Decision Aids for Adversarial Planning in Military Operations: Algorithms, Tools, and Turing-test-like Experimental Validation
Alexander Kott, Ray Budd, Larry Ground, Lakshmi Rebbapragada, John, Langston

TL;DR
This paper presents integrated algorithms and a tool that generate detailed, human-like military operation plans from high-level concepts, validated through Turing-test-like experiments showing performance comparable to human planners.
Contribution
Introduction of a novel integrated decision aid tool combining multiple algorithms for detailed military planning, validated through experimental comparison with human planners.
Findings
The tool produces plans with human-like quality.
Performance compares favorably with human planners.
The approach is applicable to various critical planning domains.
Abstract
Use of intelligent decision aids can help alleviate the challenges of planning complex operations. We describe integrated algorithms, and a tool capable of translating a high-level concept for a tactical military operation into a fully detailed, actionable plan, producing automatically (or with human guidance) plans with realistic degree of detail and of human-like quality. Tight interleaving of several algorithms -- planning, adversary estimates, scheduling, routing, attrition and consumption estimates -- comprise the computational approach of this tool. Although originally developed for Army large-unit operations, the technology is generic and also applies to a number of other domains, particularly in critical situations requiring detailed planning within a constrained period of time. In this paper, we focus particularly on the engineering tradeoffs in the design of the tool. In an…
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Taxonomy
TopicsAI-based Problem Solving and Planning · Logic, Reasoning, and Knowledge · Robotic Path Planning Algorithms
