Communication in reactive multiagent robotic systems

By Tucker R. Balch, R. Arkin. 1995

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Citations in the corpus

Lenient Multi-Agent Deep Reinforcement Learning (2017, background): “The motivation for using implicit coordination is that communication can be expensive in practical applications, and requires efficient protocols [1, 18, 29].”
Independent reinforcement learners in cooperative Markov games: a survey regarding coordination problems (2012, background): “The matter of the communication between agents is also an important point: even if the use of communication can reduce some undesirable effects of locality in MAS (Mataric, 1998), exchanged information must be relevant to be efﬁcient (Tan, 1993; Balch & Arkin, 1994).”
Learning Communication for Multi-agent Systems (2002, background): “[6] showed that communication significantly improves performance of robot agents on tasks with little environmental communication, and that more complex communication strategies provide little or no benefit over low-level communication.”
Distributed Intelligence: Overview of the Field and Its Application in Multi-Robot Systems (2008, methodology): “An example of this type of task allocation is the work of Balch and Arkin [6].”
Coverage for robotics – A survey of recent results (2001, background): “and Arkin investigated multi-robot coverage tasks in which one to five simulated robots search for and retrieve objects to a homebase [5,27].”
Coordination for Multi-Robot Exploration and Mapping (2000, background): “Balch and Arkin [1] investigated how communication in multi-robot systems affects different tasks, including the graze task where the objective is to completely cover an unknown environment.”
The Packet-World: A testbed for investigating situated multiagent systems (2005, background): “[3] and many others explored the underlying fundamentals of reactivity and situatedness and developed new architectures for situated agents that enable better adaptive behavior and support more flexible design than the early-days hard-wired stimulus-response structures.”
A survey of socially interactive robots (2003, background): “Some of the interaction mechanisms employed are com- munication[6], interference among agents[69], and aggressive competition[163].”
Infant-like Social Interactions between a Robot and a Human Caregiver (2000, background): “…Interactions between a Robot and a Human Caregiver Social robotics has generally concentrated on the behavior of groups of robots performing behaviors such as flocking, foraging or dispersion (Balch & Arkin, 1994; Mataric, 1995) or on paired robot-robot interactions (Billard & Dautenhahn, 1997).”
Multiagent Systems: A Survey from a Machine Learning Perspective (2000, background): “In three multi-robot applications, Balch and Arkin [1994] study the effects of allowing agents to communicate their states and their goals with one another.”
Multi-robot coalition formation (2006, background): “In particular, the multirobot community has historically studied both explicit and implicit coordination techniques (Balch and Arkin, 1994; Parker, 1995).”
Multi-robot exploration controlled by a market economy (2002, background): “Balch and Arkin [1] investigated the role of communication for a set of common multi-robot tasks.”
Coordinated multi-robot exploration (2005, background): “In [4], Balch and Arkin analyze the effects of different kinds of communication on the performance of teams of mobile robots that perform tasks like searching for objects or covering a terrain.”