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Learning interaction rules from multi-animal trajectories via augmented behavioral models [article]

Keisuke Fujii, Naoya Takeishi, Kazushi Tsutsui, Emyo Fujioka, Nozomi Nishiumi, Ryoya Tanaka, Mika Fukushiro, Kaoru Ide, Hiroyoshi Kohno, Ken Yoda, Susumu Takahashi, Shizuko Hiryu (+1 others)
2021 arXiv   pre-print
In this paper, we propose a new framework for learning Granger causality from multi-animal trajectories via augmented theory-based behavioral models with interpretable data-driven models.  ...  We adopt an approach for augmenting incomplete multi-agent behavioral models described by time-varying dynamical systems with neural networks.  ...  For obtaining flies data, we would like to thank Ryota Nishimura at Nagoya University.  ... 
arXiv:2107.05326v3 fatcat:yxj3vbgmivc4jggw5cersb2mnm

How to design agent-based simulation models using agent learning

Robert Junges, Franziska Klugl
2012 Proceedings Title: Proceedings of the 2012 Winter Simulation Conference (WSC)  
Results demonstrate the agents can learn and report back to the modeler a behavior that is interestingly better than a hand-made model.  ...  In this contribution we introduce the MABLe methodology for analyzing and designing agent simulation models that relies on adaptive agents, where the agent helps the modeler by proposing a suitable behavior  ...  Figure 2 : 2 Behavior trees for different objective function details putting more focus on collision avoidance or fast evacuation.  ... 
doi:10.1109/wsc.2012.6465017 dblp:conf/wsc/JungesK12 fatcat:luluffgfijbxbhqxzofabdkene

Teachable Agent [article]

Ailiya Borjigin
2015 arXiv   pre-print
The ATA model begins with the analysis of pedagogical requirements and teaching goals, using Learning by Teaching theory to design interventions which can authentically promote the learning behaviors of  ...  Apart from the benefits, existing TA design also has limitations.  ...  OCC Model The OCC model assumes that emotions can arise from the evaluation of three aspects of the world: events, agents, and objects.  ... 
arXiv:1502.02370v1 fatcat:pjm24rflvbe5behvlupz4w6dd4


Stephen Grand, Dave Cliff
2012 Autonomous Agents and Multi-Agent Systems  
A biologically inspired learning mechanism allows the neural network to adapt during the lifetime of a creature. Learning includes the ability to acquire a simple verb-object language.  ...  Each creature has a neural network responsible for sensory-motor coordination and behavior selection, and an "artificial biochemistry" that models a simple energy metabolism along with a "hormonal" system  ...  In keeping with standard biology terminology, we refer to a neuron's input-connections as 'dendrites'.  ... 
doi:10.1023/a:1010042522104 fatcat:ssmo62hspfdspkockk65qgn5vq

Learn to Interpret Atari Agents [article]

Zhao Yang and Song Bai and Li Zhang and Philip H.S. Torr
2019 arXiv   pre-print
However, the direct mapping from states to actions makes it hard to interpret the rationale behind the decision making of agents.  ...  RS-Rainbow utilizes a simple yet effective mechanism to incorporate visualization ability into the learning model, not only improving model interpretability, but leading to improved performance.  ...  In this work, we approach from a learning perspective, and propose Region Sensitive Rainbow (RS-Rainbow) to improve both the interpretability and performance of a DeepRL model.  ... 
arXiv:1812.11276v2 fatcat:74otbxhjgzbnbnnofottgm4xh4

Observer effect from stateful resources in agent sensing

Adam Eck, Leen-Kiat Soh
2012 Autonomous Agents and Multi-Agent Systems  
In this model, the agent uses reinforcement learning to learn a controller for action selection, as well as how to predict expected knowledge refinement based on resource use during sensing.  ...  In many real-world applications of multi-agent systems, agent reasoning suffers from bounded rationality caused by both limited resources and limited knowledge.  ...  Acknowledgments We would like to thank the anonymous reviewers for their constructive feedback and helpful suggestions, which were used to improve the presentation of this research.  ... 
doi:10.1007/s10458-011-9189-y fatcat:dxlr5kjfxjgrhmekut5374y3bu

Differentiable Agent-based Epidemiology [article]

Ayush Chopra, Alexander Rodríguez, Jayakumar Subramanian, Balaji Krishnamurthy, B. Aditya Prakash, Ramesh Raskar
2022 arXiv   pre-print
Agent-based models (ABMs) are an increasingly popular alternative paradigm that can represent the heterogeneity of contact interactions with granular detail and agency of individual behavior.  ...  Mechanistic simulators are an indispensable tool for epidemiology to explore the behavior of complex, dynamic infections under varying conditions and navigate uncertain environments.  ...  Conventional ABM frameworks follow an object-oriented (agent-centered) design where the agents are modeled as objects.  ... 
arXiv:2207.09714v1 fatcat:deumu4uejne5phdeorxb4mg3me

Heterogeneous trading agents

Luc Neuberg, Koen Bertels
2003 Complexity  
The observed results are not sensitive to changes in the parameter values.  ...  The model consists of autonomous, interactive agents that buy stock on a financial market. Transaction decisions are based on a number of individual and collective elements.  ...  We first introduce the model and explain how each agent is modelled and how their interaction results in the overall market behavior.  ... 
doi:10.1002/cplx.10098 fatcat:hfgbuzfrkrbcpaxqr27r2ol6fe

Efficient Value Function Approximation with Unsupervised Hierarchical Categorization for a Reinforcement Learning Agent

Yongjia Wang, John E. Laird
2010 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology  
We investigate the problem of reinforcement learning (RL) in a challenging object-oriented environment, where the functional diversity of objects is high, and the agent must learn quickly by generalizing  ...  The system is empirically evaluated in an artificial domain consisting of interacting objects with diverse functional properties and multiple functional roles.  ...  This research was supported in part by the Ground Robotics Reliability Center (GRRC) at the University of Michigan, with funding from government contract DoD-DoA W56H2V-04-2-0001 through the Joint Center  ... 
doi:10.1109/wi-iat.2010.16 dblp:conf/iat/WangL10 fatcat:c6dduyyulfftlkcalplkpsrnaa

Performance Testing Using a Smart Reinforcement Learning-Driven Test Agent [article]

Mahshid Helali Moghadam, Golrokh Hamidi, Markus Borg, Mehrdad Saadatmand, Markus Bohlin, Björn Lisper, Pasqualina Potena
2021 arXiv   pre-print
Model-free reinforcement learning is widely used for finding the optimal behavior to accomplish an objective in many decision-making problems without relying on a model of the system.  ...  This paper proposes that if the optimal policy (way) for generating test workload to meet a test objective can be learned by a test agent, then efficient test automation would be possible without relying  ...  ACKNOWLEDGMENT This work has been supported by and received funding from the TESTOMAT and IVVES European projects.  ... 
arXiv:2104.12893v1 fatcat:6af2kjgqgfftbhb7eu4lfzbzkm


Stephen Grand, Dave Cliff, Anil Malhotra
1997 Proceedings of the first international conference on Autonomous agents - AGENTS '97  
A Hebbian learning mechanism allows the neural network to adapt during the lifetime of a creature.  ...  Creatures, available on Windows95 and Macintosh platforms from late 1996, offers users an opportunity to engage with Artificial Life technologies.  ...  Acknowledgements Thanks to all at Millennium Interactive Ltd. for their involvement and permission to publish this work.  ... 
doi:10.1145/267658.267663 dblp:conf/agents/GrantCM97 fatcat:qw2svqd2dna43h3iuc6s6rcap4

Adaptive Agent Architecture for Real-time Human-Agent Teaming [article]

Tianwei Ni, Huao Li, Siddharth Agrawal, Suhas Raja, Fan Jia, Yikang Gui, Dana Hughes, Michael Lewis, Katia Sycara
2021 arXiv   pre-print
Most literature in human-agent teaming builds agents referencing a learned human model.  ...  Teamwork is a set of interrelated reasoning, actions and behaviors of team members that facilitate common objectives.  ...  The reward functions attempt to encode the desirable behavior of a bait agent.  ... 
arXiv:2103.04439v1 fatcat:2ltzoymcjzeqnm4c3f3bepc6je

Aging agents

Elpida S. Tzafestas
2001 Artificial Life and Robotics  
We are therefore seeking a senescence function that favors social rather than solitary agents in terms of longevity, without prespecifying in detail the agent's life span.  ...  We show that a senescence function relying on negative (destructive) feedback links from metabolism to genetic program conforms with these specifications.  ...  Acknowledgments I wish to thank Bernard Victorri for useful suggestions. I also thank the volunteers who  ... 
doi:10.1007/bf02481320 fatcat:id6gh5f3nzhijdxjpsziyauuru

Agents in annotated worlds

Patrick Doyle, Barbara Hayes-Roth
1998 Proceedings of the second international conference on Autonomous agents - AGENTS '98  
Agents that function effectively in heterogeneous virtual spaces must have the ability to acquire new behaviors and useful semantic information from those contexts.  ...  The human-computer interaction literature discusses how to construct spaces and objects that provide "knowledge in the world" that aids human beings to perform these tasks.  ...  ., gifts from Intel, and Seed Grants from Stanford's Center for the Study of Language and Information and Office of Technology Licensing.  ... 
doi:10.1145/280765.280797 dblp:conf/agents/DoyleH98 fatcat:sh4n52fqdbgupn4h6xn25sld64

Integrated Multi-Agent Approach to Network Security Assurance: Models of Agents' Community [chapter]

V. Gorodetski, I. Kotenko, V. Skormin
2000 IFIP Advances in Information and Communication Technology  
Emphasis is given to a description of the operation and learning mechanisms implemented in the security agents.  ...  In this paper, an integrated multi-agent approach to construction ofNetwork Security System (NSS) is considered.  ...  The discretionary ACR check agents inspect correspondence of the messages from subjects to objects on the basis of discretionary ACR base. The base represents multilevel data structure.  ... 
doi:10.1007/978-0-387-35515-3_30 fatcat:igvth4fhdbd25jbw6kpv62gziq
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