Machine Common Sense Concept Paper

Abstract

This paper summarizes some of the technical background, research ideas, and possible development strategies for achieving machine common sense. Machine common sense has long been a critical-but-missing component of Artificial Intelligence (AI). Recent advances in machine learning have resulted in new AI capabilities, but in all of these applications, machine reasoning is narrow and highly specialized. Developers must carefully train or program systems for every situation. General commonsense reasoning remains elusive. The absence of common sense prevents intelligent systems from understanding their world, behaving reasonably in unforeseen situations, communicating naturally with people, and learning from new experiences. Its absence is perhaps the most significant barrier between the narrowly focused AI applications we have today and the more general, human-like AI systems we would like to build in the future. Machine common sense remains a broad, potentially unbounded problem in AI. There are a wide range of strategies that could be employed to make progress on this difficult challenge. This paper discusses two diverse strategies for focusing development on two different machine commonsense services: (1) a service that learns from experience, like a child, to construct computational models that mimic the core domains of child cognition for objects (intuitive physics), agents (intentional actors), and places (spatial navigation); and (2) service that learns from reading the Web, like a research librarian, to construct a commonsense knowledge repository capable of answering natural language and image-based questions about commonsense phenomena.

References in corpus (3)

• Dynamic Integration of Background Knowledge in Neural NLU Systems
• Probing Physics Knowledge Using Tools from Developmental Psychology
• Modeling Semantic Plausibility by Injecting World Knowledge

Cited by in corpus (10)

• Theory of Mind abilities of Large Language Models in Human-Robot Interaction : An Illusion?
• Commonsense Knowledge in Wikidata
• Consolidating Commonsense Knowledge
• Generalizable Neuro-symbolic Systems for Commonsense Question Answering
• Learning Contextualized Knowledge Structures for Commonsense Reasoning
• Machine Common Sense
• Divergences between Language Models and Human Brains
• Abductive Reasoning as Self-Supervision for Common Sense Question Answering
• SalKG: Learning From Knowledge Graph Explanations for Commonsense Reasoning
• ANA at SemEval-2020 Task 4: mUlti-task learNIng for cOmmonsense reasoNing (UNION)