Task sequence planning by intelligent agent with context awareness

The problem of context-aware task sequence planning for independent or weakly related tasks by an intelligent agent has been considered. The principle of matching the task to the context is analyzed. The structure of the context-aware task sequence planning module as part of an intelligent agent and the corresponding algorithm are proposed. In the structure of the planning module, three main groups of blocks are implemented: operations with tasks, operations with the context, determining the relevance of tasks to the context.

The article also proposes an algorithm for calculating the dynamic priority of a task, an algorithm for determining the relevance of a task to the context, and an algorithm for adapting a set of rules for matching the task to the context. The value of the dynamic priority depends on the static priority of the task, which is assigned when a new task is added, and the degree of correspondence of the task to the context, taking into account the context vector. Two modes of starting the planning algorithm are allowed: when the intelligent agent requests a new task and when the context vector changes. The dynamic priority calculation algorithm is performed independently for each task. As a result, its software implementation has a large parallelization resource.

To adapt the set of rules for matching tasks to the context, a scheme of reinforcement learning in a contextual multi-armed bandit was used. For each matching rule, a separate instance of the reinforcement learning procedure is performed. The reinforcement learning method used in the article is Upper-Confidence-Bound Action Selection. The functional scheme of the reinforcement learning procedure, implemented in the prototype of the context-aware task sequence planning module has been proposed. The functional scheme of the reinforcement learning procedure allows the use of data decomposition and functional decomposition to parallelize the corresponding calculations.