Table 4 - uploaded by Zahid Halim
Content may be subject to copyright.
Source publication
Games have always been a popular test bed for artificial intelligence techniques. Game developers are always in constant search for techniques that can automatically create computer games minimizing the developer's task. In this work we present an evolutionary strategy based solution towards the automatic generation of two player board games. To gu...
Similar publications
Problem statement: The retail sales of computer and video games have grown enormously
during the last few years, not just in United States (US), but also all over the world. This is the reason a
lot of game developers and academic researchers have focused on game related technologies, such as
graphics, audio, physics and Artificial Intelligence (AI...
The paper describes two computer games which we developed to teach abstract physics concepts: A Slower Speed of Light , designed to teach Special Relativity, and Kirchhoff’s Revenge , designed to teach circuit laws. We report on experiences and research results using these games in physics lessons at college and high school level, user feedback, as...
With computers becoming ubiquitous and high resolution graphics reaching the next level, computer games have become a major source of entertainment. It has been a tedious task for game developers to measure the entertainment value of the computer games. The entertainment value of a game does depend upon the genre of the game in addition to the game...
![Fig. 1. The OCC model (source: [2])](profile/Adam-Wojciechowski-3/publication/324263019/figure/fig47/AS:612515193053191@1523046410697/The-OCC-model-source-2_Q320.jpg)
The "Computer Game Innovations" series is an international forum designed to enable the exchange of knowledge and expertise in the field of video game development. Comprising both academic research and industrial needs, the series aims at advancing innovative industry-academia collaboration. The monograph provides a unique set of articles presentin...
We propose a new approach to assist computer game creators in introducing AI agent-players into their games. We point out that traditional methods, such as Monte Carlo Tree Search (MCTS), may not provide creators with good interfaces to embed the required AI elements because of too fine-grained space of (often loosely defined) game states. Thus, we...
Citations
... The market size is one of the essential items in the checklist of a high-tech firms' assessment (Yoo et al., 2012) as it is the intensity of competition between start-up firms within the same industry (Santisteban and Mauricio, 2017;Song et al., 2008). Start-up success in the new media industry heavily depends on technology and market uncertainty within the commercialisation stage (Yoo et al., 2012) as well as entertainment metrics (Halim et al., 2014;Iida et al., 2003). ...
The survival of early-stage companies is uncertain. Their survival and success are often linked to obtaining a substantial financial investment. This in-depth case study examines how and why an early-stage start-up from the video game industry obtains investment from an independent equity investor and offers deeper and more comprehensive insights into the success mechanism of gaming start-ups. The study: 1) generally confirms the previous findings that certain theoretical factors increase the likelihood of success; 2) argues that accumulating these factors, rather than their presence, is essential for a start-up to be substantially funded; 3) shows that the expected market response is crucial and significantly outweighs the other factors; 4) conceptualise the reasons for start-ups' success as a reduction of two main types of risk development risk and sales risk; 5) confirms the catalytic role of crowdfunding in obtaining significant equity funding.
... Unlikely the other competing methods, the proposed mutation operation produces diverse chromosomes each time. Crossover and mutation operators in GA are used for the search space exploration [43][44][45][46]. After evaluating various rates for crossover and mutation, crossover rate of 0.6 and mutation probability of 0.2 provides balanced exploration and exploitation scheme. ...
Task schedule optimization enables to attain high performance in both homogeneous and heterogeneous computing environments. The primary objective of task scheduling is to minimize the execution time of an application graph. However, this is an NP-complete (non-deterministic polynomial) undertaking. Additionally, task scheduling is a challenging problem due to the heterogeneity in the modern computing systems in terms of both computation and communication costs. An application can be considered as a task graph represented using Directed Acyclic Graphs (DAG). Due to the heterogeneous system, each task has different execution time on different processors. The primary concern in this problem domain is to reduce the schedule length with minimum complexity of the scheduling procedure. This work presents a couple of hybrid heuristics, based on a list and guided random search to address this concern. The proposed heuristic, i.e., Hybrid Heuristic and Genetic-based Task Scheduling Algorithm for Heterogeneous Computing (HHG) uses Genetic Algorithm and a list-based approach. This work also presents another heuristic, namely, Hybrid Task Duplication, and Genetic-based Task Scheduling Algorithm for Heterogeneous Computing (HTDG). The present work improves the quality of initial GA population by inducing two diverse guided chromosomes. The proposal is compared with four state-of-the-art methods, including two evolutionary algorithms for the same task, i.e., New Genetic Algorithm (NGA) and Enhanced Genetic Algorithm for Task Scheduling (EGA-TS), and two list-based algorithms, i.e., Heterogeneous Earliest Finish Time (HEFT), and Predict Earliest Finish Time (PEFT). Results show that the proposed solution performs better than its counterparts based on occurrences of the best result, average makespan, average schedule length ratio, average speedup, and the average running time. HTDG yields 89% better results and HHG demonstrates 56% better results in comparisons to the four state-of-the-art task scheduling algorithms.
... In our framework, games undergo selection according to players' institutional preferences-the abstract values and qualities they look for in a social system. Institutional preferences fit within a broader academic interest in human preferences over games, culture, norms and language [31][32][33]. Institutional preferences have attracted specific interest with theories such as Binmore's, that the processes of cultural evolution select for institutions with the features of stability, efficiency and fairness, in that order [34]. Illustrating the potential for applications to policy, researchers have also elicited communities' preferences for the features of local resource management systems [35,36]. ...
Understanding human institutions, animal cultures and other social systems requires flexible formalisms that describe how their members change them from within. We introduce a framework for modelling how agents change the games they participate in. We contrast this between-game ‘institutional evolution’ with the more familiar within-game ‘behavioural evolution’. We model institutional change by following small numbers of persistent agents as they select and play a changing series of games. Starting from an initial game, a group of agents trace trajectories through game space by navigating to increasingly preferable games until they converge on ‘attractor’ games. Agents use their ‘institutional preferences' for game features (such as stability, fairness and efficiency) to choose between neighbouring games. We use this framework to pose a pressing question: what kinds of games does institutional evolution select for; what is in the attractors? After computing institutional change trajectories over the two-player space, we find that attractors have disproportionately fair outcomes, even though the agents who produce them are strictly self-interested and indifferent to fairness. This seems to occur because game fairness co-occurs with the self-serving features these agents do actually prefer. We thus present institutional evolution as a mechanism for encouraging the spontaneous emergence of cooperation among small groups of inherently selfish agents, without space, reputation, repetition, or other more familiar mechanisms. Game space trajectories provide a flexible, testable formalism for modelling the interdependencies of behavioural and institutional evolutionary processes, as well as a mechanism for the evolution of cooperation.
... In our framework, the selection pressures driving institutional evolution are players' institutional preferences-the values and qualities agents look for in a social system. Institutional preferences fit within a broader academic interest in human preferences between social constructions such as games, culture, norms, and language (9)(10)(11). Institutional preferences have attracted specific interest with theories such as Binmore's, that the processes of cultural evolution select for institutions with the features of stability, efficiency, and fairness, in that order (12). ...
Understanding the evolution of human social systems requires flexible formalisms for the emergence of institutions. Although game theory is normally used to model interactions individually, larger spaces of games can be helpful for modeling how interactions change. We introduce a framework for modeling "institutional evolution," how individuals change the games they are placed in. We contrast this with the more familiar within-game "behavioral evolution". Starting from an initial game, agents trace trajectories through game space by repeatedly navigating to more preferable games until they converge on attractor games that are preferred to all others. Agents choose between games on the basis of their "institutional preferences," which define between-game comparisons in terms of game-level features such as stability, fairness, and efficiency. Computing institutional change trajectories over the two-player space, we find that the attractors of self-interested economic agents over-represent fairness by 100% relative to baseline, even though those agents are indifferent to fairness. This seems to occur because fairness, as a game feature, co-occurs with the self-serving features these agents do prefer. We thus present institutional evolution as a mechanism for encouraging the spontaneous emergence of cooperation among inherently selfish agents. We then extend these findings beyond two players, and to two other types of evolutionary agent: the relative fitness maximizing agent of evolutionary game theory (who maximizes inequality), and the relative group fitness maximizing agent of multi-level/group selection theory (who minimizes inequality). This work provides a flexible, testable formalism for modeling the interdependencies of behavioral and institutional evolutionary processes.
... AI agents can also model different levels of skill, e.g., by using Monte Carlo Tree Search (MCTS) with limited computing budget [97,46]. Researchers have also emulated human-like imprecision, limited reaction time, and [11] "player modeling" and "competence" 132 [51,55,11] "player modeling" and "autonomy" 147 [11] "player modeling" and "relatedness" 23 [11] "player modeling" and "curiosity" 136 [27,11,53,65,25] "artificial intelligence" and "game testing" and "intrinsic motivation" ...
... Halim et al. [27] estimate the entertainment value of a game level using an inverted U mapping of the game's learnability, favoring not too hard and not too easy levels. Learnability is evaluated as the number of iterations of genetic optimization it takes to train an agent to win the game against other agents. ...
... On the other hand, the first principle of Schmidhuber's theory of artificial curiosity is that one should generate a curiosity reward based on exactly the same signal [80]. Further, Halim et al. [27] frame their work as motivated by Schmidhuber's theory [80], while their actual computational appraisal (i.e., learning time) is more related to competence/challenge. ...
This paper presents a review of intrinsic motivation in player modeling, with a focus on simulation-based game testing. Modern AI agents can learn to win many games; from a game testing perspective, a remaining research problem is how to model the aspects of human player behavior not explained by purely rational and goal-driven decision making. A major piece of this puzzle is constituted by intrinsic motivations, i.e., psychological needs that drive behavior without extrinsic reinforcement such as game score. We first review the common intrinsic motivations discussed in player psychology research and artificial intelligence, and then proceed to systematically review how the various motivations have been implemented in simulated player agents. Our work reveals that although motivations such as competence and curiosity have been studied in AI, work on utilizing them in simulation-based game testing is sparse, and other motivations such as social relatedness, immersion, and domination appear particularly underexplored.
... For example, Coller et al. [17] utilize video games to teach mechanical engineering students numerical methods, Koenig et al. [18] use gaming environment for patient rehabilitation, and Drettakis et al. [19] employ gaming environments for architecture and urban planning. There are many genres of games available, including predator/prey games, platform games, board-based games and real-time strategy games (RTS) [20]. Among these, RTS games provide a real-world-like environment and scenarios. ...
Computer games provide an ideal test bed to collect and study data related to human behavior using a virtual environment having real-world-like features. Studies regarding individual players' actions in a gaming session and how this correlates with their real-life personality have the potential to reveal great insights in the field of affective computing. This study profiles players using data collected from strategy games. This is done by taking into account the gameplay and the associations between the personality traits and the subjects playing the game. This study uses two benchmark strategy game datasets, namely, StarCraft and World of Warcraft. In addition, the study also uses the Age of Empire-II game data, collected using 50 participants. The IPIP-NEO-120 personality test is conducted using these participants to evaluate them on the Big-Five personality traits. The three datasets are profiled using four clustering techniques. The results identify two clusters in each of these datasets. The quality of cluster formation is also evaluated through the cluster evaluation indices. Using the clustering results, the classifiers are then trained to classify a player, after a gameplay, into one of the two profiles. Results show that the gameplay can be used to predict various personality features using strategy game data.
... Cardamone et al. 22 present an online tool to generation tracks for two open-source 3D car racing games. Halim et al. 23 present an automated approach for board based games creation using an evolutionary algorithm. Other somewhat related studies using same technique are covered in 9,10,24,25,26 . ...
Computer games are becoming a primary source of entertainment due to modern day computers and high resolution graphics. Game developers are now getting interested to quantitatively measure the entertainment value of games. In addition to its contents, entertainment value also depends on the particular genre of the game. In this work we introduce a set of entertainment metrics for the predator/prey genre of games. Further, we employ the proposed metrics for automatic generation of entertaining games using an evolutionary algorithm. The evolutionary algorithm starts with an initial set of randomly generated games and is guided by the entertainment metrics towards more entertaining population of games. The results produced are counter-checked against the entertainment criteria of human by conducting a human user survey and a controller learning ability experiment. The proposed system serves as an expert system, based on computational intelligence techniques, for automatic generation of entertaining games.
... With the advances in speed of computer processing and graphics tools, new and powerful visualization techniques are being developed. These contemporary techniques may be applied in data mining, knowledge discovery applications, and other tasks [7,61]. ...
... Its functionality is different from traditional digital computers and works in parallel as in case of human neurons [44,45]. ANNs have applications in diverse areas such as engineering, medical, computer games [61], and banking for classification and control [30]. A neural network identifies and learns from the patterns presented to the network in the form of input and corresponding target output. ...
Advances in computing technology have been instrumental in creating an assortment of powerful information visualization techniques. However, the selection of a suitable and effective visualization technique for a specific dataset and a data mining task is not trivial. This work automatically selects an appropriate visualization technique based on the given metadata and the task that a user intends to perform. The appropriate visualization is predicted based on an artificial neural network (ANN)-based model which classifies the input data into one of the eight predefined classes. A purpose built dataset extracted from the existing knowledge in the discipline is utilized to train the neural network. The dataset covers eight visualization techniques, including: histogram, line chart, pie chart, scatter plot, parallel coordinates, map, treemap, and linked graph. Various architectures using different numbers of hidden units, hidden layers, and input and output data formats have been evaluated to find the optimal neural network architecture. The performance of neural networks is measured using: confusion matrix, accuracy, precision, and sensitivity of the classification. Optimal neural network architecture is determined by convergence time and number of iterations. The results obtained from the best ANN architecture are compared with five other classifiers, k-nearest neighbor, naïve Bayes, decision tree, random forest, and support vector machine. The proposed system outperforms four classifiers in terms of accuracy and all five classifiers based on execution time. The trained neural network is also tested on twenty real-world benchmark datasets, where the proposed approach also provides two alternate visualizations, in addition to the most suitable one, for a particular dataset. A qualitative comparison with the state-of-the-art approaches is also presented. The results show that the proposed technique assists in selecting an appropriate visualization technique for a given dataset with high accuracy.
... This will not only help in comparison of results, but will also accelerate the pace of research for accident prediction, as the comparison with state-of-theart approaches will get easier and more logical. To state an example from the domain of automated generation of game contents, a set of entertainment metrics is proposed in Halim et al. (2014) which is used for the measurement of entertainment that the contents of a game may carry. The same can be used for comparison of different techniques for generation of games. ...
... The same can be used for comparison of different techniques for generation of games. A performance marker as in Halim et al. (2014) is also required for the research in the area of traffic accident prediction and road safety approaches. Once a performance marker is introduced, this will also help in conducting studies by further increasing the attributes of dataset. ...
Accident prediction is one of the most critical aspects of road safety, whereby an accident can be predicted before it actually occurs and precautionary measures taken to avoid it. For this purpose, accident prediction models are popular in road safety analysis. Artificial intelligence (AI) is used in many real world applications, especially where outcomes and data are not same all the time and are influenced by occurrence of random changes. This paper presents a study on the existing approaches for the detection of unsafe driving patterns of a vehicle used to predict accidents. The literature covered in this paper is from the past 10 years, from 2004 to 2014. AI techniques are surveyed for the detection of unsafe driving style and crash prediction. A number of statistical methods which are used to predict the accidents by using different vehicle and driving features are also covered in this paper. The approaches studied in this paper are compared in terms of datasets and prediction performance. We also provide a list of datasets and simulators available for the scientific community to conduct research in the subject domain. The paper also identifies some of the critical open questions that need to be addressed for road safety using AI techniques.
... Both SVM-and ANN- [31] based classifiers are used to observe and compare their performance. Though many types of neural networks can be used for classification problems [32,44], we focused the commonly used feed-forward neural networks known as multilayer perceptron (MLPs). We have used a 3-layered neural network (two layers of weights) with 10 neurons in the input layer (corresponding to 10 recorded attributes of the data) and experimented with 2 to 10 neurons in the hidden layer. ...
This work addresses the problem of profiling drivers based on their driving features. A purpose-built hardware integrated with a software tool is used to record data from multiple drivers. The recorded data is then profiled using clustering techniques. k-means has been used for clustering and the results are counterchecked with Fuzzy c-means (FCM) and Model Based Clustering (MBC). Based on the results of clustering, a classifier, i.e., an Artificial Neural Network (ANN) is trained to classify a driver during driving in one of the four discovered clusters (profiles). The performance of ANN is compared with that of a Support Vector Machine (SVM). Comparison of the clustering techniques shows that different subsets of the recorded dataset with a diverse combination of attributes provide approximately the same number of profiles, i.e., four. Analysis of features shows that average speed, maximum speed, number of times brakes were applied, and number of times horn was used provide the information regarding drivers’ driving behavior, which is useful for clustering. Both one versus one (SVM) and one versus rest (SVM) method for classification have been applied. Average accuracy and average mean square error achieved in the case of ANN was 84.2 % and 0.05 respectively. Whereas the average performance for SVM was 47 %, the maximum performance was 86 % using RBF kernel. The proposed system can be used in modern vehicles for early warning system, based on drivers’ driving features, to avoid accidents.
