Application of Retrograde Analysis on Fighting Games

Yu, Kristen K.; Sturtevant, Nathan R.

doi:10.1109/cig.2019.8848062

Cited by 6 publications

(4 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…My previous work focused on an application of AI to fighting games to better understand game balancing and game design (Yu and Sturtevant 2019). The goal of the AI is not to play the game the best, but instead is an AI tool for the developer to be able to identify moves that could be considered over-or under-powered.…”

Section: Previous Workmentioning

confidence: 99%

Personalized Procedural Content Generation for Increased Player Agency

2020

AIIDE

View full text Add to dashboard Cite

My interest is in the intersection between AI directors, player modeling, and procedural content generation. Using a combination of these tools, I believe that we can expand the space of tellable stories to create a story that is completely driven by the player’s actions in the game. There are two use cases for this work. The first is adding additional content to an open-world game by letting the player influence the world after the main story is completed, which would help address the problem of replayability. The second is to allow the player to create a completely personalized story from the beginning of the game, using the starting situation of the game as if it was the start of a simulation.

show abstract

Section: Previous Workmentioning

confidence: 99%

Personalized Procedural Content Generation for Increased Player Agency

2020

AIIDE

View full text Add to dashboard Cite

show abstract

“…This approach has proven effective in fast-paced fighting games where experienced players can reliably predict opponents' moves. Extensions apply hidden Markov models [19] and AI agents [20], [21] to improve fighting game input predictions. However, current methods narrowly focus on one specific genre and may not generalize well to others.…”

Section: Introductionmentioning

confidence: 99%

Pattern Reduction for Low-Traffic Speculative Video Transmission in Cloud Gaming System

Ishioka,

Fukui,

Fujiwara

et al. 2024

IEEE Access

View full text Add to dashboard Cite

Cloud gaming enables users to play high-quality games on low-end devices by offloading game processing to the cloud; however, network latency remains a significant issue affecting the gaming experience. Speculative execution is a promising approach to hide network latency by predicting and transmitting future frames early. However, existing methods generate excessive compute load and network traffic because of the numerous potential input patterns. This paper proposes a pattern reduction method using bit field representations of inputs to enable efficient speculative execution for cloud gaming. Two methods are introduced: temporal pattern analysis to detect frequent transitions and Long Short-Term Memory (LSTM) based prediction to estimate input probabilities. Experiments using actual gaming data show that the proposed methods significantly reduce rendered frames and network traffic versus prior speculative execution methods. The results demonstrate the method's effectiveness and scalability across diverse game genres.INDEX TERMS cloud-gaming, speculative execution, low-latency VOLUME 4, 2016

show abstract

“…[ 39–41 ] The action at a high level is not well understood, although traditional studies have demonstrated excellent performance in solo maneuvers or progressed to simple navigation scenarios. [ 11,42–44 ]…”

Section: Introductionmentioning

confidence: 99%

“…[39][40][41] The action at a high level is not well understood, although traditional studies have demonstrated excellent performance in solo maneuvers or progressed to simple navigation scenarios. [11,[42][43][44] Human athletes are required to be highly skilled in four areas: 1) maneuver tactics, 2) human-body control, 3) movement strategy, and 4) navigation etiquette, to be effective. The human…”

mentioning

confidence: 99%

A Phase‐Change Memristive Reinforcement Learning for Rapidly Outperforming Champion Street‐Fighter Players

Go,

Jiang,

Loke

2023

Advanced Intelligent Systems

View full text Add to dashboard Cite

The interactions with humans, and simultaneously, making of real‐time decisions in physical systems, are involved in many applications of artificial intelligence. An example of these conditions is maneuver sports. Movement‐type simulations, viz., the esports game Street Fighter (SF), recapitulate the complex multicharacter interactions and, concurrently, generate the millisecond‐level control challenges of human athletes. Herein, the physical and mental signatures of the SF agent (it is called SF R2) are controlled by utilizing a previously unreported model‐free, natural, deep reinforcement learning algorithm “Decay‐based Phase‐change memristive character‐type Proximal Policy Optimization” (DP‐PPO) through an assemblage of hybrid case‐type training processes; and an integrated training configuration for time‐trial evaluations, as well as competitions with a world's best SF player, is developed. A short length of time utilized by the SF R2 to defeat the opponent and, simultaneously, maintaining a good health level is achieved, as well as excellent handling of imperfect information settings. Training studies reveal a moderate maneuver etiquette in the SF R2, along with rapid, effective head‐to‐head competitions with one of the world's best SF player. This paves the way for achieving a broadly applicable training scheme, capable of quickly controlling complicated‐movement systems in fields where agents should observe unspecified human norms.

show abstract

Application of Retrograde Analysis on Fighting Games

Cited by 6 publications

References 36 publications

Personalized Procedural Content Generation for Increased Player Agency

Personalized Procedural Content Generation for Increased Player Agency

Pattern Reduction for Low-Traffic Speculative Video Transmission in Cloud Gaming System

A Phase‐Change Memristive Reinforcement Learning for Rapidly Outperforming Champion Street‐Fighter Players

Contact Info

Product

Resources

About