This study investigates a new line of research into the "taskability" of cognitive agents. Taskability is the ability of an agent to accept high level, task-oriented instructions from a human and translate those goal-oriented directives into a suitably decomposed plan of sensing, reasoning, or action. Little to no research has been conducted on this subject to date. This research defines taskability in a manner sufficiently formal to allow for scientific research, establishes some of the initial conditions needed to evaluate taskable agents, and advances theories and prototype agents that meet those requirements. Specifically, the four research activities being undertaken in this study include: 1. A review, analysis, and synthesis of prior work from multiple disciplines that that can lay groundwork for focused cognitive systems research in this area; 2) An analysis of the different types of tasks and their structure, as well as the associated types of knowledge that must be learned by taskable agents; 3) Research on how people use instruction with taskable agents for such activities, performing user studies to determine the required task knowledge and agent capabilities; and 4) An extension of current cognitive agent capabilities to support the behaviors observed in the aforementioned studies, resulting in a software system that can be evaluated and co-developed with theory.
The current state of the art in research for cognitive systems and agents in general allows human handlers to issue directions to agents either in terms of lower level action primitives that correspond to the agent's particular design, or constrained by a higher-order action language (or even visual repertoire) that is engineered to suffice as a shorthand for some sequence of the same kinds of agent-specific behaviors. Taskability requires that an agent be able to interpret a richer, less constrained set of instructions from a user and, despite a lack of precompiled task decomposition instructions, dynamically formulate an accurate representation of the task to be performed, and even learn new tasks via this sort of interaction. Taskable agents would fundamentally change the way humans interact with intelligent agents and robotic systems in a broad range of disciplines and environments, leading to richer interactions with more capable cars, phones, and almost any device containing a computer, all of which might respond to human interaction without resort to preprogrammed interaction modes. This in turn promises transformational advances in a range of application domains such as health care, industry, government, and home automation.
