Symbol Emergence and The Solutions to Any Task
Michael Timothy Bennett

TL;DR
This paper proposes that an agent capable of constructing Intensional Solutions can achieve artificial general intelligence and naturally acquire language by modeling others' intents through shared symbol systems.
Contribution
It introduces the concept of Intensional Solutions as a pathway to artificial general intelligence and explains how natural language can emerge from such agents.
Findings
Agents with Intensional Solutions can model other agents' intents.
Natural language may emerge from shared symbol systems.
Intensional Solutions are key to achieving AGI.
Abstract
The following defines intent, an arbitrary task and its solutions, and then argues that an agent which always constructs what is called an Intensional Solution would qualify as artificial general intelligence. We then explain how natural language may emerge and be acquired by such an agent, conferring the ability to model the intent of other individuals labouring under similar compulsions, because an abstract symbol system and the solution to a task are one and the same.
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