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Patrick Riley, Peter Stone, and Manuela Veloso. Layered Disclosure: Revealing Agents' Internals. In C. Castelfranchi
and Y. Lespérance, editors, Intelligent Agents VII. Agent Theories, Architectures, and Languages
--- 7th. International Workshop, ATAL-2000, Boston, MA, USA, July 7--9, 2000, Proceedings,
number 1986 in Lecture Notes in Artificial Intelligence, pp. 61–72, Springer, Berlin, 2001.
Publisher's
Webpage© Springer-Verlag
[PDF]280.1kB [gzipped postscript]177.4kB
A perennial challenge in creating and using complex autonomous agents is following their choices of actions as the world changes dynamically and understanding why they act as they do. This paper reports on our work to support human developers and observers to better follow and understand the actions of autonomous agents. We introduce the concept of layered disclosure by which autonomous agents have included in their architecture the foundations necessary to allow them to disclose upon request the specific reasons for their actions. Layered disclosure hence goes beyond standard plain code debugging tools. In its essence it also gives the agent designer the ability to define an appropriate information hierarchy, which can include agent-specific constructs such as internal state that persists over time. The user may request this information at any of the specified levels of detail, and either retroactively or while the agent is acting. We present layered disclosure as we created and implemented it in the simulated robotic soccer domain. We contribute the detailed and principled design to support the application of layered disclosure to other agent domains. Layered disclosure played an important role in our successful development of the RoboCup undefeated champion \mboxCMUnited-99 multiagent team.
@incollection(ATAL-LD,
author = {Patrick Riley and Peter Stone and Manuela Veloso},
title = {Layered Disclosure: Revealing Agents' Internals},
booktitle = {Intelligent Agents VII. Agent Theories,
Architectures, and Languages --- 7th.~International
Workshop, ATAL-2000, Boston, MA, USA, July 7--9,
2000, Proceedings},
editor = "C.~Castelfranchi and Y.~Lesp\'{e}rance",
publisher = "Springer",
address = {Berlin},
series = "Lecture Notes in Artificial Intelligence",
number = {1986},
pages = {61--72},
year = 2001,
wwwnote = {<a href="http://www.springer.de/comp/lncs/index.html">Publisher's Webpage</a>© Springer-Verlag},
abstract = {A perennial challenge in creating and using complex
autonomous agents is following their choices of
actions as the world changes dynamically and
understanding why they act as they do. This paper
reports on our work to support human developers and
observers to better follow and understand the
actions of autonomous agents. We introduce the
concept of {\it layered disclosure} by which
autonomous agents have included in their
architecture the foundations necessary to allow them
to disclose upon request the specific reasons for
their actions. Layered disclosure hence goes beyond
standard plain code debugging tools. In its essence
it also gives the agent designer the ability to
define an appropriate information hierarchy, which
can include agent-specific constructs such as
internal state that persists over time. The user may
request this information at any of the specified
levels of detail, and either retroactively or while
the agent is acting. We present layered disclosure
as we created and implemented it in the simulated
robotic soccer domain. We contribute the detailed
and principled design to support the application of
layered disclosure to other agent domains. Layered
disclosure played an important role in our
successful development of the RoboCup undefeated
champion \mbox{CMUnited-99} multiagent team. },
bib2html_pubtype = {Refereed Conference},
bib2html_rescat = {Other},
bib2html_funding = {CoABS,ActiveTemplates},
)
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