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N89 - 15559 

PLANNING ACTIONS IN ROBOT AUTOMATED OPERATIONS 

A. Das 

Computer and Info. Sci. Dept. 

Alabama A&M University 
Normal, AL 35762 


ABSTRACT 

Action planning in robot automated operations requires 
intelligent task level programming. Invoking intelligence 
necessiates a typical blackboard based architecture, where, a 
plan is a vector between the start frame and the goal frame. 
This vector is composed of partially ordered bases. A partial 
ordering of bases presents good and bad sides in action 
planning. Partial ordering demands nonmonotonic reasoning via 
default reasoning. This demands the use of a temporal data base 
management system. 

INTRODUCTION 

Advanced technology for the space station and the US 
economy necessiates substantial use of general purpose 
automation and robotics requiring new generation machine 
intelligence and robotics technology. Three years ago, on this 
issue, NASA's Advanced Technolog Advisory Committee published a 
set of 13-point recommendations [ 1] . Intelligent plan adoption by 
robots is one major vital curriculum. Its ultimate purpose is to 
imply multi-level environment perception and modelling, 
decisional autonomy ranging from general planning to specific 
task operating, autonomous mobility capacity, sophisticated high 
level man-machine interface and efficient execution control 
systems. However, vagaries of the real world , its geometry, 
inexactness and noise pose large practical problems to the 
researcher and this forces investigations to have excercised on 
a handful of toy examples. 

Recently, an attempt has been made to create an 
architecture for simulating intelligence in robot automated 
assembly operation[2] . In this architecture the reasoning system 
works with a two-dimensional system configuration, a task level 
configuration (embeded to high level plans and common sense 
reasoning) and a robot level configuration (numeric activities 
to live with ordinary geometric world) . In programming robots 
the task level operations are specified according to their 
expected effects on objects, detailed kinetics of motion even as 
functions of inputs are not considered directly. In the process 
of task level programming every new robot level task (geometric 
world) is seen as a clusture of incremental planning at the task 
level (plans and reasons). Any problem in this incremental 
planning working with this two-dimensional system configuration 
is resolved by a blackboard based problem solver [6]. In ttris 
blackboard, all classes of temporal, spatial and event class 


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If- 


relationships invoked by the task structure are examined, and 
suitable prescriptions generated. The current architecture looks 
at task level plans vectors connecting the start frame to the 
goal frame and are composed of bases <R,T>s, where R represents 
robot level parameters and T represents task level plan 
configuration operative in the geometric world given by R. <R,T>s 
are seen to form a partially ordered set to admit mutations with 
time. 

This paper gives a closer look at the preconditions 
existing in action planning for a robot at the task level. The 
implicability of partial ordering of <R,T>s is questioned and 
associated problems are formulated with common sense reasoning. 
It is observed that this leads to reasoning by default [9] 
bringing task level planning in the paradigm of nonmonotonic 
reasoning. A demand for a temporal data base system[3] for 
action planning seems inevitable. 

PLAN VECTOR ON A BLACKBOARD 


In ref. 2 a plan is a vector in the robot level task level 
configuration space: 


c l <R i' T j > + c 2 < V T n > + + c n <R y' T z > < Eq - 1 > 

where C^ ,C2, ,C n are real or complex numbers. <R,T>s are 

bases that mutate with time, also. They comprise a partially 
ordered set. A blackboard is a problem solver [3] enriched with 
highly domain specific heuristic knowledge. Ref. 2 discusses the 
diagnosis of the simple case of the movement of the robot arm on 
a blackboard. Since the robot does not do only one single job, 
and since most of the tasks require repetition of the same 
subtasks several time, a comparative diagnostics is attributable 
on the black board. Two or more plans are compared side by side. 
Searching "plan-invoking macros" becomes more effective. There 
are problems, however. 

WHY PARTIAL ORDER ? 

In eq.l <R,T>s are partially ordered bases. This gives 
freedom in forming the plan vector intelligently. The total 
geometric space configuration assumed in performing a job may 
then be seen as composed of a series of subtasks. Each of these 
subtasks was constructed out of realizable <R,T>s. Thus two plan 
vectors designed for totally two different jobs may be known as 
differeing by additions, subtractions and modifications of some 
<R,T>s. One plan, say, is goint to the grocery store and 
another, going to the doctor's office. These two are implemented 
in this way: 


Grocery 

Go 2 miles straight. 

Turn left, go straight. 
Turn left, go straight. 
Turn right, go straight. 


Doctor 1 s Office 

Go 2 miles straight. 

Turn left, go straight. 
Turn left, go straight. 
Turn right, go straight. 


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Turn left, go straight. Turn left, go straight. 

Turn left, go straight. Turn right, gostraight. 

These two plans differ in their final turn. Bases <R,T>s are 
partially ordered in both these plans. Therefore, if one plan is 
implemented successfully and the other is not, a comparative 
diagnostic measures can be worked out (possibly searching common 
macros in both the cases) . If on the other hand <R,T>s were 
totally ordered, the two plans differ without any flexibility of 
having a match between them. 

POSSIBLE TROUBLES IN PARTIALLY ORDERED <R,T>S 

The aforsaid example of comparing two plans on the basis of 
partially ordered bases <R,T>s requires that a strong table 
management system admonishing context dependent properties of 
<R,T>s need be present (A comparative diagnostics of a faulty 
plan, or, an working plan showing bugs later requires all the 
macros in correct order invoking the two plans) . If in the 
second plan, for example, a fault is observed in the last right 
turn, then the comparative diagnostics rquires an account of the 
past history in correct order in both the plans. If you have 
known how to go to the grocery store (Plan 1) then going to the 
doctor's office (plan 2) needs a little modification in the final 
phase. Tracing the two plan vectors side by side with <R,T>s 
implementing them is necessary in case of bugs observed in one 
(or both) of them. Such tracing of history also requires 
cataloging and comparing time of occurrence of all subtasks, 
their duration needs to be noted too. To understand why you 
could not reach the doctor's office requires answering a question 
like how long did you take to perform the first two left turns, 
for example. Temporal ramifications of <R,T>s and managing their 
order of context dependency causes trouble in working with 
partially ordered <R,T>s. Obviously, it is a horrendous task. 

THREE MORE PROBLEMS 

Three more problems will arise in comparative diagnostics, 
infact, in any reasoning about action. These three problems are 
the frame problem of McCarthy and Hayes[8], qualification problem 
of McCarthy[7] and the ramification problem of Finger[4]. 

The frame problem enters into comparative diagnostics when 
two or more plan vectors are compared basis by basis, or subtasks 
by subtasks to determine which of them remain invariant in time 
while the action is taking place. If I succeed in going to the 
grocery shop but fail to go to the doctor's office, it 
was necessary to be determined that these two plans differed only 
in their final turn (as seen before) . No interim unwarrented 
turn is admissible in both the plans, they were framed by 
preconditions . 

This framing of preconditions lead to the second problem 
called qualification problem. It arises because the number of 
preconditions are always very large. Imagine all of the 


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possibilities that prevented me in taking the last right turn 
while going to the doctor's office. Probably, my car broke down, 
probably, it was raining heavily and I missed the road sign. 
Probably, I stopped somewhere before the final turn to buy coffee 
and while taking off took a different direction. Probably, in 
the last right turn there was a detour sign forcing direction 
change. It is very unaffordable to pin point all these worldly 
possibilities . 

The third problem is the ramification problem which is very 
severe in comparative diagnostics because it is unreasonable to 
explicitly record all the consequences of actions. In both our 
examples of plans on going to grocery and doctor's office a great 
number of possible consequences may occur which may not have any 
consequence at all. In going to the grocery store after making 
the first left turn I may see the fish market and buy some fish 
and after the next turn I may find my sister's home nearby and 
deliver part of the fish to her. The applicability of these 
ramifications for one plan will not be the same for the other. 
Moreover, the comparative diagnostician will not be able to 
work out which ramifications are supposed to show up any time for 
any plan vector under investigation. Inference in default logic 
may be a way out. 

INFER <R x ,Ty> FROM THE INABILITY TO INFER <R m ,T n > 

All the ramifications of any plan vector must be expressible 
using bonafide bases <R,T>s. It turns out that if there are n 
<R,T>s in a plan vector, any one single new <R,T> for admitting a 
new event or action in the plan vector will require n 
verfications for consistency with existing constraints. 
Therefore, reasoning with default logic automatically sets in: 
facts persists in the absence of information to the contrary. If 
I want to compare my faulty plan not leading to the doctor's 
office with the successful plan leading to the grocery store, all 
the possible ramifications that may be present in both successful 
and unsuccessful plans needs to be assumed existing, because they 
cannot be verified. This is expressed using Reiters default 
rules [9] : 


<R, T> t : 


<R,T> 


do( a , t ) 


(Eq. 2 ) 


<R ' T> do(a,t) 

Which states that if <R,T> is true in time (or situation) t and 
<R,T> are still consistent after the action a, then we can infer 
<R,T> after the action. Computational problems still persists, 
though. To determine what is true after an action has been 
performed, the default frame axiom must be examined once for 
every fact of interest [5]. 

PLANNING ACTIONS 


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Within the context of above mentioned observations and 



conditions planning actions needs a strong nonmonotonic 
reasoning system for its support. The three immediately visible 
reasons for this are: the presence of incomplete information 

requires default reasoning, a changing world must be described by 
a changing data base, temporary assumptions about partial 
solution may be required for generating a complete solution[ 10] . 
In the present model the bases <R,T>s chronologically mutate in 
time between the start frame and the goal frame. This manifests 
a temporal data base system which is an extension of clasical 
predicate calculus data base. Mutations of <R,T>s also means 
that temporal information is incomplete, that is, our knowledge 
on the occurrence of events totally admits to partial ordering of 
<R,T>s to implement a plan. Such a system can be fruitfully 

dealt with a data base system called the time map manager or 
TMM [ 3 ] . Such a TMM admits shallow temporal reasoning to be 
consistent with the default reasoning system, because by default 
a deductive reasoning system works with a small number of 
calculations. Shallow reasoning systems provide the TMM with a 
mechanism for monitoring the continued validity of conditional 
predictions. Thus, the TMM rearranges tasks to take advantage of 
existing preconditions and warns of unexpected dangerous 
interaction between the effects of unrelated tasks. 

CONCLUSION 

The comparative diagnostics on a blackboard with the help of 
a time map manager is akin to visually scanning a massive amount 
of data organized in the form of a map. This brings a graphical 
picture to action planning. Default reasoning makes this action 
planning a nonmonotonic shallow reasoning system. Hints are 
there that using the time map manager a comparative diagnositcs 
on a blackboard may be able to overcome some classical problems 
associated with partial ordering of <R,T>s. It is currently 
under investigation. 

REFERENCES 

[1] Cohen, A. and Erickson, J.D., "Future Uses of Machine 
Intelligence and Robotics for the Space Station and 
Implications for the U.S. Economy", IEEE Journ. Robotics & 
Automation, Vol. RA-1, No. 3 (1985) 117-123. 

[2] Das. A. and Saha. H. , "Embedding Intelligence In Robot 

Automated Assembly", Proc. First Int. Conf. on Indus. & 
Engng. Appln. of A. I & Expert Systems, Tullahoma, 

Tennessee, June 1-3 (1988) 1083-1088. 

[3] Dean, T.L. and McDermott. D.V. , "Temporal Data Base 
Management", Artif. Intell. Vol. 32, No-1 (1987) 1-55. 

[4] Finger, J.J., "Exploiting Constraints In Design Synthesis", 
Ph.D. thesis, Stanford Univ. , Stanford, CA (1987). 


Ginsberg, M.L. and Smith, D.E., "Reasoning About Actions I: 

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[ 5 ] 


A Possible Worlds Approach", Artif. Intell. Vol. 35, No. 2 
(1988) 165-195. 


[6] Hayes-Roth. B. , "A Blackboard Architecture For Control", 
Artif. Intell. Vol. 26, No. 3 (1985) 251-321. 

[7] McCarthy. J., "Epistomological Problems of Artificial 

Intelligence", Proc. IJCAI-77, Cambridge, Mass (1977) 1038- 

1044. 

[8] McCarthy. J. and Hayes. P.J., "Some Philosophical Problems 
From The Standpoint of A. I." in Machine Intelligence-4, ed. 
by Meltzer.B. and Michie. D. , American Elseveir, N.Y. (1969) 
463-502. 

[9] Reiter. R. , "A Logic For Default Reasoning", Artif. Intell. 
vol. 13, No. 1 (1980) 81-132. 

[10] See for example, Artificial Intelligence by Rich. E. , 
McGraw-Hill Book Co., NY, 1983. 


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