@conference{Venkata2023,
title = {Systems Design Concepts mimicking Bio-inspired Self-assembly},
author = {Sanjay Sarma Oruganti Venkata and Cameron Ardoin and Israr M. Ibrahim and Ramviyas Parasuraman and Ramana M Pidaparti},
url = {https://link.springer.com/chapter/10.1007/978-981-99-0428-0_31},
doi = {10.1007/978-981-99-0428-0_31},
year = {2023},
date = {2023-07-25},
booktitle = {9th International Conference on Research Into Design (ICoRD)},
publisher = {Springer},
abstract = {Design of complex self-assembly systems requires intelligent solutions that can be manufactured effectively and efficiently. Self-organization is the spontaneous formation of organized structures that can dynamically reconfigure with changing environments. These processes are primarily observed in chemical and biological processes that resemble large-scale ecosystems and in environments as small as biological cells. Inspired by these natural processes, there is also a growing research interest in developing 4D Design and printing technologies in which 3D structures reconfigure with changing stimuli. The 4D design process requires appropriate design, computational and simulation tools aimed at building structures at larger scales that can augment the current engineering design and manufacturing processes. This study presents a new multi-agent framework with two new paradigms called agents-as-blocks and free-agent. We present further details on these new strategies in the form of preliminary case studies applied to simulating micro-environments of microtubules’ self-organization process and through a vibration simulation platform. Our simulation results closely follow the real formation patterns in the microtubules process and show some interesting self-organizing and self-assembling patterns that change with varying geometries, rules, and stimuli in a vibration-platform environment.},
keywords = {behavior-trees, design, multiagent-systems},
pubstate = {published},
tppubtype = {conference}
}

@conference{Yang2023,
title = {A hierarchical game-theoretic decision-making for cooperative multiagent systems under the presence of adversarial agents},
author = {Qin Yang and Ramviyas Parasuraman},
url = {https://acmsac-irmas2023.isr.uc.pt/index.php/track-program},
year = {2023},
date = {2023-03-31},
booktitle = {The 38th ACM/SIGAPP Symposium On Applied Computing},
series = {IRMAS},
abstract = {Underlying relationships among Multi-Agent Systems (MAS) in hazardous scenarios can be represented as Game-theoretic models. This paper proposes a new hierarchical network-based model called Game-theoretic Utility Tree (GUT), which decomposes high-level strategies into executable low-level actions for cooperative MAS decisions. It combines with a new payoff measure based on agent needs for real-time strategy games. We present an Explore game domain, where we measure the performance of MAS achieving tasks from the perspective of balancing the success probability and system costs. We evaluate the GUT approach against state-of-the-art methods that greedily rely on the rewards of the composite actions. Conclusive results on extensive numerical simulations indicate that GUT can organize more complex relationships among MAS cooperation, helping the group achieve challenging tasks with lower costs and higher winning rates. Furthermore, we demonstrated the applicability of the GUT using the simulator-hardware testbed - Robotarium. The performances verified the effectiveness of the GUT in the real robot application and validated that the GUT could effectively organize MAS cooperation strategies, helping the group with fewer advantages achieve higher performance.},
note = {Oral Presentation. Acceptance Rate: <25%},
keywords = {cooperation, multi-robot-systems, multiagent-systems, planning},
pubstate = {published},
tppubtype = {conference}
}

@conference{Yang2022,
title = {Game-theoretic Utility Tree for Multi-Robot Cooperative Pursuit Strategy},
author = {Qin Yang and Ramviyas Parasuraman},
url = {Preprint: https://arxiv.org/pdf/2206.01109.pdf
Paper: https://ieeexplore.ieee.org/abstract/document/9861828
Codes: https://github.com/herolab-uga/gut-pursuit-evasion-robotarium},
year = {2022},
date = {2022-06-21},
booktitle = {ISR Europe 2022; 54th International Symposium on Robotics
},
pages = {278-284},
abstract = {Underlying relationships among multiagent systems (MAS) in hazardous scenarios can be represented as game-theoretic models. In adversarial environments, the adversaries can be intentional or unintentional based on their needs and motivations. Agents will adopt suitable decision-making strategies to maximize their current needs and minimize their expected costs. This paper proposes and extends the new hierarchical network-based model, termed Game-theoretic Utility Tree (GUT), to arrive at a cooperative pursuit strategy to catch an evader in the Pursuit-Evasion game domain. We verify and demonstrate the performance of the proposed method using the Robotarium platform compared to the conventional constant bearing (CB) and pure pursuit (PP) strategies. The experiments demonstrated the effectiveness of the GUT, and the performances validated that the GUT could effectively organize cooperation strategies, helping the group with fewer advantages achieve higher performance.},
keywords = {control, multi-robot-systems, multiagent-systems, planning},
pubstate = {published},
tppubtype = {conference}
}

@conference{Sarma2022,
title = {A study on the ephemeral nature of knowledge shared between multi-agent and swarm systems through behavior trees},
author = {Sanjay Sarma and Ramviyas Parasuraman and Ramana Pidaparti},
year = {2022},
date = {2022-01-27},
booktitle = {The 5th International Symposium on Swarm Behavior and Bio-Inspired Robotics (SWARM 2022)},
abstract = {Achieving knowledge sharing within an artificial swarm system could lead to significant development in autonomous multiagent and robotic systems research and realize collective intelligence. However, this is difficult to achieve since there is no generic framework to transfer skills between agents other than a query-response-based approach. Moreover, natural living systems have a "forgetfulness" property for everything they learn. Analyzing such ephemeral nature (temporal memory properties of new knowledge gained) in artificial systems has never been studied in the literature. We propose a behavior tree-based framework to realize a query-response mechanism for transferring skills encoded as the condition-action control subflow of that portion of the knowledge between agents to fill this gap. We simulate a multiagent group with different initial knowledge on a foraging mission. While performing basic operations, each robot queries other robots to respond to an unknown condition. The responding robot shares the control actions by sharing a portion of the behavior tree that addresses the queries. Specifically, we investigate the ephemeral nature of the new knowledge gained through such a framework, where the knowledge gained by the agent is either limited due to memory or is forgotten over time. Our investigations show that knowledge grows proportionally with the duration of remembrance, which is trivial. However, we found minimal impact in knowledge growth due to memory. We compare these cases against a baseline that involved full knowledge precoded on all agents. We found that the knowledge-sharing strived to match the baseline condition by sharing and achieving knowledge growth as a collective system. },
keywords = {behavior-trees, multiagent-systems, swarm-robotics},
pubstate = {published},
tppubtype = {conference}
}

@workshop{Yang2022b,
title = {A Game-theoretic Utility Network for Multi-Agent Decisions in Adversarial Environments},
author = {Qin Yang and Ramviyas Parasuraman},
url = {https://dcslgatech.github.io/iros22-multi-agent-workshop/#section-program
https://dcslgatech.github.io/iros22-multi-agent-workshop/contributed_papers/IROS22-DMMAS_paper_6456.pdf
https://dcslgatech.github.io/iros22-multi-agent-workshop/posters/DMMAS_6456.pdf},
year = {2022},
date = {2022-10-27},
booktitle = {IROS 2022 Workshop on Decision Making in Multi-Agent Systems},
abstract = {Underlying relationships among multi-agent systems (MAS) in hazardous scenarios can be represented as Game theoretic models. This paper proposes a new network-based model called Game-theoretic Utility Tree (GUT), which decomposes high-level strategies into executable low-level actions for cooperative MAS decisions in adversarial environments. It combines a new payoff measure based on agent needs for real-time strategy games. We demonstrated the applicability of the GUT using the Robotarium platform, which is a simulator-hardware testbed for verifying multi-robot system algorithms. The performances verified the effectiveness of the GUT in the real robot application and validated that the GUT could effectively organize MAS cooperation strategies, helping the group with fewer advantages achieve higher performance.},
keywords = {cooperation, multiagent-systems, planning},
pubstate = {published},
tppubtype = {workshop}
}