We present a case-based reasoning technique for selecting build orders in a real-time strategy game. The case retrieval process generalizes features of the game state and selects cases using domain-specific recall methods, which perform exact matching on a subset of the case features. We demonstrate the performance of the technique by implementing it as a component of the integrated agent frame...

Bots for Real Time Strategy (RTS) games provide a rich challenge to implement. A bot controls a number of units that may have to navigate in a partially unknown environment, while at the same time search for enemies and coordinate attacks to fight them down. Potential fields is a technique originating from the area of robotics where it is used in controlling the navigation of robots in dynamic ...

Given a Markov game, it is often possible to hand-code or learn a set of policies that capture a diversity of possible strategies. It is also often possible to hand-code or learn an abstract simulator of the game that can estimate the outcome of playing two strategies against one another from any state.simulator of the game that can estimate the outcome of playing two strategies against one ano...

Behavior authoring for computer games involves writing behaviors in a programming language and then iteratively refining them by detecting issues with them. The main bottlenecks are a) the effort required to author the behaviors and b) the revision cycle as, for most games, it is practically impossible to write a behavior for the computer game AI in a single attempt. The main problem is that th...

In this paper we investigate how STRIPS planning techniques can be used to enhance the behavior of worker units that are common in real-time strategy (RTS) video games. Worker units are typically instructed to carry out simple tasks such as moving to destinations or mining for a type of resource. In this work we investigate how this interaction can be extended by providing the human player with...

This project examines overfitting and generalization error with Monte Carlo Tree Search in the real-time strategy video game StarCraft and provides a basis for evaluating the specific scenarios of StarCraft based on their estimated generalization error. How can we tell if we are overfitting the input parameters to specific scenarios in StarCraft? How well does a specific scenario of StarCraft g...

How should an AI-based explanation system explain an agent’s complex behavior to ordinary end users who have no background in AI? Answering this question is an active research area, for if an AI-based explanation system could effectively explain intelligent agents’ behavior, it could enable the end users to understand, assess, and appropriately trust (or distrust) the agents attempting to help ...

A commonly used technique for managing AI complexity in real-time strategy (RTS) games is to use action and/or state abstractions. High-level abstractions can often lead to goodions. High-level abstractions can often lead to good strategic decision making, but tactical decision quality may suffer due to lost details. A competing method is to sample the search space which often leads to good tac...