Using a Cognitive Architecture for Opponent Target Prediction

One of the most important aspects of a compelling game AI is that it anticipates the player’s actions and responds to them in a convincing manner. The first step towards doing this is to understand what the player is doing and predict their possible future actions. In this paper we show an approach where the AI system focusses on testing hypotheses made about the player’s actions using an implementation of a cognitive architecture inspired by the simulation theory of mind. The application used in this paper is to predict the target that the player is heading towards, in an RTS-style game. We improve the prediction accuracy and reduce the number of hypotheses needed by using path planning and path clustering

Middle and elementary school students’ changes in self-determined motivation in a basketball unit taught using the Tactical Games Model

Studies examining student motivation levels suggest that this is a significant factor in students’ engagement in physical education and may be positively affected when teachers employ alternative pedagogical models such as game-centered approaches (GCAs). The aim of this study was to investigate changes in self-determined motivation of students as they participated in a GCA-basketball unit taught using the Tactical Games Model (TGM). Participants were 173 students (84 girls), 79 middle school (45 girls) and 94 (39 girls) elementary school students from four seventh and five fourth/fifth grade co-educational classes. Two teachers taught 32 (middle) and 33 (elementary) level one TGM basketball lessons. Need satisfaction and self-determined motivation data were collected using a previously validated instrument, while lesson context and teacher behavior data were recorded using systematic observation instruments. Repeated measures MANOVAs were employed to examine pre-posttest differences. Results revealed a significant main effect for time in need satisfaction for both middle (relatedness increased) and elementary school students (autonomy decreased) and a significant main effect in self-determined motivation for middle school students only (introjected regulation, external regulation, and amotivation all increased). Approximately 48%/42% (middle/elementary) of lesson time was game play, 22%/22% skill practice, 17%/17% management, and 13%/19% knowledge. The primary teacher behaviors used were instruction, management, specific observation, corrective feedback and modelling. Results indicate that it is important for future research to pay greater attention to the contextual factors associated with the application of the TGM, such as the students’ previous exposure to TGM lessons, and the teachers’ training and experience in utilizing the TGM. Indeed, results of the present study demonstrate that a longer-term commitment to the TGM is necessary to reduce controlling teacher behaviors, which will lead to positive changes in students’ need satisfaction and self-determined motivation. Future research is therefore needed to embrace this challenge to provide an increased evidence-base for GCAs such as the TGM

Allocating Limited Resources to Protect a Massive Number of Targets using a Game Theoretic Model

Resource allocation is the process of optimizing the rare resources. In the area of security, how to allocate limited resources to protect a massive number of targets is especially challenging. This paper addresses this resource allocation issue by constructing a game theoretic model. A defender and an attacker are players and the interaction is formulated as a trade-off between protecting targets and consuming resources. The action cost which is a necessary role of consuming resource, is considered in the proposed model. Additionally, a bounded rational behavior model (Quantal Response, QR), which simulates a human attacker of the adversarial nature, is introduced to improve the proposed model. To validate the proposed model, we compare the different utility functions and resource allocation strategies. The comparison results suggest that the proposed resource allocation strategy performs better than others in the perspective of utility and resource effectiveness.Comment: 14 pages, 12 figures, 41 reference

Cooperative Games with Overlapping Coalitions

In the usual models of cooperative game theory, the outcome of a coalition formation process is either the grand coalition or a coalition structure that consists of disjoint coalitions. However, in many domains where coalitions are associated with tasks, an agent may be involved in executing more than one task, and thus may distribute his resources among several coalitions. To tackle such scenarios, we introduce a model for cooperative games with overlapping coalitions--or overlapping coalition formation (OCF) games. We then explore the issue of stability in this setting. In particular, we introduce a notion of the core, which generalizes the corresponding notion in the traditional (non-overlapping) scenario. Then, under some quite general conditions, we characterize the elements of the core, and show that any element of the core maximizes the social welfare. We also introduce a concept of balancedness for overlapping coalitional games, and use it to characterize coalition structures that can be extended to elements of the core. Finally, we generalize the notion of convexity to our setting, and show that under some natural assumptions convex games have a non-empty core. Moreover, we introduce two alternative notions of stability in OCF that allow a wider range of deviations, and explore the relationships among the corresponding definitions of the core, as well as the classic (non-overlapping) core and the Aubin core. We illustrate the general properties of the three cores, and also study them from a computational perspective, thus obtaining additional insights into their fundamental structure