The advantages of an AI system of actively monitoring human control of a shared resource (such as a telerobotic manipulator) are presented. A system is described in which a simple AI planning program gains efficiency by monitoring human actions and recognizing when the actions cause a change in the system's assumed state of the world. This enables the planner to recognize when an interaction occurs between human actions and system goals, and allows maintenance of an up-to-date knowledge of the state of the world and thus informs the operator when human action would undo a goal achieved by the system, when an action would render a system goal unachievable, and efficiently replans the establishment of goals after human intervention

Hendler, James

Wilson, Reid

English

NASA Technical Reports Server

N8 9-  26 5 8  0 .- 
Shared Resource Control between Human and 
Computer 
James Hendler* 
Reid Wilson 
Computer Science Dept . 
University of Maryland, College Park 
February 6, 1989 
1 Introduction 
One area of Artificial Intelligence systems for space applications is to serve as a mechanism for 
telerobotic control. When interaction is called for, it is usually viewed as a passing of cont,rol with 
either an AI system or a human in complete command of the teleoperated system. Unfortunately, 
this can cause serious inefficiencies in the planning process, both for the AI system and the human, 
when the plans and actions of each may cause potential conflicts. If the human were to, for 
example, remove a bolt which the AI system had previously placed, then it is important for the 
robot to take this information into account in planning a later action concerning tliat bolt or its 
assembly. If this information must be rediscovered by the AI system upon regaining coiit,rol, the 
system must spend much time both checking for states already achieved and for replanning the 
accomplishment of previously achieved actions. 
This paper describes the advantages to an AI system of actively monitoring a human’s control 
of a shared resource (such as a telerobotic manipulator). We describe a system in wliich a simple 
AI planning program gains efficiency by monitoring a human’s actions and recognizing when they 
cause a change in the system’s assumed state of the world. This enables the planner to recognize 
when an interaction occurs between the human’s actions and the system’s goals, and allows it  
to maintain an up-to-date knowledge of the state of the world and thus to (i) inform the liuxnan 
when an action would undo a goal achieved by the system, (ii) inform the human whcn an action 
would render a system goal unachievable, and (iii) efficiently replan the establishment of goals 
after human intervention. 
*Funding for this work was provided in part by the Office of Naval Research, the UM Institute for Advanced 
Computer Studies, and the Systems Research Center 
13 
PRECEDING PAGE BLANK NOT FILMED 
https://ntrs.nasa.gov/search.jsp?R=19890017209 2020-03-20T02:06:02+00:00Z
Shared Resource Control 
The current view of shared resource control between a human and an AI system generally centers 
on one or the other partner being in full control of the resource at any given time. Thus, in  a 
telerobotic system, either a human operator or an AI  system may be controlling an arm. When 
the human is in control the AI system is quiescent. However, when the AI system is in control, the 
human is viewed as being in a supervisory role. This allows a human to monitor the system and 
make sure that no unanticipated problems are caused by the system. (This is frequently the case 
in telerobotic systems because of the need for a human operator to deal with unanticipated or 
emergency situations outside of the AI system’s problem solving capabilities. This is particularly 
important when the situation is somewhat fluid, that is, when an earlier unexpected event may 
cause repurcussions not anticipated at system design time. As an example of such an event, 
consider an undeployed solar collector which might cause power consumption considerations not 
anticipated at system design time). 
It is our contention that in a similar manner to a human’s supervision of the -41 system, the 
-41 system must monitor the human’s use of the shared resource. While we believe that is is 
important that the human maintains control (i.e. has the final word), the AI  component can 
both gain efficiency and provide some measure of error checking by performing this monitoring. 
Allowing the AI system to monitor human performance avoids serious inefficiencies in the planning 
process and provides benefits not possible where the human is not monitored. 
Probably the most important benefit to the AI system is its ability to update its internal view 
of the world to reflect any changes observed during the times of human control. This is very 
important, because by monitoring, the system will always have an accurate view of the world. 
Although there is some overhead in such a process, the planner will have to update its state of 
the world in response to each human action, the benefit provided is that the AI system never 
needs to reestablish the current state of the world. 
The ability of the system to maintain a complete and accurate world model is quite important. 
In current systems, upon completion of the human’s control of the resource, the AI  system is left 
with an incomplete, and possibly incorrect world model. Thus, for example, if a human has 
moved some object (say a bolt), the robot must either explicitely check that the object is still 
where it was previous to human intervention (requiring a move to the location of the bolt), or it 
must assume no harmful interactions will have been caused by the human and try to work wit11 
an incomplete state model. Unfortunately, working with an incomplete state model is a major 
problem for current AI system (a long discussion and literature review of this issue is presented 
in Sanborn and Hendler, 1988). Thus, the planner is faced by a choice between reestablisliing 
all conditions, which is a highly resource intensive and inefficient process, or it must work from 
incomplete knowledge with all the attendant dangers. By monitoring the human’s actions, to 
maintain an awareness of the actual state of the world, the planner need not hare to rediscover 
information each time control is returned to it without having to sacrifice information ccrtainty. 
Another reason for the need for the AI system monitoring the human is that nrlieii part,ners 
share control of a resource they may have conflicting, complementary, and/or unrelated goals. In 
14 
each case, having the passive agent monitor the actions of the controlling partner allows significant 
benefits: 
0 Where a conflict arises, the partners may resolve it early. 
0 Where cooperation may occur, similar preconditions are not reestablished needlessly. 
0 Where goals are unrelated, accidental interactions (such as moving a tool required Mer) 
are largely alleviated. 
In the remainder of this paper we describe a small system which was designed solely to show 
the efficacy of such monitoring. In the next section we describe the system and sliow, by way 
of an example, how the monitoring provides for the sorts of interactions described above. This 
system is quite limited, and is in no way proposed as an actual telerobotic planning syst,em. Some 
steps towards that direction are discussed in section 4. 
3 An Example System 
To demonstrate how shared control better enables the partners to handle interacting goals, we 
have developed a very simple blocks world system in which control of a “ r ~ b o t ”  is shared 1xAween 
a human operator and an Artificial Intelligence Planning system. (In section 4 we will discuss 
limitations of this sort of system viz. realistic telerobotic control.) Instead of having one or 
the other partner control the robot independently (;.e., the other not receiving simiiltaneoiis 
feedback), both parties are active in monitoring the actions of the other and the effects on tlieir 
goals. Thus this shared control allows the passive agent will determine how the actions of the 
controlling agent affect its goals, and react appropriately (interacting as necessary). 
In our system the “robot” can be used for the manipulation of items in a simple domain: a set 
of various blocks of different size and orientations. Neither the blocks nor the robot may be moved 
off the edge a simple “world’ consisting of a simple grid. Only one agent (human or computer) 
may actively control the robot at one time, although the other receives the commands given to 
the robot before they are executed. While the AI system is in control, the human is allowed the 
human to assume control of the robot at any time (essentially, after any discrete move or stcp). 
A set of commands is given to an AI planning system, and it attempts to control the “10l10t” 
to acheive these goals. To do this, the planner maintains three (3) goal stacks: goals to do 
(TO-DO), goals done (DONE), and goals the planner needs to re-establish (RE-DO) because the 
human broke them after they were achieved. The planner does not sort the TO-DO or RE-DO 
goal stacks, but attempts to complete them in order from the top goal in the stack to the hott,om. 
Thus, we assume that a goal ordering has already been achieved earlier in the planning cycle, and 
that we are now at execution time. 
The AI system is designed to assume that a human is better able to determine how to deal 
with difficulties in the world. Thus, If the current goal cannot be achieved, the planner prompts 
15 
Goals : 
TO-DO: ((BLOCK1 ((0 2)) AT)) 
RE-DO: NIL 
DONE: NIL 
: AI system cannot achieve move of BLOCK1 to (0 2). Please assume 
: control. 
Figure 1: AI system requests human intervention. 
the human for help to achieve it. Figure 1 demonstrates a case in which the AI syst8eni can 
accomplish a goal with the human’s assistance that it could not achieve on its own. A deficiency 
of the planner is that it does not know it can push a block into its goal position by pushing anothw 
block into it. When it recognizes its goal is unachievable, it prompts the human for intervention. 
The human may then evaluate whether the goal should be modified or assume control of the 
system to help the planner achieve it. In this example, the human can just push BLOCK3 left 
or move it out of the way. With the human’s assistance, complicated goals may be reduced to 
simpler goals achievable by the planner. 
A more interesting behavior can be observed when an interaction occurs between tlic goals 
achieved by the planning system and the actions required by the human. In this case, the robot 
warns the human about the potential interaction, and asks for confirmation. Thus, the liuniitn is 
able to avoid accidently interfering with the planning system. If another move n.ould solve the 
same problem, the human might choose to avoid the interaction. 
In Figure 2, we see a situation where trying to move the robot to the right will result in  tlie 
human breaking a achieved goal namely the origin of BLOCK1 at position ( 7  7 ) .  Tlie AI planner, 
monitoring the human’s actions, will require confirmation that this is an intentional conscqnence. 
In this case, the human will avoid breaking the goal. Checking the goal stacks, one sees that no 
change has occurred. 
If the human does decide to proceed with the move, the planner is able to recognize that a 
goal has been made invalid (i.e. “broken”). This goal is moved to a RE-DO stack, and will be 
re-established when the planner regains control. In this manner, the planiiing syst,em is alilc to 
avoid taking later steps whlch rely on previously achieved goals which are no longer valid. 
16 
0 
1 
2 
3 
4 
5 
6 
7 
8 
9 
Cur ren t  Goals :  
Human Ac t ion :  
System Response:  
Human Ac t ion :  
Cur ren t  Goals  : 
TO-DO:  ((BLOCK2 ( ( 7  6))  AT) (BLOCK3 ( ( 7  8 ) )  AT)) 
DONE: ((BLOCK1 ( ( 7  7 ) )  AT)) 
move r i g h t  
Moving t o  s q u a r e  ( 5  4) w i l l  b r e a k  an  a c h i e v e d  g o a l .  
Are you s u r e ?  ( Y / [ N I )  
N 
TO-DO: ((BLOCK2 ( ( 7  6))  AT) (BLOCK3 ( (7  8)) AT)) 
RE-DO: N I L  
RE-DO: NIL 
DONE:  ((BLOCK1 ( ( 7  7 ) )  AT)) 
Figure 2: AI makes human aware of interaction 
In Figure 3, we see that the human is not directly pushing a block from a required location, 
but as a consequence of the command to push BLOCKS the already completed goal involving 
BLOCK1 will be broken. Prompted again for confirmation, the operator this time confirins the 
action. At this point we see that in the planners goal tables, the BLOCK1 goal lias becn moved 
from the DONE stack to the RE-DO stack. Because the AI system monitored the liiimiui’s 
actions, it realizes that goal needs to be re-achieved, and plans accordingly. (Note that the goal 
involving BLOCKS, which was at its target destination, was not moved because the 1 h i n c r  only 
sees the top goal in the stack, and had not therefore achieved that goal.) 
In addition, beneficial actions may also be observed. This, if the AI system recognizcs that 
the human has moved the robot such that the planner’s current goal is achieved, the system is 
aware of this and moves the goal to the DONE stack. Had the human not re-achieved the goal, 
that would have become the planner’s current goal. 
Finally, to remphasize, at each stage in the interaction the AI planning system is maint,aiiiiiig 
an accurate world model. goal. Because it has been monitoring the human’s actions, it knows 
where the robot is, where each block is, and the correct contents of the goal stacks. Thus, upon 
17 
0123456789 
Cur ren t  Goals :  TO-DO:  ((BLOCK2 ( ( 7  6 ) )  AT) (BLOCK3 ( ( 7  8 ) )  AT)) 
RE-DO: NIL 
DONE: ((BLOCK1 ( ( 7  7 ) )  AT)) 
Moving t o  s q u a r e  (5  4)  w i l l  b r eak  an a c h i e v e d  g o a l  
Are you sure? ( Y / [ N ] )  
Human Ac t ion :  move up 
System Response:  
Human Ac t ion :  Y 
Cur ren t  Goa l s :  TO-DO:  ((BLOCK2 ( ( 7  6 ) )  AT) (BLOCK3 ( ( 7  8 ) )  AT)) 
RE-DO: ((BLOCK1 ( ( 7  7 ) )  AT)) 
DONE: NIL  
Figure 3: Goal moved to RE-DO stack 
regaining control it plans its current goal without having to resetablish the positions of the blocks. 
4 Real-time Considerations 
While the simple system we have implemented shows the need for the AI  system to monitor human 
operation, in the real world the problem is obviously significantly more complex. The most 
significant difference is that the human control is not performed by giving easily recognizable 
commands to an AI system, but rather by the continuous operation of the telerobotic device. 
Thus, even the simple recognition of the human’s invalidating of a previously achieved goal 
becomes a major problem. 
For monitoring to be applicable in the real world, as opposed to a simulation environment, 
a large amount of run-time support for real-time computing is necessary. Our current research 
(Hendler, 1989) focuses on examining shared resource control in a real-time environenient via the 
use of RSARUTI, a hard real-time operating system developed by the Systems Research Group 
18 
at the university of Maryland (Levi et. al, 1988). The MARUTI OS is designed to support 
real-time applications on a variety of hardware platforms. MARUTI supports guaranteetl-scr~icc 
scheduling, in which jobs that are accepted by the system are verified to satisy gencral time 
constraints. In MARUTI, the system automatically verifies the schedulability of each conlpoilcntj 
of a job with respects to its constraints and those of other jobs in the system. 
The use of real-time in a telerobotic environment also poses a problem. The tolerance for 
response times must be large enough that the human may effectively interact, and without ob- 
solete information. However, when there is a time lag, as in space applications, having tlie -41 
system monitor the human actions may be especially useful as the human might not othern-ise 
get feedback until damage is already done. One does not, for example, wish an operator to get a 
warning about a potential interaction after it has occurred. 
5 Conclusions 
In this paper we have discussed the need for having an AI system monitor hiiman usage of a 
shared resource. As discussed, we have demonstrated that this allows greater efficiency for t hc 
AI system, as an accurate world model can be maintained, and also allows for early dctcction of 
interactions enabling the discovery of cooperation or the avoidance of conflicts. 
6 References 
Hendler, J.A. “Real Time Planning” Proceedings A A A I  Spring Symposium on Planning and 
Search, March, 1989 (In Press). 
Levi, S., Tsipathi, S., Carson, S. and Agrawala, A The M A R U T I  Hard Real-Time 0yertl.iing 
S y s t e m  Technical Report, Computer Science Dept., University of Maryland, 1988. 
Sanborn, J. and Hendler, J. “Monitoring and Reacting: Planning in dynamic domaiiis” Inter- 
national Journal of A I  and Engineering, Volume 3(2), April, 1988. p.95. 
19 


Shared resource control between human and computer

Abstract

Similar works

Full text

Available Versions

NASA Technical Reports Server