Learning Context-Sensitive Strategies in Space Fortress

Agarwal, Akshat; Hope, Ryan M.; Sycara, Katia

doi:10.1109/ro-man46459.2019.8956452

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…A recent effort has applied the deep reinforcement learning approach to Space Fortress (Agarwal, Hope, & Sycara, 2018). Standard state-of-the-art reinforcement algorithms were not able to learn to play this game.…”

Section: Discussionmentioning

confidence: 99%

Learning rapid and precise skills.

Anderson¹,

Betts²,

Bothell³

et al. 2019

Psychological Review

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A theory is presented about how instruction and experience combine to produce human fluency in a complex skill. The theory depends critically on 4 aspects of the ACT-R architecture. The first is the timing of various modules, particularly motor timing, which results in behavior that closely matches human behavior. The second is the ability to interpret declarative representations of instruction so that they lead to action. The third aspect concerns how practice converts this declarative knowledge into a procedural form so that appropriate actions can be quickly executed. The fourth component, newly added to the architecture, is a Controller module that learns the setting of control variables for actions. The overall theory is implemented in a computational model that is capable of simulating human learning. Its predictions are confirmed in a first experiment involving 2 games derived from the experimental video game Space Fortress. The second experiment tests predictions from the Controller module about lack of transfer between video games. Across the 2 experiments a single model, with the same parameter settings, is shown to simulate human learning of 3 video games.

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Section: Discussionmentioning

confidence: 99%

Learning rapid and precise skills.

Anderson¹,

Betts²,

Bothell³

et al. 2019

Psychological Review

Self Cite

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“…In this work (Li et al, 2020), the researchers identified the components of team adaptation in more detail, with respect to not only the final outcome (performance measure) but also the adaptive behaviors that occur as the mission evolves, a quantification that is novel in the human factors and multi-agent literature. Space Fortress (Agarwal et al, 2019), a game that has been used extensively for psychological research, was adapted for investigate teams. Team/Co-Op Space Fortress (TSF) is a 2-D cooperative game where two players control spaceships to destroy a fortress.…”

Section: Human Teammate Substitutementioning

confidence: 99%

Human–Autonomy Teaming: Definitions, Debates, and Directions

et al. 2021

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Researchers are beginning to transition from studying human–automation interaction to human–autonomy teaming. This distinction has been highlighted in recent literature, and theoretical reasons why the psychological experience of humans interacting with autonomy may vary and affect subsequent collaboration outcomes are beginning to emerge (de Visser et al., 2018; Wynne and Lyons, 2018). In this review, we do a deep dive into human–autonomy teams (HATs) by explaining the differences between automation and autonomy and by reviewing the domain of human–human teaming to make inferences for HATs. We examine the domain of human–human teaming to extrapolate a few core factors that could have relevance for HATs. Notably, these factors involve critical social elements within teams that are central (as argued in this review) for HATs. We conclude by highlighting some research gaps that researchers should strive toward answering, which will ultimately facilitate a more nuanced and complete understanding of HATs in a variety of real-world contexts.

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“…To capture the demands of mutually dependent teamwork, we are studying teams that cooperate in Coop-Space Fortress (CSF) (Agarwal, Hope, & Sycara, 2019), a flexible and extensible simulation test-bed we have developed. In collaborative tasks, the final outcome is determined by factors at the individual level (e.g.…”

Section: Introductionmentioning

confidence: 99%

Team Synchronization and Individual Contributions in Coop-Space Fortress

Agrawal

et al. 2020

Proceedings of the Human Factors and Ergonomics Society Annual

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This work studied human teamwork with a concentration on the influence of team synchronization and in- dividual differences on performance. Human participants were paired to complete collaborative tasks in a simulated game environment, in which they were assigned roles with corresponding responsibilities. Cross- correlation analysis was employed to quantify the degree of team synchronization and time-lag between two teammates’ collective actions. Results showed that team performance is determined by factors at both the individual and team levels. We found interaction effects between team synchronization and individual differences and quantified their contributions to team performance. The application of our research findings and proposed quantitative methods for developing adaptive agents for human-autonomy teaming is discussed.

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Learning Context-Sensitive Strategies in Space Fortress

Cited by 4 publications

References 12 publications

Learning rapid and precise skills.

Learning rapid and precise skills.

Human–Autonomy Teaming: Definitions, Debates, and Directions

Team Synchronization and Individual Contributions in Coop-Space Fortress

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