Game AI Research Articles

Implementation of Vector Algebra and The Hybrid Pseudo Random Number Generator in Android Game of Kuaci

Kuaci is a game property made of plastic and has various unique shapes. Two to ten people usually play Kuaci game in turn. The game starts with determining how many Kuaci will be at stake, and then each player puts the Kuaci into a box. At a distance of one foot from the box, a first straight line is made, and at a distance of two meters from the first line, the second line is created. From the second line, the kuaci master is thrown to the first line to determine the turn. The player’s turn is sorted from the closest one to the first line to the furthest from the first line. The first player tosses the Kuaci master into the box until the Kuaci master and the regular pieces located inside the box are out, then from the place where the Kuaci was thrown, the player aims at the opponent to eliminate it. If other players have been eliminated, then the player has won the game. The game was built on Android smartphone that has recently become the biggest contributor to the technological development of the traditional games. The authors implemented Vector Algebra in determining the direction of the computer player Kuaci master and The Hybrid Pseudo Random Number Generator on the determination of the throwing power of the computer player Kuaci master so the computer player can balance the game with the user. These methods implementation is to assure the user to play the game without eliminating the essence of the game such as managing the strategy to defeat the opponent. As a result, the performance of the opponent’s Kuaci master can run according to the difficulty of each game.

Speed Optimization for Incremental Updating of Grid-Based Distance Maps

In the context of robotics and game AI, grid-based Distance Maps (DMs) are often used to fulfill collision checks by providing each traversable cell maximal clearance to its closest obstacle. A key challenge for DMs’ application is how to improve the efficiency of updating the distance values when cell states are changed (i.e., changes caused by newly inserted or removed obstacles). To this end, this paper presents a novel algorithm to speed up the construction of DMs on planar, eight-connected grids. The novelty of our algorithm, Canonical Ordering Dynamic Brushfire (CODB), lies in two aspects: firstly, it only updates those cells which are affected by the changes; secondly, it employs the strategy of Canonical Ordering from the fast path planning community to guide the direction of the update; therefore, the construction requires much fewer cell visits and less computation costs compared to previous algorithms. Furthermore, we propose algorithms to compute DM-based subgoal graphs. Such a spatial representation can be used to provide high-level, collision-free roadmaps for agents with certain safety radius to engage fast and rational path planning tasks. We present our algorithm both intuitively and through pseudocode, compare it to competing algorithms in simulated scenarios, and demonstrate its usefulness for real-time path planning tasks.

An Application of Peircean Triadic Logic: Modelling Vagueness

Development of decision-support and intelligent agent systems necessitates mathematical descriptions of uncertainty and fuzziness in order to model vagueness. This paper seeks to present an outline of Peirce’s triadic logic as a practical new way to model vagueness in the context of artificial intelligence (AI). Charles Sanders Peirce (1839–1914) was an American scientist–philosopher and a great logician whose triadic logic is a culmination of the study of semiotics and the mathematical study of anti-Cantorean model of continuity and infinitesimals. After presenting Peircean semiotics within AI perspective, a mathematical formulation of a Peircean triadic set is given in relationship with classical and fuzzy sets. Using basic logical operators, all possible respective implication operators, bi-equivalence operators, valid rules of inference, and associative, distributive and commutative logical properties are derived and verified through the truth function approach. In order to suggest practical directions, aggregation operators for Peirce’s triadic logic have been formulated. A mathematical formulation of a medical diagnostic problem and ER diagram of a library management system using Peirce’s triadic relation show potential for further applications of the proposed triadic set and triadic logic. Alongside, a classical AI game—The Wumpus World—is implemented to show practical efficacy in comparison with binary implementation. Besides giving some preliminary formulations for trichotomous set theory and definition of finite automaton, development of hybrid architectures for intelligent agents and evolutionary computations are discussed as potential practical avenues for Peirce’s triadic logic.

AI Generality and Spearman’s Law of Diminishing Returns


 
 
 Many areas of AI today use benchmarks and competitions with larger and wider sets of tasks. This tries to deter AI systems (and research effort) from specialising to a single task, and encourage them to be prepared to solve previously unseen tasks. It is unclear, however, whether the methods with best performance are actually those that are most general and, in perspective, whether the trend moves towards more general AI systems. This question has a striking similarity with the analysis of the so-called positive manifold and general factors in the area of human intelligence. In this paper, we first show how the existence of a manifold (positive average pairwise task correlation) can also be analysed in AI, and how this relates to the notion of agent generality, from the individual and the populational points of view. From the populational perspective, we analyse the following question: is this manifold correlation higher for the most or for the least able group of agents? We contrast this analysis with one of the most controversial issues in human intelligence research, the so-called Spearman's Law of Diminishing Returns (SLODR), which basically states that the relevance of a general factor diminishes for most able human groups. We perform two empirical studies on these issues in AI. We analyse the results of the 2015 general video game AI (GVGAI) competition, with games as tasks and "controllers" as agents, and the results of a synthetic setting, with modified elementary cellular automata (ECA) rules as tasks and simple interactive programs as agents. In both cases, we see that SLODR doesnot appear. The data, and the use of just two scenarios, does not clearly support the reverse either, a Universal Law of Augmenting Returns (ULOAR), but calls for more experiments on this question.
 
 

Optimal Skipping Rates: Training Agents with Fine-Grained Control Using Deep Reinforcement Learning

These days game AI is one of the focused and active research areas in artificial intelligence because computer games are the best test-beds for testing theoretical ideas in AI before practically applying them in real life world. Similarly, ViZDoom is a game artificial intelligence research platform based on Doom used for visual deep reinforcement learning in 3D game environments such as first-person shooters (FPS). While training, the speed of the learning agent greatly depends on the number of frames the agent is permitted to skip. In this paper, how the frame skipping rate influences the agent’s learning and final performance is proposed, particularly using deep Q-learning, experience replay memory, and the ViZDoom Game AI research platform. The agent is trained and tested on Doom’s basic scenario(s) where the results are compared and found to be 10% better compared to the existing state-of-the-art research work on Doom-based agents. The experiments show that the profitable and optimal frame skipping rate falls in the range of 3 to 11 that provides the best balance between the learning speed and the final performance of the agent which exhibits human-like behavior and outperforms an average human player and inbuilt game agents.

DESIGN IMPLEMENTASI KECERDASAAN BUATAN PADA CHATBOT LINE DALAM PERMAINAN TEBAK ANGKA

Information technology provides a new change in the increasingly diverse world of games with play games that are fun when played and content is given in such an interesting way in packaging, artificial intelligence in games can provide interactive play between users and the system adds to the playing experience, the game world has entered Social media applications are some of the applications that were originally formed for relationships and now add realtime gaming features to play with friendships that have been added to the application. The game that will be designed using the chatbotline on social media Line starts from the game play flow about guessing a number that will later come out whether the guess results from the user are correct or wrong based on artificial intelligence and the highest score is the winner. The results obtained from game design provide the user experience for interaction in playing or with other users to increase the excitement in using social media.

Trained Behavior Trees: Programming by Demonstration to Support AI Game Designers

Programming by demonstration (PbD) has a straightforward application in the development of the artificial intelligence (AI) for nonplayer characters (NPCs) in a video game: a game designer controls the NPC during a training session in the game, and thus demonstrates the expected behavior for that character in different situations. Afterwards, applying some machine learning technique on the traces recorded during the demonstration, an AI for the NPC can be generated. Nevertheless, with this approach, it is very hard for the game designer to fully control the resulting behavior, which is a key requirement for game designers, who are responsible for putting together a fun experience for the player. In this paper, we present trained behavior trees (TBTs). TBTs are behavior trees (BTs) generated from traces obtained in a game through PbD. BTs are a technique widely used for AI game programming that are created and modified through special purpose visual editors. By inducing a BT from a PbD game session, we combine the ease of use of PbD with the ability to fine-tune the learned behavior of BTs. Furthermore, TBTs facilitate the use of BTs by game designers and promote their authoring control on game AI.

The Dots and Boxes Records Storing Standard Format for Machine Learning and The Design and Implementation of Its Generation Tool

Dots and Boxes is a very popular game for two players. There are many teams in the world who are studying smart game programs about this game. Unfortunately, different from the popular game Go, there are no uniform Dots and Boxes game storing records standards format. This results in a large number of classical match records that cannot be preserved, and it also affects the study of this game algorithms. In particular, machine learning algorithms rely on a large number of records. The AlphaGo developed by Google defeated human top players for the first time in Go with the help of a large number of human chess games. In order to facilitate the recording and saving of Dots and Boxes game records. Based on the JSON standard, this paper designs a Dots and Boxes game records standard format and its storage standard, which has excellent expansion and strong compatibility. It also supports cross-platform applications, facilitates secondary development, and is easy to read and write. In order to facilitate the use and promotion of this standard format, simplify the development process of the game AI algorithm. This article uses Python3 language, based on PyQt5 framework, to implement a Dots and Boxes game software DotsAndBoxes, which can generate and read the above-mentioned game records standard file. It has the characteristics of strong versatility, high portability, good scalability, and low maintenance cost.

Modelling and implementation of 9tka game with MaxN algorithm

9tka is a board game created by Adam Kaluza. The game can be played with 2 up to 4 players, with the goal of conquering as many areas in the board as possible. The aim of this research is to implement the 9tka game into a digital game that can be played on a personal computer. The implementation will include the feature to play against computer players. The rules and game’s play of 9tka is modelled, and then implemented using Java. The Artificial Intelligence (AI) of the computer player is implemented using the MaxN algorithm, which is an extension of the minimax algorithm. Several tests were done to gauge the robustness of the implemented AI. The experiments show that the AI is capable to make a move in time less than 541 milliseconds on average, across all types of players. Moreover, from eight respondents, the average amount of human wins is 2.25 out of 5 matches, across all types of players. This shows that the implemented AI on computer player has a better chance to defeat human opponents.

StarCraft Micromanagement With Reinforcement Learning and Curriculum Transfer Learning

Real-time strategy games have been an important field of game artificial intelligence in recent years. This paper presents a reinforcement learning and curriculum transfer learning method to control multiple units in StarCraft micromanagement. We define an efficient state representation, which breaks down the complexity caused by the large state space in the game environment. Then, a parameter sharing multi-agent gradient-descent Sarsa( $\lambda$ ) algorithm is proposed to train the units. The learning policy is shared among our units to encourage cooperative behaviors. We use a neural network as a function approximator to estimate the action–value function, and propose a reward function to help units balance their move and attack. In addition, a transfer learning method is used to extend our model to more difficult scenarios, which accelerates the training process and improves the learning performance. In small-scale scenarios, our units successfully learn to combat and defeat the built-in AI with 100% win rates. In large-scale scenarios, the curriculum transfer learning method is used to progressively train a group of units, and it shows superior performance over some baseline methods in target scenarios. With reinforcement learning and curriculum transfer learning, our units are able to learn appropriate strategies in StarCraft micromanagement scenarios.

The Behavioral Economics of Artificial Intelligence: Lessons from Experiments with Computer Players

Artificial intelligence (AI) is starting to pervade the economic and social life rendering strategic interactions with artificial agents more and more common. At the same time, experimental economic research has increasingly employed computer players to advance our understanding of strategic interaction in general. What can this strand of research teach us about an AI-shaped future? I review 90 experimental studies using computer players. I find that, in a nutshell, humans act more selfishly and more rational in the presence of computer players, and they are often able to exploit these players. Still, many open questions prevail.

Development of a Computer Player for Seejeh (A.K.A Seega, Siga, Kharbga) Board Game with Deep Reinforcement Learning

Recent years have proven the existing room of deep reinforcement learning (DRL) applications. DRL has been utilized as an AI computer player in many board games. Seejeh is an ancient board game, where no one attempts to create an AI system that is able to learn to play it. Seejeh is a two-player, zero-sum, discrete, finite and deterministic game of perfect information. Seejeh board game is different from all other strategic board games. It has two stages; Positioning and moving. Player place two tiles at each action in stage one. A player might have a sequence of moves in the second stage unlike Othello and Go. In this work, we develop an automated player based solely on reinforcement learning, without human data, guidance or domain knowledge beyond game rules. This paper presents a self-play algorithm utilizing DRL and search algorithms. The system starts with a neural network that knows nothing about the game of Seejeh. It then plays games against itself, by combining this neural network with powerful search algorithms. To the best of our knowledge, we are the first who develop an agent that learns to play Seejeh game.

Applying Behavior characteristics to decision-making process to create believable game AI

With the development of artificial intelligence in computer games the problem of creating characters with believable and diverse behavior to inhabit in-game worlds becomes more and more actual. A big number of required characters and high standards of a modern game artificial intelligence makes the problem even more complex. In this paper we propose a utility-based decision-making model which gives the possibility to generate characters with believable behavior. The believability of such characters comes from their decision-making process that takes into account not only assessment of game environment, but also their personal characteristics and social status. Designed model was used to generate AI driven characters for a development of player’s opponents with personality traits for a computer card strategy. The model was used to simulate human flows on main railroad hub of 2014 Winter Olympics in order to reveal areas where pedestrian flow should be controlled.

Training the use of theory of mind using artificial agents

When engaging in social interaction, people rely on their ability to reason about unobservable mental content of others, which includes goals, intentions, and beliefs. This so-called theory of mind ability allows them to more easily understand, predict, and influence the behavior of others. People even use their theory of mind to reason about the theory of mind of others, which allows them to understand sentences like ‘Alice believes that Bob does not know about the surprise party’. But while the use of higher orders of theory of mind is apparent in many social interactions, empirical evidence so far suggests that people do not use this ability spontaneously when playing strategic games, even when doing so would be highly beneficial. In this paper, we attempt to encourage participants to engage in higher-order theory of mind reasoning by letting them play a game against computational agents. Since previous research suggests that competitive games may encourage the use of theory of mind, we investigate a particular competitive game, the Mod game, which can be seen as a much larger variant of the well-known rock–paper–scissors game. By using a combination of computational agents and Bayesian model selection, we simultaneously determine to what extent people make use of higher-order theory of mind reasoning, as well as to what extent computational agents can encourage the use of higher-order theory of mind in their human opponents. Our results show that participants who play the Mod game against computational theory of mind agents adjust their level of theory of mind reasoning to that of their computer opponent. Earlier experiments with other strategic games show that participants only engage in low orders of theory of mind reasoning. Surprisingly, we find that participants who knowingly play against second- and third-order theory of mind agents apply up to fourth-order theory of mind themselves, and achieve higher scores as a result.

IEEE Transactions on Computational Intelligence and AI in Games

IEEE Transactions on Computational Intelligence and AI in Games

Reports of the Workshops of the 32nd AAAI Conference on Artificial Intelligence

The AAAI-18 workshop program included 15 workshops covering a wide range of topics in AI. Workshops were held Sunday and Monday, February 2–7, 2018, at the Hilton New Orleans Riverside in New Orleans, Louisiana, USA. This report contains summaries of the Affective Content Analysis workshop; the Artificial Intelligence Applied to Assistive Technologies and Smart Environments; the AI and Marketing Science workshop; the Artificial Intelligence for Cyber Security workshop; the AI for Imperfect-Information Games; the Declarative Learning Based Programming workshop; the Engineering Dependable and Secure Machine Learning Systems workshop; the Health Intelligence workshop; the Knowledge Extraction from Games workshop; the Plan, Activity, and Intent Recognition workshop; the Planning and Inference workshop; the Preference Handling workshop; the Reasoning and Learning for Human-Machine Dialogues workshop; and the the AI Enhanced Internet of Things Data Processing for Intelligent Applications workshop.

2019 IEEE Conference on Games (CoG)

Quality-diversity (QD) algorithms search for a set of good solutions which cover a space as defined by behavior metrics. This simultaneous focus on quality and diversity with explicit metrics sets QD algorithms apart from standard single- and multi-objective evolutionary algorithms, as well as from diversity preservation approaches such as niching. These properties open up new avenues for artificial intelligence in games, in particular for procedural content generation. Creating multiple systematically varying solutions allows new approaches to creative human-AI interaction as well as adaptivity. In the last few years, a handful of applications of QD to procedural content generation and game playing have been proposed; we discuss these and propose challenges for future work.

“The Game Ain’t What It Used to Be”: Drug Sellers’ Perceptions of the Modern Day Underground and Legal Markets

This study draws on interviews with 20 drug sellers in Philadelphia to explore their subjective perceptions of risk and reward within the modern context of increased police scrutiny and few illegal or legal opportunities for social mobility. Most respondents earned little, viewed drug sales negatively, and perceived the next generation of drug sellers to be unpredictable and prone to risk. They sought exit opportunities, associating continued participation in drug sales with childhood and wishing to participate more fully in the mainstream economy. Half of the sample worked in legal employment, and many argued that this was both necessary and beneficial to reducing risk. The findings suggest that traditional theories explaining participation in drug sales may need to be re-examined in light of changes in the landscape of the drug economy. Moreover, they may provide context to understanding why deterrence-based policies have generally failed to disrupt drug markets.

Designing Behavioural Artificial Intelligence to Record, Assess and Evaluate Human Behaviour

The context of the work presented in this article is the assessment and automated evaluation of human behaviour. To facilitate this, a formalism is presented which is unambiguous as well as such that it can be implemented and interpreted in an automated manner. In the greater scheme of things, comparable behaviour evaluation requires comparable assessment scenarios and, to this end, computer games are considered as controllable and abstract environments. Within this context, a model for behavioural AI is presented which was designed around the objectives of: (a) being able to play rationally; (b) adhering to formally stated behaviour preferences; and (c) ensuring that very specific circumstances can be forced to arise within a game. The presented work is based on established models from the field of behavioural psychology, formal logic as well as approaches from game theory and related fields. The suggested model for behavioural AI has been used to implement and test a game, as well as AI players that exhibit specific behavioural preferences. The overall aim of this article is to enable the readers to design their own AI implementation, using the formalisms and models they prefer and to a level of complexity they desire.

Modeling Player Experience with the N-Tuple Bandit Evolutionary Algorithm

Automatic game design is an increasingly popular area of research that consists of devising systems that create content or complete games autonomously. The interest in such systems is two-fold: games can be highly stochastic environments that allow presenting this task as a complex optimization problem and automatic play-testing, becoming benchmarks to advance the state of the art on AI methods. In this paper, we propose a general approach that employs the N-Tuple Bandit Evolutionary Algorithm (NTBEA) to tune parameters of three different games of the General Video Game AI (GVGAI) framework. The objective is to adjust the game experience of the players so the distribution of score events through the game approximates certain pre-defined target curves. We report satisfactory results for different target score trends and games, paving the path for future research in the area of automatically tuning player experience.