Autonomous systems must consider the moral ramifications of their actions. Moral norms vary among people and depend on common sense, posing a challenge for encoding them explicitly in a system. I propose to develop a model of repeated analogical chaining and analogical reasoning to enable autonomous agents to interactively learn to apply common sense and model an individual’s moral norms.
We present a computational model, MoralDM, which integrates several AI techniques in order to model recent psychological findings on moral decision-making. Current theories of moral decision-making extend beyond pure utilitarian models by relying on contextual factors that vary with culture. MoralDM uses a natural language system to produce formal representations from psychological stimuli, to reduce tailorability. The impacts of secular versus sacred values are modeled via qualitative reasoning, using an order of magnitude representation. MoralDM uses a combination of first-principles reasoning and analogical reasoning to determine consequences and utilities when making moral judgments. We describe how MoralDM works and show that it can model psychological results and improve its performance via accumulating examples.
Understanding commonsense reasoning is one of the core challenges of AI. We are exploring an approach inspired by cognitive science, called analogical chaining, to create cognitive systems that can perform commonsense reasoning. Just as rules are chained in deductive systems, multiple analogies build upon each other’s inferences in analogical chaining. The cases used in analogical chaining – called common sense units – are small, to provide inferential focus and broader transfer. Importantly, such common sense units can be learned via natural language instruction, thereby increasing the ease of extending such systems. This paper describes analogical chaining, natural language instruction via microstories, and some subtleties that arise in controlling reasoning. The utility of this technique is demonstrated by performance of an implemented system on problems from the Choice of Plausible Alternatives test of commonsense causal reasoning.
Concept learning is a central problem for cognitive systems. Generalization techniques can help organize examples by their commonalities, but comparisons with non-examples, near-misses, can provide discrimination. Early work on near-misses required hand-selected examples by a teacher who understood the learner’s internal representations. This paper introduces Analogical Learning by Integrating Generalization and Near-misses (ALIGN) and describes three key advances. First, domain-general cognitive models of analogical processes are used to handle a wider range of examples. Second, ALIGN’s analogical generalization process constructs multiple probabilistic representations per concept via clustering, and hence can learn disjunctive concepts. Finally, ALIGN uses unsupervised analogical retrieval to find its own near-miss examples. We show that ALIGN out-performs analogical generalization on two perceptual data sets: (1) hand-drawn sketches; and (2) geospatial concepts from strategy-game maps.