Advancing AI Capabilities for Dynamic Physical Environments: Transitioning from Closed-World Problem Solving to Open-World Challenges

Abstract

Contemporary artificial intelligence (AI) systems have excelled in well-defined tasks within controlled environments, often surpassing human performance, especially in complex games like Go, StarCraft, and Dota. However, transitioning to dynamic and unpredictable real-world scenarios presents a formidable challenge. AI systems must respond to unforeseen events, adapt to environmental changes, and navigate unfamiliar terrains. To facilitate their deployment across diverse applications, AI must move beyond closed-world problem-solving and confront open-world challenges. Addressing this pivotal issue requires a multifaceted approach. Firstly, the development of robust evaluation methodologies is important to accurately assess an agent's performance in a pre-novelty environment. This baseline assessment distinguishes whether an agent's success or failure in a novel task is attributable to its intrinsic capabilities or merely a matter of chance. In Chapter 3, we discuss our solution to this challenge: Phy-Q, a metric for measuring physical reasoning intelligence. Secondly, it is crucial to create environments that enable the introduction of novel elements, thus facilitating comprehensive evaluations of agents facing unforeseen challenges. To tackle this issue, in Chapter 4, we introduce ScienceBird Novelty, a framework that empowers users to inject a wider range of novelties and evaluate agents specializing in novelty adaptation. Furthermore, designing a benchmark tailored specifically to evaluate an agent's performance in handling novelty is a non-trivial endeavour. Such a benchmark is essential for isolating the reasons behind an agent's success or failure in various novel situations. To this end, in Chapter 5, we present NovPhy, a benchmark that assesses the performance characteristics of novelty adaptation agents. Drawing upon a comprehensive toolkit of methodologies and approaches, we introduce our novel adaptation framework, NAPPING (Novelty Adaptation Principles Learning) in Chapter 6. Through a series of empirical demonstrations, we illustrate how NAPPING empowers deep reinforcement learning agents to rapidly adapt to a wide spectrum of novel situations, devoid of any prior knowledge regarding the specific novelty at hand. This breakthrough in AI adaptation promises to significantly enhance the flexibility and utility of intelligent systems across diverse real-world applications, paving the way for more effective collaboration between AI and human counterparts in the face of ever-changing conditions. Show more