R-HTN: Rebellious Online HTN Planning for Safety and Game AI

TL;DR

This paper presents R-HTN, an online hierarchical planning algorithm enabling agents to rebel against user directives when necessary for safety or personality, by either stopping or modifying plans.

Contribution

It introduces R-HTN, a novel online HTN planning method that incorporates directives allowing agents to disobey or adapt plans for safety and personality considerations.

Findings

R-HTN agents never violate directives.

Agents aim to achieve goals if feasible, despite disobedience.

The approach is effective in safety-critical and personality-driven tasks.

Abstract

We introduce online Hierarchical Task Network (HTN) agents whose behaviors are governed by a set of built-in directives \D. Like other agents that are capable of rebellion (i.e., {\it intelligent disobedience}), our agents will, under some conditions, not perform a user-assigned task and instead act in ways that do not meet a user's expectations. Our work combines three concepts: HTN planning, online planning, and the directives \D, which must be considered when performing user-assigned tasks. We investigate two agent variants: (1) a Nonadaptive agent that stops execution if it finds itself in violation of \D~ and (2) an Adaptive agent that, in the same situation, instead modifies its HTN plan to search for alternative ways to achieve its given task. We present R-HTN (for: Rebellious-HTN), a general algorithm for online HTN planning under directives \D. We evaluate R-HTN in two task domains where the agent must not violate some directives for safety reasons or as dictated by their personality traits. We found that R-HTN agents never violate directives, and aim to achieve the user-given goals if feasible though not necessarily as the user expected.