Cooperative multi-agent planning (MAP) is a relatively recent research field that combines
technologies, algorithms, and techniques developed by the Artificial Intelligence Planning …

Users of AI systems may rely upon them to produce plans for achieving desired objectives.
Such AI systems should be able to compute obfuscated plans whose execution in …

This paper deals with the problem of classical planning for multiple cooperative agents who
have private information about their local state and capabilities they do not want to reveal …

To engage diverse agents in cooperative behavior, it is important, even necessary, to
provide algorithms that do not reveal information that is private or proprietary. A number of …

Privacy Preserving Plans in Partially Observable Environments Page 1 Privacy Preserving Plans
in Partially Observable Environments Using Goal Recognition Design for Improved Privacy …

In many cases several entities, such as commercial companies, need to work together
towards the achievement of joint goals, while hiding certain private information. To …

Multi-agent planning (MAP) deals with planning systems that reason on long-term goals by
multiple collaborative agents which want to maintain privacy on their knowledge. Recently …

This paper proposes a reinforcement learning sliding mode strategy for the time-varying
formation control of quadrotor unmanned aerial vehicles system. Previous research relied …

Real world applications often require cooperation of multiple independent entities. Classical
planning is a well established technique solving various challenging problems such as …

In this paper, we suggest a domain independent approach, MAPJA, to solve multi-agent
planning problems with cooperative goals involving joint actions. We consider the capability …