Modeling player engagement is a key challenge in games. However, the gameplay signatures of engaged players can be highly context-sensitive, varying based on where the game is used or what population of players is using it. Traditionally, models of player engagement are investigated in a particular context, and it is unclear how effectively these models generalize to other settings and populations. In this work, we investigate a Bayesian hierarchical linear model for multi-task learning to devise a model of player engagement from a pair of datasets that were gathered in two complementary contexts: a Classroom Study with middle school students and a Laboratory Study with undergraduate students. Both groups of players used similar versions of Crystal Island, an educational interactive narrative game for science learning. Results indicate that the Bayesian hierarchical model outperforms both pooled and context-specific models in cross-validation measures of predicting player motivation from in-game behaviors, particularly for the smaller Classroom Study group. Further, we find that the posterior distributions of model parameters indicate that the coefficient for a measure of gameplay performance significantly differs between groups. Drawing upon their capacity to share information across groups, hierarchical Bayesian methods provide an effective approach for modeling player engagement with data from similar, but different, contexts.
Min, Wookhee (North Carolina State University) | Baikadi, Alok (University of Pittsburgh) | Mott, Bradford (North Carolina State University) | Rowe, Jonathan (North Carolina State University) | Liu, Barry (North Carolina State University) | Ha, Eun Young (IBM) | Lester, James (North Carolina State University)
Recent years have seen a growing interest in player modeling, which supports the creation of player-adaptive digital games. A central problem of player modeling is goal recognition, which aims to recognize players’ intentions from observable gameplay behaviors. Player goal recognition offers the promise of enabling games to dynamically adjust challenge levels, perform procedural content generation, and create believable NPC interactions. A growing body of work is investigating a wide range of machine learning-based goal recognition models. In this paper, we introduce GOALIE, a multidimensional framework for evaluating player goal recognition models. The framework integrates multiple metrics for player goal recognition models, including two novel metrics, n-early convergence rate and standardized convergence point . We demonstrate the application of the GOALIE framework with the evaluation of several player goal recognition models, including Markov logic network-based, deep feedforward neural network-based, and long short-term memory network-based goal recognizers on two different educational games. The results suggest that GOALIE effectively captures goal recognition behaviors that are key to next-generation player modeling.
As the non-playable characters (NPCs) of squad-based shooter computer games share a common goal, they should work together in teams and display cooperative behaviours that are tactically sound. Our research examines genetic programming (GP) as a technique to automatically develop effective team behaviours for shooter games. GP has been used to evolve teams capable of defeating a single powerful enemy agent in a number of environments without the use of any explicit team communication. The aim of this paper is to explore the effects of communication on the evolution of effective squad behaviours. Thus, NPCs are given the ability to communicate their perceived information during evolution. The results show that communication between team members enables an improvement in average team effectiveness.
New research from DeepMind, Alphabet Inc.'s London-based artificial intelligence unit could ultimately shed light on this fundamental question. They have been investigating the conditions in which reward-optimizing beings, whether human or robot, would chose to cooperate, rather than compete. The answer could have implications for how computer intelligence may eventually be deployed to manage complex systems such as an economy, city traffic flows, or environmental policy. Joel Leibo, the lead author of a paper DeepMind published online Thursday, said in an email that his team's research indicates that whether agents learn to cooperate or compete depends strongly on the environment in which they operate. While the research has no immediate real-world application, it would help DeepMind design artificial intelligence agents that can work together in environments with imperfect information.
Research in AI continues to make video games better. The technology informs NPCs that can move and fight more convincingly, orcs with personalities and ever-more realistic visuals. Now researchers at DeepMind have taught an AI to play a customized version of Quake III Arena like a human. The team focused on a capture the flag mode, one in which the map changes from match to match. Its AI agents had to learn general strategies to be able to adapt to each new map, something humans do easily.