Ensemble Framework for Real-time Decision Making
This paper introduces a new framework for real-time decision making in video games. An Ensemble agent is a compound agent composed of multiple agents, each with its own tasks or goals to achieve. Usually when dealing with real-time decision making, reactive agents are used; that is agents that return a decision based on the current state. While reactive agents are very fast, most games require more than just a rule-based agent to achieve good results. Deliberative agents---agents that use a forward model to search future states---are very useful in games with no hard time limit, such as Go or Backgammon, but generally take too long for real-time games. The Ensemble framework addresses this issue by allowing the agent to be both deliberative and reactive at the same time. This is achieved by breaking up the game-play into logical roles and having highly focused components for each role, with each component disregarding anything outwith its own role. Reactive agents can be used where a reactive agent is suited to the role, and where a deliberative approach is required, branching is kept to a minimum by the removal of all extraneous factors, enabling an informed decision to be made within a much smaller time-frame. An Arbiter is used to combine the component results, allowing high performing agents to be created from simple, efficient components.
Code (1)
Tasks
Decision MakingSimilar Papers 제목 키워드 기반
One Explanation to Rule them All -- Ensemble Consistent Explanations
Transparency is a major requirement of modern AI based decision making systems deployed in real world. A popular approach for achieving transparency is by means of explanations. A wide variety of different explanations h…
AllcounterfactualDecision MakingLightTS: Lightweight Time Series Classification with Adaptive Ensemble Distillation -- Extended Version
Due to the sweeping digitalization of processes, increasingly vast amounts of time series data are being produced. Accurate classification of such time series facilitates decision making in multiple domains. State-of-the…
ClassificationDecision MakingEnsemble LearningTime Series+2A Deep Ensemble Multi-Agent Reinforcement Learning Approach for Air Traffic Control
Air traffic control is an example of a highly challenging operational problem that is readily amenable to human expertise augmentation via decision support technologies. In this paper, we propose a new intelligent decisi…
Decision MakingManagementMulti-agent Reinforcement Learningreinforcement-learning+2Enhancing Visual Interpretability and Explainability in Functional Survival Trees and Forests
Functional survival models are key tools for analyzing time-to-event data with complex predictors, such as functional or high-dimensional inputs. Despite their predictive strength, these models often lack interpretabilit…
Decision MakingExploiting Uncertainties from Ensemble Learners to Improve Decision-Making in Healthcare AI
Ensemble learning is widely applied in Machine Learning (ML) to improve model performance and to mitigate decision risks. In this approach, predictions from a diverse set of learners are combined to obtain a joint decisi…
Decision MakingEnsemble Learning