Synergising Human-like Responses and Machine Intelligence for Planning in Disaster Response

TL;DR

This paper introduces an attention-based cognitive architecture inspired by Dual Process Theory that combines human-like rapid responses with machine intelligence for improved planning in disaster response scenarios.

Contribution

It presents a novel framework that dynamically integrates heuristic and optimized planning systems for autonomous agents in complex, changing environments.

Findings

Effective management of complex tasks in dynamic environments

Improved mission objective optimization through system synergy

Real-time adaptive engagement of response systems

Abstract

In the rapidly changing environments of disaster response, planning and decision-making for autonomous agents involve complex and interdependent choices. Although recent advancements have improved traditional artificial intelligence (AI) approaches, they often struggle in such settings, particularly when applied to agents operating outside their well-defined training parameters. To address these challenges, we propose an attention-based cognitive architecture inspired by Dual Process Theory (DPT). This framework integrates, in an online fashion, rapid yet heuristic (human-like) responses (System 1) with the slow but optimized planning capabilities of machine intelligence (System 2). We illustrate how a supervisory controller can dynamically determine in real-time the engagement of either system to optimize mission objectives by assessing their performance across a number of distinct attributes. Evaluated for trajectory planning in dynamic environments, our framework demonstrates that this synergistic integration effectively manages complex tasks by optimizing multiple mission objectives.