Evidence based Semantics

Toward Deep Explanation of Observations

Abstract

We present Evidence-based Semantics as a simple and fundamental but novel problem in AI that has many application areas: to explain the meaning of observed events via collecting useful evidence. Evidence here is a piece of new information from the open real world, that is useful for explaining the meaning of the observed event, and the knowledge for entailing the observed events. This information may not be of the generality that is the goal of learning, but may be a piece of knowledge or an assertion in a one-time message. In this presentation, we propose two approaches to EBS and a couple of applied toy examples of these approaches for actions and utterances in real life.