Distributed problem solving and planning
Multiagent systems
ConGolog, a concurrent programming language based on the situation calculus
Artificial Intelligence
COLBERT: A Language for Reactive Control in Sapphira
KI '97 Proceedings of the 21st Annual German Conference on Artificial Intelligence: Advances in Artificial Intelligence
Planning with Sensing for a Mobile Robot
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
A robotic soccer passing task using petri net plans
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems: demo papers
A framework for facilitating cooperation in multi-agent systems
The Journal of Supercomputing
Towards cooperation of heterogeneous, autonomous robots: A case study of humanoid and wheeled robots
Robotics and Autonomous Systems
LearnPNP: a tool for learning agent behaviors
RoboCup 2010
Autonomous Agents and Multi-Agent Systems
Cognitive concepts in autonomous soccer playing robots
Cognitive Systems Research
Modeling robot behavior with CCL
SIMPAR'12 Proceedings of the Third international conference on Simulation, Modeling, and Programming for Autonomous Robots
An approach to team programming with markup for operator interaction
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
An approach to team programming with markup for operator interaction
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
| Hi-index | 0.00 |
The aim of this paper is to describe a novel representation framework for high level robot and multi-robot programming, called Petri Net Plans (PNP), that allows for representing all the action features that are needed for describing complex plans in dynamic environments. We provide a sound and complete execution algorithm for PNPs based on the semantics of Petri nets. Moreover, we show that multi-robot PNPs allow for a sound and complete distributed execution algorithm, given that a reliable communication channel is provided. PNPs have been used for describing effective plans for actual robotic agents which inhabit dynamic, partially observable and unpredictable environments, and experimented in different application scenarios.