Interaction in heterogeneous mission operations teams is not well matched to classical models of coordination with autonomous systems. We describe methods of loose coordination and information management in mission operations. We describe an information agent and information management tool suite for managing information from many sources, including autonomous agents. We present an integrated model of levels of complexity of agent and human behavior, which shows types of information processing and points of potential error in agent activities. We discuss the types of information needed for diagnosing problems and planning interactions with an autonomous system. We discuss types of coordination for which designs are needed for autonomous system functions

Johnson, Kathy A.

Malin, Jane T.

NASA Technical Reports Server

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NASA Johnson Space Center 
Information for Successful Interaction with Autonomous Systems 
Jane T. Malin and Kathy A. Johnson 
NASA Johnson Space Center 
2]0] NASA Road] 
Houston, TX 77058-3696 
jane.t.malin@nasa.gov 
Abstract 
Interaction in heterogeneous mission operations teams is not 
well matched to classical model s of coordination with 
autonomous systems. We describe methods of loose 
coordination and information management in mission 
operations. We describe an information agent and 
information management tool suite for managing 
information from many sources, including autonomous 
agents. We present an integrated model of levels of 
complexity of agent and human behavior, which shows 
types of information processing and points of potential error 
in agent activities. We discuss the types of information 
needed for diagnosing problems and planning interactions 
with an autonomous system. We discuss types of 
coordination for which designs are needed for autonomous 
system functions. 
We are developing design concepts for collaborative 
intelligent systems to support future mission operations 
teams. Teams of distributed computer and human agents 
will collaborate, in multi-agent mixed-initiative operations. 
Software tools, including software agents, are needed to 
support work processes and interaction in teams that 
include autonomous agents . In mission operations teams, 
the level of involvement of intelligent agents and human 
team members will vary significantly. Software agents will 
be continuously engaged in focused autonomous tasks, 
while human agents wi ll periodically manage multiple 
tasks, including occasional interaction with autonomous 
systems. Human activities include managing information, 
understanding past, ongoing and planned operations and 
situations, solving problems, and negotiating and 
participating in carrying out solutions. 
There are several ways in which interaction in such teams 
is not well suited for classical models of coordination with 
autonomous systems (Nodine, Chandrasekara, and Unruh 
2001). Human shift work and on-call work makes role-
agent relationships unstable. Agents can vary in 
complexity and sophistication of reasoning and 
representation. There are potential delays in information 
and communication. Tasks may be interrupted and 
discontinuous, and may not be tightly coordinated. 
" Plans " with multiple tasks and fluctuating resources are 
likely to be ill-structured and flexible . We will consider 
U.S. Government work not protected by U.S. copyright. 
kathy.a. johnson@nasa.gov 
design of software agents for loose coordination and 
information management. 
Loose Coordination 
To shift between autonomy and interdependence, 
autonomous agents need to provide information to support 
loose coordination, and to orient team members as they 
pass in and out of team activities . Orientation during 
handover is not merely a matter of information access or 
retrieval, since context can be stale or missing (Reddy, 
Dourish and Pratt 200 1; Chalmers 2002). Work is required 
to organize, understand and analyze histories of 
information and actions from multiple sources (Danzitch et 
al. 2002). There is significant risk of misunderstanding, 
being distracted and forgetting information, which can lead 
to inappropriate actions and surprises (Malin et al. 1991; 
Christoffersen and Woods 2002; McFarlane and Latorella 
2002). 
Several means of loose coordination among human teams 
are used in control room domains. Loose coordination is 
supported by voice loops (Patterson, Watts-Perotti, and 
Woods 1999), by review of console logs or diaries (Jones 
1995; Robinson, Kovalainen, and Auramaki 2000), and by 
handover meetings, briefmgs and reports between shifts 
(Patterson and Woods 2001). Anomaly response teams are 
formed to handle problems that come up during missions 
(Watts, Woods, and Patterson 1996). During quiet phases 
of operation of the International Space Station in the early 
unmarmed phase, a single Station Duty Officer covered the 
longest shift. System experts developed anomaly response 
instructions for these graveyard shift operators. These 
instructions specified how to analyze, log, report on events 
and to notify other team members when data indicated that 
a problem might be occurring. Loose coordination 
promotes team awareness of important achievements and 
fai lures, significant changes, and problems and issues that 
may influence upcoming activities. Information is 
provided not only on events but also on the stance of team 
members : assessments, priorities and plans. Systems 
designed to promote awareness provide the following 
benefits: better management of team movement between 
loose and tight coordination, reduced and simplified 
communication, better predictive coordination of actions 
https://ntrs.nasa.gov/search.jsp?R=20100033293 2019-08-30T11:41:25+00:00Z
and more appropriate help and assistance (Gutwin and 
Greenburg 1999). This awareness can prepare team 
members for three types of coordination: assuming some 
or all duties of another agent (handover), participating in 
coordinated activity with another agent, or controlling 
another agent (assessing, directing, correcting). 
Information Management 
As the complexity of tasks, controlled processes and agents 
increases, the complexity of information management tasks 
increases. We have analyzed information management by 
International Space Station control center personnel during 
shifts and handovers. Figure I shows types of information 
sources and information handling. Chronological console 
logs are used to keep a stab le record of se lected material 
from streams of rapidly changing distributed information. 
The information is further refined and combined with 
diverse interim and semi-permanent information sources, 
to produce summary reports, action lists and formal 
request packages. These products are used to orient other 
team members as they prepare to come on shift and take 
over responsibilities. Other participants also use and 
review these products from various remote locations as 
Transitory Data Sources 
they work cooperatively on issues and plans, and they may 
respond as needed in person, or by phone or emaiL 
Information management in a control center is cha llenging. 
It is difficult keep up with the volatile information sources, 
while preparing for the next relevant operation and 
participating in team work on plans and issues. Selecting 
and recording information from rapid ly changing sources 
can be tedious and repetitious. Voluminous information 
from multiple sources makes it difficult to successfully 
fmd items and move them among tools and team members. 
It is an important and challenging task to combine and 
organize information from multiple sources and time scales 
to support coordination: to orient the next shift worker, or 
a new team member who will participate in work on a 
problem. 
We are prototyping information agents and a suite of tools 
that can help address these information management 
challenges. Intelligent Briefmg and Response Assistants 
(IBRAs) are triggered by data from autonomous agents 
that manage remote life support systems and handle 
anomalies. IBRAs select and organize information and 
enter it into information management tools. IBRAs make 
console log entries and assemble reports and briefmgs in 
issue workspaces . These agents can pass on information 
"Permanent" 
"Working" Sources 
Real time data VoiceNideo/TV Sources Flight Rules 
Thoughts 
+ ~ Plans Instructions 
Convert to: Requests 
Notices, Data Analyses, Procedures 
Configuration Status Responses 
Requirements 
r ~ + + ~I Analyses 
Select, Record and Annotate in: Console Records 
Log, Notes, Screen Shots and Recordings Reports Documentation 
Drafts 
1 1 1 1 
Extract for Response into : Find, Copy, Link, Attach, Organize, Format into: Reports 
To Do and Actions and Formal Requests 
.. ~I .. 
Make locally or globally accessible for: 
Review, Revision, Response, Monitoring 
Challenges: Find and keep track of information from multiple sources and time scales. 
Copy and transmit among tools without losing critical information. 
Allocate attention and keep up with parallel heterogeneous streams of information. 
Conform to processes. Manage accessibility. 
Figure 1. Information Management in a Control Center. 
from intelligent system management agents (ISMAs) 
concerning goals, procedures, control regimes, 
configurations, states and status. The prototype tool suite 
has the following features : 
• Team members and agents use the same tools, to 
facilitate incremental agent development and learning 
approaches that reuse techniques of users . 
• Web-based and database-based tools facilitate global 
and tool-independent access and search . 
• Explicit content links between tools enable finding 
things and keeping track of their locations, minimizing 
extra data entry effort or error. 
The too ls in the Team Work Center include: 
• Agent Instructions and Procedures - specify Briefing 
and Response Instructions (BRIs) and team processes 
and procedures. 
• Electronic Console Logger - create a database of log 
entries, to support review of large log files, automated 
logging, and generation of reports and custom logs. 
• Workspace Manager - collect and share items related to 
an issue, anomaly or work topic in one accessible 
workspace, with capability to handle files , links, 
actions, logs, and paperwork. 
• Report Maker - create report formats that collect 
information from multiple databases (log entries, data, 
data analyses, notices, actions, procedures, links to 
workspaces and references) and embed them in 
editable reports. Example reports include notices, 
handover reports, and anomaly reports. 
• Notifier - manage notification of team members at 
varying locations in various roles. 
• Portal - links to most recently changed data in the tools. 
The Team Work Center prototype includes the Portal and 
three of the tools: Logger, Workspace Manager, and 
Report Maker. IBRAs use these tools to manage 
information from autonomous systems (ISMAs) and to 
increase team understanding of events, assessments, 
priorities and plans. We have demonstrated feas ibility of 
our concepts and prototypes in a testbed where simulated 
advanced life support hardware is operated by an 
autonomous system management agent (Malin et al. 2002). 
Avoiding Bad Outcomes 
What can autonomous systems provide human team 
members in the way of orienting information and 
coordination support, so that unexpected and inappropriate 
outcomes can be avoided? Cognitive theories of human 
error (Norman 1981 ; Rasmussen 1982; Reason 1990) 
discuss the types of slips and mistakes that can occur at 
stages in automatic and conscious processing of 
information, intentions and actions. These theories indicate 
stages where faulty cognitive processing or fau lty 
information can lead to bad outcomes. We have enhanced 
and integrated aspects of these theories to address stages of 
information processing in both human and software agents 
(Figure 2). 
INTENTION SELECTION MEANING 
Intended 
State 
Structure 
CO NSTRUCT 
Activity 
Structure 
COO 
CON 
RDINATE/ 
TROL 
COMPLEXITY 
E 
Activity 
XECUTE 
CONSTRUCT Assessed 
~ 
... ~~ t State Structure 
H Resources I I Goals I ..: ... 
~ I Models I .. ... ~ 
." 
MATCH State 
~ .. t 
I Rules I ~~ 
I State Context I I Expectation ~ , 
TRIGGER 
Signal 
.... t 
I Skills I Attention ~~ 
u 
World/Physical SystemlEnvironment 
Affordances, Feedback, States, Functions, Operations 
ANALYZE 
INTERPRET 
P ERCEIVE 
Figure 2. Model of stages of information processing in controlled agent behavior. 
J 
This model partitions controlled agent behavior into three 
levels of complexity and three types of stages of 
information processing. The three types of stages are 
processing meaning, making selections and processing 
intentions. In this theory, an activity is an intended action 
with associated agent(s) and resources, and standards for 
evaluating success. The explicit selection stages extend and 
integrate concepts from other theorists. The processing that 
depends on skills, rules and knowledge is located at 
multiple levels of complexity in selection. The three levels 
of complexity are also used to make distinctions in the 
scope and permanence of internal knowledge that is 
required for processing. Internal conscious knowledge 
includes rules, models, goals and resource knowledge. 
There are opportunities for error at each of nine stages in 
processing. There are additional causes of error in faulty 
sk ills, rules, beliefs, goals and knowledge that are applied 
in the stages of processing. Understanding the basis of an 
action or an error may involve understanding all these 
factors. 
For successful coordination, human agents and 
autonomous systems need a variety of contextual 
information concerning agent skills, rules, knowledge, 
intentions and procedures. How can orientation and 
interaction be designed to support human agents who will 
intervene to help autonomous agents manage unexpected 
events, including agent errors? The amount and variety of 
information that is needed for understanding increases as 
agents become more complex. Likewise, information 
needs increase as agents need more help, from assessment 
to direction, to correction and then to handover. For 
coordination, information needs to be understandable in 
the context of the interaction and types of control that are 
supported by the agent. These include supported types of 
direction (opportunities for control) and correction 
(opportunities for changing skills, rules and knowledge) . 
To assess the appropriateness and urgency of an action and 
to plan an intervention, it may be necessary to understand 
much more than action content and status. Other needed 
information may include the place of the action in an 
activity structure, how intentions were constructed, what 
assumptions and models were used in the reasoning, and 
what goals and standards were applied or constructed for 
evaluating success. 
With increasing information demands, avoiding error in 
interaction with complex autonomous systems becomes 
more of an information management problem. Information 
agents such as IBRAs can perform some of this work. It 
will be important to build detailed scenarios of types of 
coordination that autonomous agents are required to 
support, to learn how to structure and present information 
that supports understanding and intervention. 
Conclusion 
In this paper, we have discussed several ways in which 
interaction in heterogeneous mission operations teams is 
not we ll matched to classical models of coordination with 
autonomous systems. We have presented methods of loose 
coord ination and information management used in mission 
operations. We have briefly described an information agent 
and information management tool suite that we have 
prototyped and demonstrated. We are continuing to design 
and develop agent and tool capabilities, using the 
information agent to manage information from many 
sources including an autonomous agent. We have 
presented a new cognitive model of agent and human 
errors that shows types of information processing and 
points of error in agent activities. We have used this model 
to show the range of information that may be needed for 
diagnosing problems in an autonomous system and 
planning interactions. We call for building detailed 
scenarios to discover requirements for autonomous system 
functions to support various types of coordination. 
Acknowledgements 
This work is funded by NASA research and development 
programs in Engineering Complex Systems and Intelligent 
Systems. The authors want to thank Michael Shafto for his 
constant support of this interdisciplinary work. We wish to 
thank members of the project teams for their excellent 
work: Land Fleming, Lou Flores, Kenneth Jenks, Grace 
Lei, Arthur Molin, Patrick Oliver, David Overland, Gene 
Peter, Debra Schreckenghost, Dan Smith, Kevin Taylor, 
Carroll Thronesbery and Jiajie Zhang. 
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