Multi-Agent Intention Recognition and Progression

For an agent in a multi-agent environment, it is often beneficial to be able to predict what other agents will do next when deciding how to act. Previous work in multi-agent intention scheduling assumes a priori knowledge of the current goals of other agents. In this paper, we present a new approach to multi-agent intention scheduling in which an agent uses online goal recognition to identify the goals currently being pursued by other agents while acting in pursuit of its own goals. We show how online goal recognition can be incorporated into an MCTS-based intention scheduler, and evaluate our approach in a range of scenarios. The results demonstrate that our approach can rapidly recognise the goals of other agents even when they are pursuing multiple goals concurrently, and has similar performance to agents which know the goals of other agents a priori.

Authors

• Michael Dann RMIT University
• Yuan Yao Nanjing University
• Natasha Alechina Open University
• Brian Logan University of Aberdeen & Utrecht University
• Felipe Meneguzzi Department of Computer Science, Aberdeen University
• John Thangarajah RMIT University

Keywords

• Agent-based and Multi-agent Systems: MAS: Agent theories and models
• Planning and Scheduling: PS: Activity and plan recognition