Like the idea of identifying and constructing tasks for assistants so they can be more readily be challenged and compared. This article suggests this be done and gives some examples.
A Taxonomy of Automated Assistants
By Jerrold M. Grochow
Communications of the ACM, April 2020, Vol. 63 No. 4, Pages 39-41 10.1145/3382746
Automated cars are in our future—and starting to be in our present. In 2014, the Society of Automotive Engineers (SAE) published the first version of a taxonomy for degree of automation in vehicles from Level 0 (not automated) to Level 5 (fully automated, no human intervention necessary).8 Since then, this taxonomy has gained wide acceptance—to the point where everyone from the U.S. government (used by the NHTSA5) to auto manufacturers to the popular press are talking in terms of "skipping level 3" or "everyone wants a level 5 car."1 As technology gets developed and improved, having an accepted taxonomy helps ensure people can talk to each other and know they are talking about the same thing. It is time for one of our computing organizations (perhaps ACM?) to develop an analogous taxonomy for automated assistants. With Siri, Alexa, Cortana, and cohorts selling in the "tens of millions"2 and with more than 20 competitors on the market,7 having an easily understandable taxonomy will help practitioners and end users alike.
There is already a significant body of literature aimed at improving the design and use of automated assistants in both industry and academic arenas (with a variety of category names for these devices and systems, using some combination of "automated," "digital," "smart," "intelligent," "personal," "agent," and "assistant"), as the bibliographies of cited works show. Some recent work focused on task content, use cases, and features. The task content of human activity has been widely studied over a long period of time, but Trippas et al.9 note that "how intelligent assistants are used in a workplace setting is less studied and not very well understood." While not presenting a taxonomy of assistants, this type of task content analysis could be used as an aid in intelligent assistant design. Similarly, Mehrotra et al.4 studied interaction with a desktop-based digital assistant with an eye to "help guide development of future user support systems and improve evaluations of current assistants." Knote et al.3 evaluated 115 "smart personal assistants" by literature and website review to create a taxonomy based on cluster analysis of design characteristics such as communications mode, direction of interaction, adaptivity, and embodiment (virtual character, voice), and so forth—a technology and features-based taxonomy. A commercial study of 22 popular "intelligent ... or automated personal assistants"7 reported "Intelligent Agents can be classified based on their degree of perceived intelligence and capability such as simple reflex agents, model-based reflex agents, goal-based agents, utility-based agents and learning agents." While this is an arguably useful taxonomy, it also primarily addresses the technology used and not the actual use of the automated assistant. The website additionally presents editor and user ratings of ease of use, features, and performance that may be of value to end users. .... '
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