Intention Progression with Temporally Extended Goals

The Belief-Desire-Intention (BDI) approach to agent development has formed the basis for much of the research on architectures for autonomous agents. A key advantage of the BDI approach is that agents may pursue multiple intentions in parallel. However, previous approaches to managing possible interactions between concurrently executing intentions are limited to interactions between simple achievement goals (and in some cases maintenance goals). In this paper, we present a new approach to intention progression for agents with temporally extended goals which allow mixing reachability and invariant properties, e.g., “travel to location A while not exceeding a gradient of 5%”. Temporally extended goals may be specified at run-time (top-level goals), and as subgoals in plans. In addition, our approach allows human-authored plans and plans implemented as reinforcement learning policies to be freely mixed in an agent program, allowing the development of agents with ‘neurosymbolic’ architectures.