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Artificial Intelligence
Self-adjusting autonomous systems | Permalink The long-standing promise of artificial intelligence (AI) is a bright new world in which smart machines positively transform the ways we live and work. However, a well-known difficulty for AI is the problem of brittleness.1 Autonomous systems tend to ‘break’ when confronted with even slight deviations from the situations anticipated by their designers. Of course, this is not surprising. Why would something ‘work’ in a given situation if it was not built to do the right thing? We would not expect Deep Blue (a supercomputer) to be able to play even mediocre checkers, because it was built specifically for chess. But AI strives for systems that work in a variety of situations, including ones not anticipated. The solution that we outline here is to add an additional, metacognitive layer of intelligence that watches a system and adjusts its behaviour when the system fails. Most approaches attempt to avoid failure altogether by programming specific reactions for various classes of situations, but this requires classes that cover all possible failure states. Unfortunately, the approach has proven intractable. Instead, we note that an intelligent agent learns from a failure. A fool is doomed to repeat it.2 Metacognition (cognition about cognition) has also been tried before, but much of this research focuses on improving existing performance by adding a metalayer (details published elsewhere3, 4). Our research is different in that we define a relatively small set of failures or anomalies and provide a generalizable mapping at the metalevel to a small set of recovery response strategies. Figure 1. The metacognitive loop (MCL). 
A lightweight, three-phase architecture exists for deploying response strategies (see Figure 1). We call this architecture the metacognitive loop or MCL.5, 6 MCL acts as an executive monitor and controller when connected to an intelligent host. First, it notes when host behaviour or sensor readings diverge from expectations. Second, it assesses any such anomaly and the options the host has for dealing with the difficulty. Finally, it guides the host towards putting these options into action. To implement this architecture, we have developed three ontologies (knowledge representation hierarchies) that support the classification and reasoning abilities in each of the MCL phases. The core of these ontologies contains abstract and domain-neutral concepts. When an actual anomaly is detected, MCL attempts to map it onto the core of the loop so that it may reason about it abstractly. Nodes in the ontologies are linked, expressing relationships between the concepts they represent. There are linkages both within and between the ontologies, which together allow MCL to perform abstraction and reasoning about the anomaly being considered. Each of the three phases of MCL employs one of the ontologies. The note phase uses an ontology of indications, where an indication is a sensory or contextual signifier that the system's expectations have been violated. Processing in the indications ontology allows the assess phase to map nodes in the indications ontology to nodes in the failure ontology, which contains nodes that abstractly describe how a system might fail. Nodes in the failure ontology represent the underlying cause of expectation violations. Finally, when hypotheses about the failure have been generated, the guide phase maps that information to its own response ontology, which describes means for handling failures at various levels of abstraction. Reasoning from indications to responses is done by treating the ontologies as a Bayesian network in which all random variables are Boolean.7 The random variables in the indications ontology are true if the corresponding indication has been observed and are false otherwise. Variables in the failure network are true if the corresponding failure has actually occurred and are false otherwise. This is not directly observable, but standard inference methods make it possible to compute a probability distribution over these variables based on the observable evidence (the indications). Finally, random variables in the response ontology are true if the response will likely repair the underlying failure and are false otherwise. Each response has an associated cost, and again standard inference methods are used to find the response with the highest expected utility. Using the lightweight methodology described here and a more knowledge-intensive version described elsewhere,8 MCL has been shown effective in numerous domains and various cognitive tasks.5, 9,10 As discussed, our approach to the brittleness problem is to add a metacognitive layer that facilitates a host system's ability to adjust itself in the face of failure. A significant feature that we have started to explore is the use of MCL to decide what, when and how to learn a new skill. Thus, a particular anomaly might lead to the response (conclusion) that training is needed to be better equipped for the given situation. The guide phase then amounts to initiating a training program and monitoring its progress. Finally, we are also exploring a new metacognitive integrated dual-cycle architecture that applies the MCL principles at both the metacognitive and cognitive levels.11 The hope is that such research will enable a more robust kind of intelligence that can withstand a volatile environment. References
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