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Agent deception via polynomial path planning
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AI
DOI：10.1016/j.engappai.2025.111205.png)
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Abstract
En 中文
Deceptive behavior involves an intelligent agent creating plans that conceal its true intentions while appearing to pursue different goals. It is crucial for inducing confusion in various applications, including security, military, and competitive environments, where the ability to conceal true intentions can lead to significant strategic advantages. Developing better artificial intelligence (AI) for adversarial environments or strategic decision-making scenarios (e.g., more realistic testing of human or AI decision-making capabilities in games and simulations) requires effective deceptive planning. In this paper, we propose a novel polynomial path planner that enables an agent to deceive its observers. Our contributions include the following: (i) we develop a framework for obtaining ambiguous functions; (ii) we introduce new deception metrics; (iii) we present a method for standardizing trajectories to enable shape-based comparisons independent of speed; (iv) we conduct a human survey to evaluate deception and its relation to goal recognition; and (v) we outperform the state of the art on a multitude of deception metrics. Furthermore, our findings show that using more complex functions and increasing the level of misdirection greatly enhances agent deception effectiveness.
Keywords:
Deception
Motion and path planning
Agent-based systems
Journal
IF:
8
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5.3K
Citations:
3.5W
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