Learning from natural instructions

Machine learning is traditionally formalized and investigated as the study of learning concepts and decision functions from labeled examples, requiring a representation that encodes information about the domain of the decision function to be learned. We are interested in providing a way for a human teacher to interact with an automated learner using natural instructions, thus allowing the teacher to communicate the relevant domain expertise to the learner without necessarily knowing anything about the internal representations used in the learning process. In this paper we suggest to view the process of learning a decision function as a natural language lesson interpretation problem, as opposed to learning from labeled examples. This view of machine learning is motivated by human learning processes, in which the learner is given a lesson describing the target concept directly and a few instances exemplifying it. We introduce a learning algorithm for the lesson interpretation problem that receives feedback from its performance on the final task, while learning jointly (1) how to interpret the lesson and (2) how to use this interpretation to do well on the final task. traditional machine learning by focusing on supplying the learner only with information that can be provided by a task expert. We evaluate our approach by applying it to the rules of the solitaire card game. We show that our learning approach can eventually use natural language instructions to learn the target concept and play the game legally. Furthermore, we show that the learned semantic interpreter also generalizes to previously unseen instructions.     

Creating Decision Trees from Rules using RBDT‐1

Most of the methods that generate decision trees for a specific problem use the examples of data instances in the decision tree–generation process. This article proposes a method called RBDT‐1—rule‐based decision tree—for learning a decision tree from a set of decision rules that cover the data instances rather than from the data instances themselves. The goal is to create on demand a short and accurate decision tree from a stable or dynamically changing set of rules. The rules could be generated by an expert, by an inductive rule learning program that induces decision rules from the examples of decision instances such as AQ‐type rule induction programs, or extracted from a tree generated by another method, such as the ID3 or C4.5. In terms of tree complexity (number of nodes and leaves in the decision tree), RBDT‐1 compares favorably with AQDT‐1 and AQDT‐2, which are methods that create decision trees from rules. RBDT‐1 also compares favorably with ID3 while it is as effective as C4.5 where both (ID3 and C4.5) are well‐known methods that generate decision trees from data examples. Experiments show that the classification accuracies of the decision trees produced by all methods under comparison are indistinguishable.     

Challenging games help students learn: An empirical study on engagement,flow and immersion in game-based learning

In this paper, we investigate the impact of flow (operationalized as heightened challenge and skill), engagement, and immersion on learning in game-based learning environments. The data was gathered through a survey from players (N = 173) of two learning games (Quantum Spectre: N = 134 and Spumone: N = 40). The results show that engagement in the game has a clear positive effect on learning, however, we did not find a significant effect between immersion in the game and learning. Challenge of the game had a positive effect on learning both directly and via the increased engagement. Being skilled in the game did not affect learning directly but by increasing engagement in the game. Both the challenge of the game and being skilled in the game had a positive effect on both being engaged and immersed in the game. The challenge in the game was an especially strong predictor of learning outcomes. For the design of educational games, the results suggest that the challenge of the game should be able to keep up with the learners growing abilities and learning in order to endorse continued learning in game-based learning environments.     

Inducing decision trees with an ant colony optimization algorithm

Decision trees have been widely used in data mining and machine learning as a comprehensible knowledge representation. While ant colony optimization (ACO) algorithms have been successfully applied to extract classification rules, decision tree induction with ACO algorithms remains an almost unexplored research area. In this paper we propose a novel ACO algorithm to induce decision trees, combining commonly used strategies from both traditional decision tree induction algorithms and ACO. The proposed algorithm is compared against three decision tree induction algorithms, namely C4.5, CART and cACDT, in 22 publicly available data sets. The results show that the predictive accuracy of the proposed algorithm is statistically significantly higher than the accuracy of both C4.5 and CART, which are well-known conventional algorithms for decision tree induction, and the accuracy of the ACO-based cACDT decision tree algorithm.     

Serious games and learning effectiveness: The case of It’s a Deal!

Although the value of serious games in education is undeniable and the potential benefits of using video games as ideal companions to classroom instruction is unquestionable, there is still little consensus on the game features supporting learning effectiveness, the process by which games engage learners, and the types of learning outcomes that can be achieved through game play. Our aim in this discussion is precisely to advance in this direction by providing evidence of some of the factors influencing the learning effectiveness of a serious game called It’s a Deal! This serious game was created for the purpose of teaching intercultural business communication between Spaniards and Britons in business settings in which English is used as the lingua franca. This paper hypothesizes that the immersive, all-embracing and interactive learning environment provided by the video game to its users may contribute to develop and enhance their intercultural communicative competence. The study attempts to answer three main research questions: (a) after playing It’s a Deal!, did the students sampled improve their intercultural awareness, intercultural knowledge and intercultural communicative competence in business English? (b) If they improved their intercultural learning, what are the factors influencing such improvement? And (c) if they did not improve their intercultural learning, what are the factors influencing such failure? The game participants who volunteered to take part in the study were all students of English Studies at the University of Alicante in the academic year 2010-2011. One hundred and six students completed both the pre-test and the post-test questionnaires, and played It’s a Deal! A sample of fifty students was selected randomly for the empirical study. The results obtained in the tests performed were compared and contrasted intra-group, both qualitatively and quantitatively, for the purpose of finding any statistically significant difference that may confirm whether or not there was an improvement in the students’ intercultural communicative competence in business English as a result of the implementation of the It’s a Deal! serious game. Findings of this study demonstrate that the video game is an effective learning tool for the teaching of intercultural communication between Spaniards and Britons in business settings in which English is used as the lingua franca. In particular, whereas the game had a small learning effect on intercultural awareness and a medium learning effect on intercultural knowledge, it had a large learning effect on intercultural communicative competence. The study also documents correlating factors that make serious games effective, since it shows that the learning effectiveness of It’s a Deal! stems from the correct balance of the different dimensions involved in the creation of serious games, specifically instructional content, game dimensions, game cycle, debriefing, perceived educational value, transfer of learnt skills and intrinsic motivation.     

全局游戏策略中基于经验的学习方法

全局游戏策略GGP(General Game Playing)旨在开发一种没有游戏经验支撑下能够精通各类游戏的人工智能。在原有强化学习算法研究的基础上,提出一种基于经验的简化学习方法,通过对游戏状态的筛选和游戏经验的归纳,从而降低决策对经验数量的需求,提高决策效率,并能达到指定胜利、平局或失败的游戏目标。通过在三种不同的游戏规则下与玩家进行游戏比赛实验表明,该学习方法能有效地达到预期结果。     

World of Warcraft and the impact of game culture and play in an undergraduate game design course

During the past two decades, digital games have become an increasingly popular source of study for academics, educational researchers and instructional designers. Much has been written about the potential of games for teaching and learning, both in the design of educational/serious games and the implementation of off-the-shelf games for learning. Yet relatively little research has been conducted about how game culture and the enmeshed practice of play may impact classroom dynamics. The purpose of this study is to present a case study about how the use of World of Warcraft (WoW) as a teaching tool and medium of play impacted class dynamics in an undergraduate university-level course for game design. Specifically, this study will address how WoW’s game culture and the practice of play impacted (a) student-to-student dynamics and (b) class dynamics. The goal of this study is to explore some of the dynamics of play as a component of learning.     

Using J-pruning to reduce overfitting in classification trees

The automatic induction of classification rules from examples in the form of a decision tree is an important technique used in data mining. One of the problems encountered is the overfitting of rules to training data. In some cases this can lead to an excessively large number of rules, many of which have very little predictive value for unseen data. This paper is concerned with the reduction of overfitting during decision tree generation. It introduces a technique known as J-pruning, based on the J-measure, an information theoretic means of quantifying the information content of a rule.     

Learning through playing Virtual Age: Exploring the interactions among student concept learning,gaming performance,in-game behaviors,and the use of in-game characters

Video games possess many unique features that facilitate learning. Meanwhile, teaching about evolution is never an easy task due to the existence of some barriers to its learning. Virtual Age, therefore, has been developed in an attempt to harness the power of gaming to increase student understanding of biological evolution. The aim of this study was to examine whether Virtual Age is effective for learning about evolution and to further explore the interplay of student concept learning, gaming performance, and in-game behaviors. A total of 62 7th graders took part in the study, and significant findings were revealed. The students did learn by playing Virtual Age, and their long-term knowledge retention was promising. The in-game behaviors, such as times and duration of viewing the relevant information embedded in Virtual Age, were significantly related to gaming performance (game score), which subsequently influenced learning outcomes. Moreover, the results of cluster analysis indicated that three clusters of low learning outcomes/low gaming performance, high learning outcomes, and high gaming performance emerged. Overall, Virtual Age is an effective game for learning about evolution based on its sound and sophisticated design. Implications derived from the study and suggestions for future work are proposed.     

A novel modular Q-learning architecture to improve performance under incomplete learning in a grid soccer game

Multi-agent reinforcement learning methods suffer from several deficiencies that are rooted in the large state space of multi-agent environments. This paper tackles two deficiencies of multi-agent reinforcement learning methods: their slow learning rate, and low quality decision-making in early stages of learning. The proposed methods are applied in a grid-world soccer game. In the proposed approach, modular reinforcement learning is applied to reduce the state space of the learning agents from exponential to linear in terms of the number of agents. The modular model proposed here includes two new modules, a partial-module and a single-module. These two new modules are effective for increasing the speed of learning in a soccer game. We also apply the instance-based learning concepts, to choose proper actions in states that are not experienced adequately during learning. The key idea is to use neighbouring states that have been explored sufficiently during the learning phase. The results of experiments in a grid-soccer game environment show that our proposed methods produce a higher average reward compared to the situation where the proposed method is not applied to the modular structure.     

MGRS: A multi-granulation rough set

The original rough set model was developed by Pawlak, which is mainly concerned with the approximation of sets described by a single binary relation on the universe. In the view of granular computing, the classical rough set theory is established through a single granulation. This paper extends Pawlak’s rough set model to a multi-granulation rough set model (MGRS), where the set approximations are defined by using multi equivalence relations on the universe. A number of important properties of MGRS are obtained. It is shown that some of the properties of Pawlak’s rough set theory are special instances of those of MGRS.Moreover, several important measures, such as accuracy measureα, quality of approximationγ and precision of approximationπ, are presented, which are re-interpreted in terms of a classic measure based on sets, the Marczewski-Steinhaus metric and the inclusion degree measure. A concept of approximation reduct is introduced to describe the smallest attribute subset that preserves the lower approximation and upper approximation of all decision classes in MGRS as well. Finally, we discuss how to extract decision rules using MGRS. Unlike the decision rules (“AND” rules) from Pawlak’s rough set model, the form of decision rules in MGRS is “OR”. Several pivotal algorithms are also designed, which are helpful for applying this theory to practical issues. The multi-granulation rough set model provides an effective approach for problem solving in the context of multi granulations.     

Fuzzy signaling game of deception between ant-inspired deceptive robots with interactive learning

In this study the robotic deception phenomenon is raised in the framework of a signaling game which utilizes fuzzy logic and game theory along with inspirations from nature. Accomplishing the fuzzy signaling strategy set for deceptive players serves as a great part of our contribution and on this aim, hierarchical fuzzy inference systems support receiver’s actions and sender’s ant-inspired deceptive signals (track and pheromone). In addition, special deceptive robots and visually-supported experimental environment are also provided. The fuzzy behavior of robots defines the strategy type of players. The final result of deception process depends on this strategy type which leads to proposing a payoff matrix in which each cell of mutual costs is defined with special supporting logic related to our deception game with pursuit–evasion applications. Furthermore, motivated by animal signaling, through applying mixed strategies on deceiver’s honesty level and rival’s trust level, the corresponding learning dynamics are investigated and the conceptual discussion put forward serves as a proof to the smart human-like behavior that occurs between the robots: the interactive learning. Simulation results show that robots are capable of interactive learning within deceptive interaction and finally change their strategies to adopt themselves to new situation occurred due to opponent’s strategy change. Because of repetitive change in strategies as a result of learning, the conditions of a persistent deception without breakdown holds for this game where deceiver can frequently benefit from deception without leaving rival to lose its trust totally. The change in strategy will happen after a short time needed to learn the new situation. In rival’s learning process, this short time, which we call the ignorance time, exactly is the period that deceiver can benefit from deception while its evil intends are still concealed. Moreover, in this study an algorithm is given for the proposed signaling game of deception and an illustrative experiment in the introduced experimental environment demonstrates the process of a successful deception. The paper also gives solution to the proposed game by analyzing mixed Nash equilibrium which turns out to be the interior center fixed point of the learning dynamics.     

A rule-based group decision model for warehouse evaluation under interval-valued Intuitionistic fuzzy environments

We consider the issue of warehouse evaluation towards successful logistic and supply chain management. Suppose a company has managed a chain of owned warehouses, and now this company is in need of acquiring some new and profitable warehouse adding to its operation chain. A key business decisions here is how to choose the most profitable warehouses from a number of potential warehouses. In reality, the challenge is that the future profitability is unpredictable. Therefore, it is infeasible to rank potential warehouses directly for choice. To address such a problem, this paper proposes a new rule-based decision model. This model includes the following characteristics: (i) decision information is provided via interval-valued intuitionistic fuzzy values; (ii) multiple experts as a group of decision makers are involved; (iii) both subjective evaluations from experts and objective data of historical profitability are employed; (iv) both certain and uncertain information are exploited. The core decision mechanism is, making use of uncertain information of owned warehouses, to induce a collection of “if…then…”rules, and subsequently to exploit these rules for prediction of preference orders of all potential warehouses. Therein, we develop and integrate multiple techniques for the purposes of (a) aggregation of uncertain information; (b) construction of pairwise comparison; (c) induction of certain and uncertain rules; and (d) decision rules exploitation. We finally elaborate our discussion with a numerical example illustrating the application of the proposed decision mechanism to supply-chain domain problems.     

Evaluation of an electronic video game for improvement of balance

Virtual environments have been investigated for fitness and medical rehabilitation. In this study, the Sony EyeToy ^? and PlayStation 2 ^? were used with the AntiGrav? game to evaluate their potential for improving postural balance. The game required lateral head, body, and arm movements. The performance on balance tests of subjects who trained for 3?weeks with this game was compared to the performance of controls who were not trained. Training subjects showed improvement for two of the three tests (each testing a different facet of balance), suggesting specificity of training, while control subjects did not show significant improvement on any test. Simulator sickness questionnaire results showed a variety of mild symptoms, which decreased over the training sessions. Motor learning analysis of the game scores showed that mastery had been achieved on the easier level in the game, but not on the second level of difficulty. This reflects the potential for continued learning and training through advanced levels within a game. A model parameter using the time constants of game score improvement was developed, which could be used to quantify the difficulty for any video game design. The results suggest that this video game could be used for some aspects of balance training.     

An adaptive rule-based classifier for mining big biological data

In this paper, we introduce a new adaptive rule-based classifier for multi-class classification of biological data, where several problems of classifying biological data are addressed: overfitting, noisy instances and class-imbalance data. It is well known that rules are interesting way for representing data in a human interpretable way. The proposed rule-based classifier combines the random subspace and boosting approaches with ensemble of decision trees to construct a set of classification rules without involving global optimisation. The classifier considers random subspace approach to avoid overfitting, boosting approach for classifying noisy instances and ensemble of decision trees to deal with class-imbalance problem. The classifier uses two popular classification techniques: decision tree and k-nearest-neighbor algorithms. Decision trees are used for evolving classification rules from the training data, while k-nearest-neighbor is used for analysing the misclassified instances and removing vagueness between the contradictory rules. It considers a series of k iterations to develop a set of classification rules from the training data and pays more attention to the misclassified instances in the next iteration by giving it a boosting flavour. This paper particularly focuses to come up with an optimal ensemble classifier that will help for improving the prediction accuracy of DNA variant identification and classification task. The performance of proposed classifier is tested with compared to well-approved existing machine learning and data mining algorithms on genomic data (148 Exome data sets) of Brugada syndrome and 10 real benchmark life sciences data sets from the UCI (University of California, Irvine) machine learning repository. The experimental results indicate that the proposed classifier has exemplary classification accuracy on different types of biological data. Overall, the proposed classifier offers good prediction accuracy to new DNA variants classification where noisy and misclassified variants are optimised to increase test performance.     

Scaffolding game-based learning: Impact on learning achievements,perceived learning,and game experiences

One of the central challenges of integrating game-based learning in school settings is helping learners make the connections between the knowledge learned in the game and the knowledge learned at school, while maintaining a high level of engagement with game narrative and gameplay. The current study evaluated the effect of supplementing a business simulation game with an external conceptual scaffold, which introduces formal knowledge representations, on learners' ability to solve financial-mathematical word problems following the game, and on learners' perceptions regarding learning, flow, and enjoyment in the game. Participants (M_age = 10.10 years) were randomly assigned to three experimental conditions: a “study and play” condition that presented the scaffold first and then the game, a “play and study” condition, and a “play only” condition. Although no significant gains in problem-solving were found following the intervention, learners who studied with the external scaffold before the game performed significantly better in the post-game problem-solving assessment. Adding the external scaffold before the game reduced learners' perceived learning. However, the scaffold did not have a negative impact on reported flow and enjoyment. Flow was found to significantly predict perceived learning and enjoyment. Yet, perceived learning and enjoyment did not predict problem-solving and flow directly predicted problem solving only in the “play and study” condition. We suggest that presenting the scaffold may have “problematized” learners' understandings of the game by connecting them to disciplinary knowledge. Implications for the design of scaffolds for game-based learning are discussed.