Understanding how goals organize purposeful human and machine behavior
The syntax and semantics of goals
Artificial Intelligence
Summary
Goals help people and computers act purposefully by combining what they know about the world with what they want to achieve. This paper looks at how the structure and meaning of goals relate to how well they can describe different kinds of actions. The authors compare goals to language and logic to better understand how goal representations work and how they can be designed. They also point out that different ways of representing goals can affect how easily and effectively goals can be used in reasoning and decision-making.
What this means in practice
- •For robotics developers: Design goal representations that better capture desired robot behaviors and allow flexible planning.
- •For ai system architects: Improve AI systems’ decision-making by distinguishing form and meaning of goals to optimize expressivity and efficiency.
A position paper. It proposes an approach and reports no results.
Authors
David M. Abel, Mark K. Ho
Abstract
In both cognitive science and computer science, goals are conceptualized as cognitive states that flexibly combine with world knowledge to organize and specify purposeful behavior. In this way, goals are compositional representations whose content relates to rational behavior. We here draw attention to goals as representations and their content because it highlights a parallel with other areas in cognitive science - in particular, the syntax-semantics interface in linguistics and logic - while also foregrounding foundational questions about the expressivity, design, and efficiency of different goal representations. For example, goals are typically taken as fixed and imposing constraints on desirable behaviors, but we can also identify constraints on goal representations themselves, such as whether a particular goal language is sufficiently expressive to capture behaviors of interest, or whether different goal representations capture the same behavior. Here, we synthesize work that aims to characterize the properties of different goal representations and suggest these are points of a broader design space. We close by discussing how distinguishing the form and meaning of goals can elucidate the implicit assumptions we make about goals, inform the study of interactions between higher-level cognition and motivation, and isolate axes of variation for different conceptions of goals.