On Chatbots Exhibiting Goal-Directed Autonomy in Dynamic Environments

TL;DR

This paper discusses how chatbots operating in dynamic environments can advance goal-directed autonomy and handle complex business tasks, exemplified by a water quality advisory system.

Contribution

It introduces the concept of goal-directed autonomy in chatbots within dynamic settings and demonstrates this through a prototype Water Advisor system.

Findings

Chatbots can effectively operate with sensor data in dynamic environments.

Water Advisor demonstrates multi-modal interaction with water quality data.

The approach opens new research avenues for autonomous conversational agents.

Abstract

Conversation interfaces (CIs), or chatbots, are a popular form of intelligent agents that engage humans in task-oriented or informal conversation. In this position paper and demonstration, we argue that chatbots working in dynamic environments, like with sensor data, can not only serve as a promising platform to research issues at the intersection of learning, reasoning, representation and execution for goal-directed autonomy; but also handle non-trivial business applications. We explore the underlying issues in the context of Water Advisor, a preliminary multi-modal conversation system that can access and explain water quality data.