a high level three-dimensional model that can be used by other procedural modelling solutions to create detailed monumental buildings. The results also demonstrate that it is possible to use simple text to create a rudimentary 3D model, showing the way to further investigation, allowing non-specialized users to increase effectiveness using a procedural modelling system.

Simulating ecological models is always a difficult task, not only because of its complexity but also due to the slowness associated with each simulation run as more variables and processes are incorporated into the complex ecosystem model. The computational overhead becomes a very important limitation for model calibration and scenario analysis, due to the large number of model runs generally required. This paper presents a framework for ecological simulations that intends to increase system performance through the ability to do parallel simulations, allowing the joint analysis of different scenarios. This framework evolved from the usage of one simulator and several agents, that configure the simulator to run specific scenarios, related to possible ecosystem management options, one at a time, to the use of several simulators, each one simulating a different scenario concurrently, speeding up the process and reducing the time for decision between the alternative scenarios proposed by the agents. This approach was tested with a farmer agent that seeks optimal combinations of bivalve seeding areas in a large mariculture region, maximizing the production without exceeding the total allowed seeding area. Results obtained showed that the time needed to acquire a "near" optimal solution decreases proportionally with the number of simulators in the network, improving the performance of the agent's optimization process, without compromising its rationality. This work is a step forward towards an agent based decision support system to optimize complex environmental problems.

Over the last few years, research in Artificial Intelligence has focussed on games with incomplete information and non-deterministic moves. The game of Poker is a perfect theme for studying this subject. The best known Poker variant is Texas Hold'em that combines simple rules with a huge amount of possible playing strategies. This paper is focussed on developing algorithms for performing simple online opponent modelling in Texas Hold'em Poker enabling to select the best strategy to play against each given opponent. Several autonomous agents were developed in order to simulate typical Poker player's behaviour and an observer agent was developed, capable of using simple opponent modelling techniques, in order to select the best playing strategy against each opponent. The results obtained in realistic experiments using eight distinct poker playing agents showed the usefulness of the approach. The observer agent is clearly capable of outperforming all their counterparts in all tests performed.

The paper presents the architecture and algorithms developed for Dom Dinis, a simple compass-guided robot built by the authors. This includes environment exploration, task planning and task execution. Environment exploration, based on repeating a reactive goal search, enables a progressive construction of a grid based map. Based on the (possibly incomplete) map, the robot is able to plan its tasks. The execution capabilities of the robot include exception handling. Essential to all these capabilities is the knowledge of the robot's position in the world. The position is computed based on tracking traversed distances and followed orientations. Orientation is given by a compass. Dom Dinis does not use wheel encoders at all.