This article introduces a teacher-student framework for reinforcement learning, synthesizing and extending material that appeared in conference proceedings [22] and in a non-archival workshop paper [6]. In this framework, a teacher agent instructs a student agent by suggesting actions the student should take as it learns. However, the teacher may only give such advice a limited number of times....

We consider how Lorentz-violating interactions in the Faddeev-Popov ghost sector will affect scalar QED. The behavior depends sensitively on whether the gauge symmetry is spontaneously broken. If the symmetry is not broken, Lorentz violations in the ghost sector are unphysical, but if there is spontaneous breaking, radiative corrections will induce Lorentz-violating and gauge-dependent terms in...

Real time strategy (RTS) games provide various research areas for Artificial Intelligence. One of these areas involves the management of either individual or small group of units, called micromanagement. This research provides an approach that implements an imitation of the player’s decisions as a mean for micromanagement combat in the RTS game Starcraft. A bayesian network is generated to fit ...

The study of expertise is difficult to do in a laboratory environment due to the challenge of finding people at different skill levels and the lack of time for participants to acquire mastery. In this paper, we report on two studies that analyze naturalistic gameplay data using cohort analysis to better understand how skill relates to practice and habit. Two cohorts are analyzed, each from two ...

Real-time strategy (RTS) games pose challenges to AI research on many levels, ranging from selecting targets in unit combat situations, over efficient multi-unit pathfinding, to high-level economic decisions. Due to the complexity of RTS games, writing competitive AI systems for these games requires high speed adaptive algorithms and simplified models

This paper presents a constraint optimization approach to walling in real-time strategy (RTS) games. Walling is a specific type of spatial reasoning, typically employed by human expert players and not currently fully exploited in RTS game AI, consisting on finding configurations of buildings to completely or partially block paths. Our approach is based on local search, and is specifically desig...

From an AI point of view, Real-Time Strategy (RTS) games are hard because they have enormous state spaces, they are real-time and partially observable. In this paper, we explore an approach to deploy gametree search in RTS games by using game state abstraction, and explore the effect of using different abstractions over the game state. Different abstractions capture different parts of the game ...

A major challenge in the field of case-based reasoning is building case libraries representative of the state space the system will encounter. We approach this problem by automating the process of converting expert demonstrations, in the form of game replays, into cases. To achieve this, we present a technique for annotating traces with goals that can be used by a case-based planner. We have im...