bluuebunny/crossref_metadata_2025 huggingface dataset:
Application of the BLUPe predictor in the selection of potential soybean varieties for Orinoquia
Authors: Valencia Ramírez Rubén Alfredo , Tibocha Ardila Yuli Stephani
Published: October 2022

The Colombian Orinoquía and the Altillanura subregion show comparative and competitive advantages for soybean production (edaphoclimatic conditions, cost-benefit ratio, potential area, and location), essential food with a high protein content (~37%) and used mostly for poultry and pig nutrition. However, this immense region has scarce varietal alternatives of high grain yield and quality that are adapted to its climatic and edaphic conditions. The current research is based on the selection of superior lines or potential varieties with high genetic merit using the restricted maximum likelihood/standardized best linear unbiased predictor (REML/BLUPe) procedure. Sixty advanced lines and four commercial varieties were tested in an 8x8 alpha lattice design. Grain yield (GY) oscillated between 1,117 and 4,431 kg ha-1, the population average yield was 2,682 kg ha-1, and BLUPe predictors ranged between 5.37 and -3.71. With a t-test at a significance of 5% (1.67) and a predictor comparator of (t1-t2)≥1.67 (√2), six outstanding lines were identified with superior BLUPe values compared to the mean (P<0.05) and GY>3,500 kg ha-1. In descending order (kg ha-1), the GY was: L-041 (4,431), L-019 (4,326), L-104 (3,923), L-149 (3,832), L-202 (3,536), and L-201 (3,519 kg ha-1). The BLUPe standardized predictor allowed an effective selection (92%) of lines.
Benchmarking polymeric cryogels for immobilized metal affinity chromatography
Authors: SAYLAN Yeşeren
Published: October 2022

Cryogels are polymers prepared in frozen environment and seen as new separation matrices with their applications in many bioseparation methods. They have significant benefits including supermacroporosity, short diffusion path, low pressure, and low resistance to both adsorption and elution. Macro and connected pores give cryogels a unique spongy structure. Immobilized metal affinity chromatography (IMAC) is a generally employed analytical separation method for the purification of biomolecules. Several transition ions create stable complexes with electron-rich compounds. IMAC sorbent is obtained by complexing first-order transition metal ions over chelating agents. Lysozyme is an enzyme found in various vertebrate cells and secretions. Common applications include its use as a cell disrupting agent, as an anti-bacterial agent, as a food additive, and as a medicine for the treatment of infections and ulcers. In this study, cryogel-based polymeric material prepared by free-radical polymerization method with hydroxyethyl methacrylate/glycidyl methacrylate monomer pair were covalently interacted with iminodiacetic acid metal chelating agent, and regions showing affinity for lysozyme enzyme were formed by binding with Ni(II) ions. The cryogel was first characterized by Fourier transform infrared spectrophotometer, scanning electron microscopy, thermal gravimetric analysis, X-ray photoelectron spectroscopy and swelling test. Then, the effects of pH, lysozyme concentration, temperature, ionic strength and flow rate on lysozyme adsorption capacity were explored and optimum conditions were found.
Education and Social Relationships between Museums and Older People: A Scoping Review
Authors: ARAUJO OLGA SUSANA COSTA COITO E , HINSLIFF-SMITH KATHRYN , CACHIONI MEIRE
Published: October 2022

ABSTRACT Rapidly ageing population increases the demand for activities with learning and social stimulation. Museums are well placed to provide such a function. Objective: How and in what form are museums engaging with older populations. The review will focus on three aspects: (1) Identifying the frameworks and tools being used by and in museums with older people. (2) Assess variables used in research and in areas that supports knowledge creation. (3) Identify evidence in relation to the interactions between museums and older audiences. A review of 8 databases and 2 university repositories (English and Portuguese language). The search retrieved 810 potential sources (2002-2020), 39 sources met the inclusion criteria. They focused on the educational aspects of museums and were produced by disciplines like psychology and gerontology, museology, arts and education. Museums play an important role on health (emphasis on psychological and physical wellbeing), socialization (discussing stereotypes, reducing isolation, redefining social roles), socioeconomic aspects (with local focus on services, partnerships) and on creative aging (promoting non-formal education with lifelong learning at different levels).
Finding Schrödinger’s Gun
Authors: Robertson Justus , Young R.
Published: October 2022

Interactive narratives are branching stories with events that change based on feedback from participants. One method of generating interactive narratives is a plan-based process called mediation. A sub-process within mediation called accommodation creates new story content when a participant deviates from the main storyline. We show that a model of character knowledge allows accommodation to find a novel class of branching stories previously inaccessible by the algorithm.
Implications of Ad Hoc Artificial Intelligence in Music
Authors: Merz Evan
Published: October 2022

This paper is an examination of several well-known applications of artificial intelligence in music generation. The algorithms in EMI, GenJam, WolframTones, and Swarm Music are examined in pursuit of ad hoc modifications. Based on these programs, it is clear that ad hoc modifications occur in most algorithmic music programs. We must keep this in mind when generalizing about computational creativity based on these programs. Ad hoc algorithms model a specific task, rather than a general creative algorithm. The musical metacreation discourse could benefit from the skepticism of the procedural content practitioners at AIIDE.
Optimizing Players’ Expected Enjoyment in Interactive Stories
Authors: Yu Hong , Riedl Mark
Published: October 2022

In interactive storytelling systems and other story-based computer games, a drama manager is a background agent that aims to bring about an enjoyable and coherent experience for the players. In this paper, we present a personalized drama manager that increases a player's expected enjoyment without removing player agency. Our personalized drama manager models a player's preference using data-driven techniques, predicts the probability the player transitioning to different story experiences, selects an objective experience that can maximize the player's expected enjoyment, and guides the player to the selected story experience. Human study results show that our drama manager can significantly increase players' enjoyment ratings in an interactive storytelling testbed, compared to drama managers in previous research.
Monte-Carlo Tree Search for Persona Based Player Modeling
Authors: Holmgård Christoffer , Liapis Antonios , Togelius Julian , Yannakakis Georgios
Published: October 2022

Is it possible to conduct player modeling without any players? In this paper we use Monte-Carlo Tree Search-controlled procedural personas to simulate a range of decision making styles in the puzzle game MiniDungeons 2. The purpose is to provide a method for synthetic play testing of game levels with synthetic players based on designer intuition and experience. Five personas are constructed, representing five different decision making styles archetypal for the game. The personas vary solely in the weights of decision-making utilities that describe their valuation of a set affordances in MiniDungeons 2. By configuring these weights using designer expert knowledge, and passing the configurations directly to the MCTS algorithm, we make the personas exhibit a number of distinct decision making and play styles.
Combining Gameplay Data with Monte Carlo Tree Search to Emulate Human Play
Authors: Devlin Sam , Anspoka Anastasija , Sephton Nick , Cowling Peter , Rollason Jeff
Published: October 2022

Monte Carlo Tree Search (MCTS) has become a popular solution for controlling non-player characters. Its use has repeatedly been shown to be capable of creating strong game playing opponents. However, the emergent playstyle of agents using MCTS is not necessarily human-like, believable or enjoyable. AI Factory Spades, currently the top rated Spades game in the Google Play store, uses a variant of MCTS to control non-player characters. In collaboration with the developers, we collected gameplay data from 27,592 games and showed in a previous study that the playstyle of human players significantly differed from that of the non-player characters. This paper presents a method of biasing MCTS using human gameplay data to create Spades playing agents that emulate human play whilst maintaining a strong, competitive performance. The methods of player modelling and biasing MCTS presented in this study are generally applicable to digital games with discrete actions.
An Expressive Dilemma Generation Model for Players and Artificial Agents
Authors: Harmon Sarah
Published: October 2022

Dilemma scenarios and knowledge of social values support engaging and believable narrative gameplay. Previous work in dilemma generation has explored scenarios involving utility payoffs between two to three people. However, these models primarily require character relationships as preconditions, and do not extend to more complex choices that relate only to causes and values. This paper builds upon past work to create an expressive model of dilemma categorization for player and non-player characters.
Matching Games and Algorithms for General Video Game Playing
Authors: Bontrager Philip , Khalifa Ahmed , Mendes Andre , Togelius Julian
Published: October 2022

This paper examines the performance of a number of AI agents on the games included in the General Video Game Playing Competition. Through analyzing these results, the paper seeks to provide insight into the strengths and weaknesses of the current generation of video game playing algorithms. The paper also provides an analysis of the given games in terms of inherent features which define the different games. Finally, the game features are matched with AI agents, based on performance, in order to demonstrate a plausible case for algorithm portfolios as a general video game playing technique.
Learning Combat in NetHack
Authors: Campbell Jonathan , Verbrugge Clark
Published: October 2022

Combat in roguelikes involves careful strategy to best match a large variety of items and abilities to a given opponent, and the significant scripting effort involved can be a major barrier to automation. This paper presents a machine learning approach for a subset of combat in the game of NetHack. We describe a custom learning approach intended to deal with the large action space typical of this genre, and show that it is able to develop and apply reasonable strategies, outperforming a simpler baseline approach. These results point towards better automation of such complex game environments, facilitating automated testing and design exploration.
Studying the Effects of Training Data on Machine Learning-Based Procedural Content Generation
Authors: Snodgrass Sam , Summerville Adam , Ontanon Santiago
Published: October 2022

The exploration of Procedural Content Generation via Machine Learning (PCGML) has been growing in recent years. However, while the number of PCGML techniques and methods for evaluating PCG techniques have been increasing, little work has been done in determining how the quality and quantity of the training data provided to these techniques effects the models or the output. Therefore, little is known about how much training data would actually be needed to deploy certain PCGML techniques in practice. In this paper we explore this question by studying the quality and diversity of the output of two well-known PCGML techniques (multi-dimensional Markov chains and Long Short-term Memory Recurrent Neural Networks) in generating Super Mario Bros. levels while varying the amount and quality of the training data.
A Social Robot as a Card Game Player
Authors: Correia Filipa , Alves-Oliveira Patrícia , Ribeiro Tiago , Melo Francisco , Paiva Ana
Published: October 2022

This paper describes a social robotic game player that is able to successfully play a team card game called Sueca. The question we will address in this paper is: how can we build a social robot player that is able to balance its ability to play the card game with natural and social behaviours towards its partner and its opponents. The first challenge we faced concerned the development of a competent artificial player for a hidden information game, whose time constraint is the average human decision time. To accomplish this requirement, the Perfect Information Monte Carlo (PIMC) algorithm was used. Further, we have performed an analysis of this algorithm's possible parametrizations for games trees that cannot be fully explored in a reasonable amount of time with a MinMax search. Additionally, given the nature of the Sueca game, such robotic player must master the social interactions both as a partner and as an opponent. To do that, an emotional agent framework (FAtiMA) was used to build the emotional and social behaviours of the robot. At each moment, the robot not only plays competitively but also appraises the situation and responds emotionally in a natural manner. To test the approach, we conducted a user study and compared the levels of trust participants attributed to the robots and to human partners. Results have shown that the robot team exhibited a winning rate of 60%. Concerning the social aspects, the results also showed that human players increased their trust in the robot as their game partners (similar to the way to the trust levels change towards human partners).
Modeling Individual Differences in Game Behavior Using HMM
Authors: Bunian Sara , Canossa Alessandro , Colvin Randy , Seif El-Nasr Magy
Published: October 2022

Player modeling is an important concept that has gained much attention in game research due to its utility in developing adaptive techniques to target better designs for engagement and retention. Previous work has explored modeling individual differences using machine learning algorithms performed on aggregated game actions. However, players’ individual differences may be better manifested through sequential patterns of the in-game player’s actions. While few works have explored sequential analysis of player data, none have explored the use of Hidden Markov Models (HMM) to model individual differences, which is the topic of this paper. In particular, we developed a modeling approach using data collected from players playing a Role-Playing Game (RPG). Our proposed approach is two fold: 1. We present a Hidden Markov Model (HMM) of player in-game behaviors to model individual differences, and 2. using the output of the HMM, we generate behavioral features used to classify real world players’ characteristics, including game expertise and the big five personality traits. Our results show predictive power for some of personality traits, such as game expertise and conscientiousness, but the most influential factor was game expertise.
Toward Automated Story Generation with Markov Chain Monte Carlo Methods and Deep Neural Networks
Authors: Harrison Brent , Purdy Christopher , Riedl Mark
Published: October 2022

In this paper, we introduce an approach to automated story generation using Markov Chain Monte Carlo (MCMC) sampling. This approach uses a sampling algorithm based on Metropolis-Hastings to generate a probability distribution which can be used to generate stories via random sampling that adhere to criteria learned by recurrent neural networks. We show the applicability of our technique through a case study where we generate novel stories using an acceptance criteria learned from a set of movie plots taken from Wikipedia. This study shows that stories generated using this approach adhere to this criteria 85%-86% of the time.
Gemini: Bidirectional Generation and Analysis of Games via ASP
Authors: Summerville Adam , Martens Chris , Samuel Ben , Osborn Joseph , Wardrip-Fruin Noah , Mateas Michael
Published: October 2022

Current approaches to game generation don't understand the games they generate. As a result, even the most sophisticated systems in this regard, e.g., Game-o-Matic, betray this problem — generating games with goals that are at odds with their mechanics. We describe Gemini, the first bidirectional game generation and analysis system. Gemini is able to take games as input, perform a proceduralist reading of them, and produce possible interpretations that the games might afford. By utilizing the declarative nature of Answer Set Programming (ASP), this analysis pathway opens up generation of games targeting specific interpretations and makes it possible to ensure the generated games are consistent with the desired reading. For Gemini, we developed a game specification language capable of expressing a larger domain of games than is possible with VGDL, the most widespread representation. We demonstrate the generality of our approach by generating games in a series of domains. These domains are based on prototypes hand-created by a team without knowledge of the constraints and capabilities of Gemini.
Exhaustive and Semi-Exhaustive Procedural Content Generation
Authors: Sturtevant Nathan , Ota Matheus
Published: October 2022

Within the area of procedural content generation (PCG) there are a wide range of techniques that have been used to generate content. Many of these techniques use traditional artificial intelligence approaches, such as genetic algorithms, planning, and answer-set programming. One area that has not been widely explored is straightforward combinatorial search -- exhaustive enumeration of the entire design space or a significant subset thereof. This paper synthesizes literature from mathematics and other subfields of Artificial Intelligence to provide reference for the algorithms needed when approaching exhaustive procedural content generation. It builds on this with algorithms for exhaustive search and complete examples how they can be applied in practice.
Toward a Unified Understanding of Experience Management
Authors: Thue David , Bulitko Vadim
Published: October 2022

We present a new way to represent and understand experience managers — AI agents that tune the parameters of a running game to pursue a designer's goal. Existing representations of AI managers are diverse, which complicates the task of drawing useful comparisons between them. Contrary to previous representations, ours uses a point of unity as its basis: that every game/manager pair can be viewed as only a game with the manager embedded inside. From this basis, we show that several common, differently-represented concepts of experience management can be re-expressed in a unified way. We demonstrate our new representation concretely by comparing two different representations, Search-Based Drama Management and Generalized Experience Management, and we present the insights that we have gained from this effort.
Playable Experiences at AIIDE 2018
Authors: Samuel Ben , Reed Aaron , Short Emily , Heck Samantha , Robison Barrie , Wright Landon , Soule Terence , Treanor Mike , McCoy Joshua , Sullivan Anne , Shirvani Alireza , Garcia Edward , Farrell Rachelyn , Ware Stephen , Compton Katherine
Published: October 2022

This paper describes the accepted entries to the sixth PlayableExperiences track to be held at the AIIDE conference.The Playable Experiences track showcases innovative completeworks that are informed, inspired, or otherwise enabledby artificial intelligence.
Multi-Agent Narrative Experience Management as Story Graph Pruning
Authors: Ware Stephen G. , Garcia Edward T. , Shirvani Alireza , Farrell Rachelyn
Published: October 2022

In many intelligent interactive narratives, the player controls an avatar while an experience manager controls non-player characters (NPCs). The space of all stories can be viewed as a story graph, where nodes are states and edges are actions taken by the player, by NPCs, or by both jointly. In this paper, we cast experience management as a story graph pruning problem. We start with the full graph and prune intelligently until each NPC has at most one action in every state. Considering the entire graph allows us to foresee the long-term consequences of every pruning decision on the space of possible stories. By never pruning player actions, we ensure the experience manager can accommodate any choice. When used to control the story of an adventure game, players found our technique generally produced higher agency and more believable NPC behavior than a control.
Lyra: Simulating Believable Opinionated Virtual Characters
Authors: Azad Sasha , Martens Chris
Published: October 2022

Creating believable simulations of large populations of characters in virtual worlds represents a grand challenge for interactive artificial intelligence, requiring reasoning about social intelligence. In this paper, we focus on one aspect of this challenge: the dynamics of opinion change for virtual characters and its relationship with social affinity. We developed a simulated population of characters that debate politically-charged topics, called Lyra. Characters’ knowledge, opinions, and biases spread through this society based on existing cognitive models and social science theories. Our simulation generates outlines of group conversations that portray the system’s evolution, and clusters characters into affinity groups based on the outcome of the debates. We conducted a human-subjects study to evaluate these generated conversations and affinity groups for their believability and to inform future iterations of the simulation. We believe successful simulation of opinion change in social dynamics provides a foundation for computational recognition, prediction, and interfacing with humans.
Behavioral Evaluation of Hanabi Rainbow DQN Agents and Rule-Based Agents
Authors: Canaan Rodrigo , Gao Xianbo , Chung Youjin , Togelius Julian , Nealen Andy , Menzel Stefan
Published: October 2022

Hanabi is a multiplayer cooperative card game, where only your partners know your cards. All players succeed or fail together. This makes the game an excellent testbed for studying collaboration. Recently, it has been shown that deep neural networks can be trained through self-play to play the game very well. However, such agents generally do not play well with others. In this paper, we investigate the consequences of training Rainbow DQN agents with human-inspired rule-based agents. We analyze with which agents Rainbow agents learn to play well, and how well playing skill transfers to agents they were not trained with. We also analyze patterns of communication between agents to elucidate how collaboration happens. A key finding is that while most agents only learn to play well with partners seen during training, one particular agent leads the Rainbow algorithm towards a much more general policy. The metrics and hypotheses advanced in this paper can be used for further study of collaborative agents.
Game Level Clustering and Generation Using Gaussian Mixture VAEs
Authors: Yang Zhihan , Sarkar Anurag , Cooper Seth
Published: October 2022

Variational autoencoders (VAEs) have been shown to be able to generate game levels but require manual exploration of the learned latent space to generate outputs with desired attributes. While conditional VAEs address this by allowing generation to be conditioned on labels, such labels have to be provided during training and thus require prior knowledge which may not always be available. In this paper, we apply Gaussian Mixture VAEs (GMVAEs), a variant of the VAE which imposes a mixture of Gaussians (GM) on the latent space, unlike regular VAEs which impose a unimodal Gaussian. This allows GMVAEs to cluster levels in an unsupervised manner using the components of the GM and then generate new levels using the learned components. We demonstrate our approach with levels from Super Mario Bros., Kid Icarus and Mega Man. Our results show that the learned components discover and cluster level structures and patterns and can be used to generate levels with desired characteristics.
Reinforcement Learning with Quantum Variational Circuit
Authors: Lockwood Owen , Si Mei
Published: October 2022

The development of quantum computational techniques has advanced greatly in recent years, parallel to the advancements in techniques for deep reinforcement learning. This work explores the potential for quantum computing to facilitate reinforcement learning problems. Quantum computing approaches offer important potential improvements in time and space complexity over traditional algorithms because of its ability to exploit the quantum phenomena of superposition and entanglement. Specifically, we investigate the use of quantum variational circuits, a form of quantum machine learning. We present our techniques for encoding classical data for a quantum variational circuit, we further explore pure and hybrid quantum algorithms for DQN and Double DQN. Our results indicate both hybrid and pure quantum variational circuit have the ability to solve reinforcement learning tasks with a smaller parameter space. These comparison are conducted with two OpenAI Gym environments: CartPole and Blackjack, The success of this work is indicative of a strong future relationship between quantum machine learning and deep reinforcement learning.
Trouncing in Dota 2: An Investigation of Blowout Matches
Authors: Viggiato Markos , Bezemer Cor-Paul
Published: October 2022

compete against each other, such as Dota 2, play a major role in esports tournaments, attracting millions of spectators. Some matches (so-called blowout matches) end extremely quickly or have a very large difference in scores. Understanding which factors lead to a victory in a blowout match is useful knowledge for players who wish to improve their chances of winning and for improving the accuracy of recommendation systems for heroes. In this paper, we perform a comparative study between blowout and regular matches. We study 55,287 past professional Dota 2 matches to (1) investigate how accurately we can predict victory using only pre-match features and (2) explain the factors that are correlated with the victory. We investigate three machine learning algorithms and find that Gradient Boosting Machines (XGBoost) perform best with an Area Under the Curve (AUC) of up to 0.86. Our results show that the experience of the player with the picked hero has a different importance for blowout and regular matches. Also, hero attributes are more important for blowouts with a large score difference. Based on our results, we suggest that players (1) pick heroes with which they achieved a high performance in previous matches to increase their chances of winning and (2) focus on heroes’ attributes such as intelligence to win with a large score difference.
Anthology: A Social Simulation Framework
Authors: Azad Sasha , Wellnitz Jennifer , Garcia Luis , Martens Chris
Published: October 2022

Social simulation research seeks to understand the dynamics of complex human behavior by simulating populations of individual decision-makers as multi-agent systems. However, prior work in games and entertainment fail to account for interactions between social behavior, geography, and relationships in a manner that allows researchers to easily reuse their frameworks and model social characters. We present Anthology, an extensible software framework for modeling human social systems, within the context of an ongoing research agenda to integrate AI techniques from social simulation games and computational social science to enable researchers to model and reason about the complex dynamics of human social behavior.
Anthology comprises a motive-based agent decision making algorithm; a knowledge representation system for relationships; a flexible specification language for precondition-effect-style actions; a user interface to inspect and interact with the simulation as it runs in real-time; and an extensive user documentation and reference manual. We describe our participatory research design process used for the developing Anthology, the state of the current system, it's limitations and our future development directions.
Story Representation and Interactive Drama
Authors: Magerko Brian
Published: October 2022

When building a story-intensive game, there is always the question of how much freedom to give the player. Give the player too little, and he may feel constrained and disconnected from the character he is controlling. Give him too much freedom, and the progression of the story may lag or stop altogether. The field of interactive drama attempts to strike a balance between interaction and authorship. The story experienced in an interactive drama is dependent both on the plot content authored as well as the player’s choices in the story. Which story representation is appropriate for a particular approach to interactive drama and the relationship between that language and other elements of the architecture is a key factor in design. This paper introduces our interactive drama architecture, IDA, and addresses the requirements it has for a story representation. How those requirements are met by our representational choices is the focus of the rest of the paper.
Search-Based Drama Management in the Interactive Fiction Anchorhead
Authors: Nelson Mark , Mateas Michael
Published: October 2022

Drama managers guide a user through a story experience by modifying the experience in reaction to the user's actions. Search-based drama management (SBDM) casts the dramamanagement problem as a search problem: Given a set of plot points, a set of actions the drama manager can take, and an evaluation of story quality, search can be used to optimize the user's experience. SBDM was first investigated by Peter Weyhrauch in 1997, but little explored since. We return to SBDM to investigate algorithmic and authorship issues, including the extension of SBDM to different kinds of stories, especially stories with subplots and multiple endings, and issues of scalability. In this paper we report on experiments applying SBDM to an abstract story search space based on the text-based interactive fiction Anchorhead. We describe the features employed by the story evaluation function, investigate design issues in the selection of a set of drama management actions, and report results for drama managed versus unmanaged stories for a simulated random user
Agent Architecture Considerations for Real-Time Planning in Games
Authors: Orkin Jeff
Published: October 2022

Planning in real-time offers several benefits over the more typical techniques of implementing Non-Player Character (NPC) behavior with scripts or finite state machines. NPCs that plan their actions dynamically are better equipped to handle unexpected situations. The modular nature of the goals and actions that make up the plan facilitates re-use, sharing, and maintenance of behavioral building blocks. These benefits, however, come at the cost of CPU cycles. In order to simultaneously plan for several NPCs in real-time, while continuing to share the processor with the physics, animation, and rendering systems, careful consideration must taken with the supporting architecture. The architecture must support distributed processing and caching of costly calculations. These considerations have impacts that stretch beyond the architecture of the planner, and affect the agent architecture as a whole. This paper describes lessons learned while implementing real-time planning for NPCs for F.E.A.R., a AAA first person shooter shipping for PC in 2005.
Cooperative Pathfinding
Authors: Silver David
Published: October 2022

Cooperative Pathfinding is a multi-agent path planning problem where agents must find non-colliding routes to separate destinations, given full information about the routes of other agents. This paper presents three new algorithms for efficiently solving this problem, suitable for use in Real-Time Strategy games and other real-time environments. The algorithms are decoupled approaches that break down the problem into a series of single-agent searches. Cooperative A* (CA*) searches space-time for a non-colliding route. Hierarchical Cooperative A* (HCA*) uses an abstract heuristic to boost performance. Finally, Windowed Hierarchical Cooperative A* (WHCA*) limits the space-time search depth to a dynamic window, spreading computation over the duration of the route. The algorithms are applied to a series of challenging, maze-like environments, and compared to A* with Local Repair (the current video-games industry standard). The results show that the new algorithms, especially WHCA*, are robust and efficient solutions to the Cooperative Pathfinding problem, finding more successful routes and following better paths than Local Repair A*
Biased Cost Pathfinding
Authors: Geramifard Alborz , Chubak Pirooz , Bulitko Vadim
Published: October 2022

In this paper we introduce the Biased Cost Pathfinding (BCP) algorithm as a simple yet effective meta-algorithm that can be fused with any single-agent search method in order to make it usable in multi-agent environments. In particular, we focus on pathfinding problems common in real-time strategy games where units can have different functions and mission priorities. We evaluate BCP paired with the A* algorithm in several game-like scenarios. Performance improvement of up to 90% is demonstrated with respect to several metrics.
Computational Support for Play Testing Game Sketches
Authors: Smith Adam , Nelson Mark , Mateas Michael
Published: October 2022

Early-stage game prototypes need to be informative without requiring excessive commitments. Paper prototypes are frequently used as a way of trying out core mechanics while leaving them easy to change. Play testing on even these early-stage prototypes can give an idea of how the rules play out and whether the game is fun and engaging. Recently, researchers have proposed using automated analysis of games to discover additional properties of games, such as exploits and other gameplay issues. We propose a lightweight game-sketching approach to give designers access to insight derived from both human and machine play testing. Using our system, BIPED, a designer specifies a game's mechanics and maps them to a set of board-game-like primitives. Games created with BIPED can be played interactively on a computer as well as automatically analyzed, giving designers two complementary sources of design backtalk. In this paper, we describe the language designers may use to sketch games, how they might use our tool in the two modes of play testing, and how the prototypes are computationally realized. Additionally, we study using our system to prototype a game and examine it in human and machine play tests.
Crowd Simulation Via Multi-Agent Reinforcement Learning
Authors: Torrey Lisa
Published: October 2022

Artificial intelligence is frequently used to control virtual characters in movies and games. When these characters appear in crowds, controlling them is called crowd simulation. In this paper, I suggest that crowd simulation could be accomplished by multi-agent reinforcement learning, a method by which groups of agents can learn to act autonomously in their environment. I present a case study that explores the challenges and benefits of this type of approach and encourages the development of learning techniques for AI in entertainment media.
Learning Companion Behaviors Using Reinforcement Learning in Games
Authors: Sharifi AmirAli , Zhao Richard , Szafron Duane
Published: October 2022

Our goal is to enable Non Player Characters (NPC) in computer games to exhibit natural behaviors. The quality of behaviors affects the game experience especially in story-based games, which rely on player-NPC interactions. We used Reinforcement Learning to enable NPC companions to develop preferences for actions. We implemented our RL technique in BioWare Corp.’s Neverwinter Nights. Our experiments evaluate an NPC companion’s behaviors regarding traps. Our method enables NPCs to rapidly learn reasonable behaviors and adapt to changes in the game.
The SAM Algorithm for Analogy-Based Story Generation
Authors: Ontanon Santiago , Zhu Jichen
Published: October 2022

Analogy-based Story Generation (ASG) is a relatively under-explored approach for story generation and computational narrative. In this paper, we present the SAM (Story Analogies through Mapping) algorithm as our attempt to expand the scope and complexity of stories generated by ASG. Comparing with existing work and our prior work, there are two main contributions of SAM: it employs 1) analogical reasoning both at the specific story content and general domain knowledge levels, and 2) temporal reasoning about the story (phase) structure in order to generate more complex stories. We illustrate SAM through a few example stories.
CPOCL: A Narrative Planner Supporting Conflict
Authors: Ware Stephen , Young R.
Published: October 2022

Conflict is an essential element of interesting stories, but little research in computer narrative has addressed it directly. We present a model of narrative conflict inspired by narratology research and based on Partial Order Causal Link (POCL) planning. This model informs an algorithm called CPOCL which extends previous research in story generation. Rather than eliminate all threatened causal links, CPOCL marks certain steps in a plan as non-executed in order to preserve the conflicting subplans of all characters without damaging the causal soundness of the overall story.
Employing Fuzzy Concept for Digital Improvisational Theatre
Authors: Magerko Brian , Dohogne Peter , Deleon Chris
Published: October 2022

This paper describes the creation of a digital improvisational theatre game, called Party Quirks, that allows a human user to improvise a scene with synthetic actors according to the rules of the real-world Party Quirks improv game. The AI actor behaviors are based on our study of communication strategies between real-life actors on stage and the fuzzy concepts that they employ to define and portray characters. This paper describes the underlying fuzzy concepts used to enable reasoning in ambiguous environments, like improv theatre. It also details the development of content for the system, which involved the creation of a system for animation authoring, design for efficient data reuse, and a work flow centered on Google Docs enabling parallel data entry and rapid iteration.
Build Order Optimization in StarCraft
Authors: Churchill David , Buro Michael
Published: October 2022

In recent years, real-time strategy (RTS) games have gained interest in the AI research community for their multitude of challenging subproblems — such as collaborative pathfinding, effective resource allocation and unit targeting, to name a few. In this paper we consider the build order problem in RTS games in which we need to find concurrent action sequences that, constrained by unit dependencies and resource availability, create a certain number of units and structures in the shortest possible time span. We present abstractions and heuristics that speed up the search for approximative solutions considerably in the game of StarCraft, and show the efficacy of our method by comparing its real-time performance with that of professional StarCraft players.
Personalized Procedural Content Generation to Minimize Frustration and Boredom Based on Ranking Algorithm
Authors: Yu Hong , Trawick Tyler
Published: October 2022

A growing research community is working towards procedurally generating content for computer games and simulation applications with various player modeling techniques. In this paper, we present a two-step procedural content generation framework to minimize players' frustration and/or boredom according to player feedback and gameplay features. In the first step, we dynamically categorize the player styles based on a simple questionnaire beforehand and the gameplay features. In the second step, two player models (frustration and boredom) are built for each player style category. A ranking algorithm is utilized for player modeling to address two problems inherent in player feedback: inconsistency and inaccuracy. Experiment results on a testbed game show that our framework can generate less boring/frustrating levels with very high probabilities.
The Story Workbench: An Extensible Semi-Automatic Text Annotation Tool
Authors: Finlayson Mark
Published: October 2022

Text annotations are of great use to researchers in the language sciences, and much effort has been invested in creating annotated corpora for an wide variety of purposes. Unfortunately, software support for these corpora tends to be quite limited: it is usually ad-hoc, poorly designed and documented, or not released for public use. I describe an annotation tool, the Story Workbench, which provides a generic platform for text annotation. It is free, open-source, cross-platform, and user friendly. It provides a number of common text annotation operations, including representations (e.g., tokens, sentences, parts of speech), functions (e.g., generation of initial annotations by algorithm, checking annotation validity by rule, fully manual manipulation of annotations) and tools (e.g., distributing texts to annotators via version control, merging doubly-annotated texts into a single file). The tool is extensible at many different levels, admitting new representations, algorithm, and tools. I enumerate ten important features and illustrate how they support the annotation process at three levels: (1) annotation of individual texts by a single annotator, (2) double-annotation of texts by two annotators and an adjudicator, and (3) annotation scheme development. The Story Workbench is scheduled for public release in March 2012.
Glengarry Glen Ross: Using BDI for Sales Game Dialogues
Authors: Muller Tijmen , Heuvelink Annerieke , Bosch, van den Karel , Swartjes Ivo
Published: October 2022

Serious games offer an opportunity for players to learn communication skills by practicing conversations with nonplaying characters (NPCs). To realize this potential, the player needs freedom of play to discover the relationships between its actions and their effects on the partner and the conversation. Scripting is currently the common approach to design in-game dialogue. Although scripting is a robust technique, the approach tends to produce deterministic conversations, allowing little control to the player. It is claimed that a Belief-Desire-Intention (BDI) approach to model the behavior of NPCs allows greater freedom to the player, and delivers better scalability and re-use of dialogues. This claim is evaluated by using BDI in the development of a sales-talk training game in the real-estate domain. It is concluded that BDI enables representative NPCs that respond appropriately and the game allows the player its freedom of choice to explore. The results also showed that BDI brings about new challenges to address, in order to further increase the quality of in-game dialogue.
Third Eye Crime: Building a Stealth Game Around Occupancy Maps
Authors: Isla Damián
Published: October 2022

Rarely does an AI algorithm form the core mechanic of a commercial game. But this is what happened in the case of Third Eye Crime, an upcoming stealth puzzler by Moonshot Games, soon to be released on iOS, whose gameplay relies heavily on the use of Occupancy Maps.
Incorporating Human Relationships Into Path Planning
Authors: Sturtevant Nathan
Published: October 2022

While pathfinding is ubiquitous in games, it is most often abstracted away fromeverything else that is going on in the game and viewed as a simple point-to-pointshortest path problem. The notable exception to this is work on tactical pathfinding.Tactical pathfinding incorporates tactical elements, most often for battle simulations,into the pathfinding engine in order to improve high-level behavior. This paper looksat how the relationship between characters in a game should influence the pathsthat are taken through the game. We suggest a simple model of character relationshipsand show how this can be used to influence the paths that are taken through theworld. Finally, we discuss design-related issues of how this could be incorporatedinto games.
Towards a Generic Method of Evaluating Game Levels
Authors: Liapis Antonios , Yannakakis Georgios , Togelius Julian
Published: October 2022

This paper addresses the problem of evaluating the quality of game levels across different games and even genres, which is of key importance for making procedural content generation and assisted game design tools more generally applicable. Three game design patterns are identified for having high generality while being easily quantifiable: area control, exploration and balance. Formulas for measuring the extent to which a level includes these concepts are proposed, and evaluation functions are derived for levels in two different game genres: multiplayer strategy game maps and single-player roguelike dungeons. To illustrate the impact of these evaluation functions, and the similarity of impact across domains, sets of levels for each function are generated using a constrained genetic algorithm. The proposed measures can easily be extended to other game genres.
Predicting Army Combat Outcomes in StarCraft
Authors: Stanescu Marius , Poo Hernandez Sergio , Erickson Graham , Greiner Russel , Buro Michael
Published: October 2022

Smart decision making at the tactical level is important for Artificial Intelligence (AI) agents to perform well in the domain of real-time strategy (RTS) games.  This paper presents a Bayesian model that can be used to predict the outcomes of isolated battles, as well as predict what units are needed to defeat a given army.  Model parameters are learned from simulated battles, in order to minimize the dependency on player skill.  We apply our model to the game of StarCraft,  with the end-goal of using the predictor as a module for making high-level combat decisions, and show that the model is capable of making accurate predictions.
“If it didn’t happen, why would I change my decision?”: How Judges Respond to Counterfactual Explanations for the Public Safety Assessment
Authors: Yacoby Yaniv , Green Ben , Griffin Jr. Christopher L. , Doshi-Velez Finale
Published: October 2022

Many researchers and policymakers have expressed excitement about algorithmic explanations enabling more fair and responsible decision-making. However, recent experimental studies have found that explanations do not always improve human use of algorithmic advice. In this study, we shed light on how people interpret and respond to counterfactual explanations (CFEs)---explanations that show how a model's output would change with marginal changes to its input(s)---in the context of pretrial risk assessment instruments (PRAIs). We ran think-aloud trials with eight sitting U.S. state court judges, providing them with recommendations from a PRAI that includes CFEs. We found that the CFEs did not alter the judges' decisions. At first, judges misinterpreted the counterfactuals as real---rather than hypothetical---changes to defendants. Once judges understood what the counterfactuals meant, they ignored them, stating their role is only to make decisions regarding the actual defendant in question. The judges also expressed a mix of reasons for ignoring or following the advice of the PRAI without CFEs. These results add to the literature detailing the unexpected ways in which people respond to algorithms and explanations.
They also highlight new challenges associated with improving human-algorithm collaborations through explanations.