An Optimal Itinerary Generation in a Configuration Space of Large Intellectual Agent Groups with Linear Logic
This work addresses coordination and optimization in multi-agent systems, but appears incremental as it builds on existing linear logic frameworks.
The paper tackles the problem of generating optimal itineraries for groups of intelligent agents performing parallel tasks by modeling them in a linear logic game category, achieving a solution defined as a play with maximal total reward based on certainty of agent goals.
A group of intelligent agents which fulfill a set of tasks in parallel is represented first by the tensor multiplication of corresponding processes in a linear logic game category. An optimal itinerary in the configuration space of the group states is defined as a play with maximal total reward in the category. New moments also are: the reward is represented as a degree of certainty (visibility) of an agent goal, and the system goals are chosen by the greatest value corresponding to these processes in the system goal lattice.