The state of hybrid artificial intelligence for interstellar missions

Abstract

Interstellar missions will require a high degree of autonomy mediated through artificial intelligence (AI). All interstellar missions are characterised by 50-100-year transits to extrasolar systems. High system availability demands that interstellar spacecraft are self-repairable imposing significant demands on onboard intelligence. We review the current status of artificial intelligence to assess its capabilities in providing such autonomy. In particular, we focus on hybrid AI methods as these appear to offer the richest capabilities in offsetting weaknesses inherent in paradigmic approaches. Symbolic manipulation systems offer logical and comprehensible rationality with predictable behaviours but are brittle beyond their specific applications (a charge that may be levelled at neural networks unless the transfer learning problem can be resolved). More modern approaches to expert systems include Bayesian networks that incorporate probabilistic treatment to accommodate uncertainty. Artificial neural networks are fundamentally different. They are opaque to analysis but potentially offer greater adaptability in application by virtue of their ability to learn. Indeed, deep machine learning is a variation on neural networks with unsupervised neural front ends and supervised neural back ends. Reinforcement learning offers a promising approach for learning directly from the environment. There are inherent weaknesses in neural approaches regarding their hidden mechanisms rendering their distributed representations opaque to analysis. Hybridising symbolic processing techniques with artificial neural networks appears to offer the advantages of both. Human cognition appears to implement both neural learning and symbolic processing. There are several approaches to such hybridisation that we explore including knowledge-based artificial neural networks, fuzzy neural networks, Bayesian methods such as Markov logic networks and genetic methods such as learning classifier systems. Markov logic networks propose a natural correlation between Bayesian probability and neural weights but mapping representation of symbols into switching neurons is less clear (though vector symbolic architectures present an approach) while learning classifier systems are reinforcement learning methods that are promising for interacting with the physical world. We conclude that current AI may not yet be up to the task of interstellar transits and flybys let alone for physical interaction with unknown planetary environments. Certainly, AI is incapable of interactive encounters with extraterrestrial intelligence.