Peters, Alan

NASA Technical Reports Server

NASA Tech Briefs, June 2007 29
Information Sciences
Algorithm Would Enable Robots to Solve Problems Creatively
A control architecture is based on hypotheses concerning natural intelligence.
Lyndon B. Johnson Space Center, Houston, Texas
A control architecture and algorithms
to implement the architecture have
been conceived to enable a robot to
learn from its experiences and to com-
bine knowledge gained from prior expe-
riences in such a way as to be able to
solve new problems. The architecture is
an abstraction of an interacting system
of relatively simple components that,
when properly interconnected, should
enable the spontaneous emergence of
behaviors from the complete system that
would not necessarily be expected from
the individual components. These emer-
gent behaviors should enable a robot to
interact robustly and intelligently with a
complex, dynamic environment.
Departing from the customary ap-
proach to artificial intelligence, the the-
oretical foundation of this architecture
includes a hypothesis that has gained
some acceptance in research on natural
intelligence in animals. Stated suc-
cinctly, the hypothesis is that intelli-
gence requires embodiment, situated-
ness, development, and interaction.
Applied to a computer, the hypothesis
states that to be able to develop intelli-
gence, a computer must be able to,
among other things, manipulate the en-
vironment and sense the results; in
other words, the computer must be
equipped with a robotic interface with
the physical world. A related hypothesis
is that sensory-motor coordination
forms the basis for intelligent behavior,
and, hence, intelligence. This follows
from the demonstrated ability of sen-
sory-motor coordination information to
self-organize into categories.
The architecture represents a set of
parallel distributed computational mod-
ules (software objects) that communi-
cate with each other through message
passing. The principal objects loosely
correspond to the (presumed) common
computational modules of mammalian
brains. A set of sensory processing mod-
ules continually updates a spatio-tempo-
rally indexed short-term memory struc-
ture denoted the sensory ego-sphere
(SES). Depending on the task context,
an attentional mechanism determines
the saliency of incoming sensory infor-
mation. In response to changes in the
state of motion of the robot, the time se-
ries of sensory information is partitioned
into episodes. Episodes are encoded in
vector form and stored in a database, de-
noted the database associative memory
(DBAM), that comprises the long-term
memory of the robot.
The DBAM has the implicit mathe-
matical structure of a linear vector
space that is constructed as the direct
sum of three subspaces. One subspace
encodes a change in motor state (a
motor event). Another subspace en-
codes the state of the sensors immedi-
ately prior to the motor event — in
other words, the sensory preconditions
of the motor event. The third subspace
encodes the sensory state subsequent to
the motor event (sensory post-condi-
tions). The vectors in the DBAM and
the external events from which they are
formed are called sensory-motor coor-
dination (SMC) events.
Several research projects have demon-
strated that over time, as  a robot senses
and acts, clusters form within a vector
space of SMC descriptors. Categories
emerge. Thus, it becomes possible to se-
lect an exemplar that describes an equiv-
alence class of SMC events. The encod-
ing of SMC events as motor events with
sensory preconditions and sensory post-
conditions makes it possible to use sim-
ple vector-space distances as measures of
dissimilarity or similarity of complete
SMC events. Vector-space distances can
also serve as measures of dissimilarity or
similarity between the sensory post-con-
ditions of one event and the sensory pre-
conditions of another.
The architecture utilizes a control
strategy, called topological action map-
ping, that appears to be employed by
mammals  to achieve specific goals. In
this architecture, action maps are imple-
mented  by a spreading activation net-
work (SAN). (A SAN is one of a particu-
lar class of graph traversal algorithms
that selects optimal paths.) For each goal
to be reached by the robot, there corre-
sponds an SMC event. Moreover, the cur-
rent state of the robot is indicated by an
SMC event. Given the goal event, the
SAN searches through the DBAM,
matching its sensory preconditions to
the sensory post-conditions of other
events. The search is constrained by a
number of global state variables and pre-
viously formed linkages. Upon finding a
sufficiently close match, the SAN iterates
the search until the current state is
reached. The current motor state
(hence, the current behavior) is main-
tained until the sensory post-conditions
trigger a change of state. Thus, the robot
proceeds through a sequence of behav-
iors until the goal is reached. 
One key aspect of this architecture is
that the robot cannot be programmed
through conventional means: it must
be taught to perform tasks, either
through direct control or by example.
The robot must practice each task sev-
eral times to enable the formation of
clusters of SMC events and to learn the
sequences of these events. Such repeti-
tion enables the robot to perform the
task later, without supervision. How-
ever, this does not, in itself, enable cre-
ative problem solving.
To be able to solve problems cre-
atively, the robot must engage in a
process analogous to dreaming. After in-
tervals of continuous motor activity, the
robot performs computations in which it
plays back data on recently performed
sequences of tasks. SMC events are com-
pared quasi-randomly to other events in
the DBAM. If the sensory post-condi-
tions of a given event are sufficiently sim-
ilar to the sensory precondition of an-
other event, links are formed between
them. Such a link is formed, even if the
two behaviors have never occurred in se-
quence before. This link gives the robot
a new possible behavior transition that it
could make, given the appropriate sen-
sory trigger. Thus, the robot learns to an-
https://ntrs.nasa.gov/search.jsp?R=20100002241 2019-08-30T08:32:52+00:00Z
30 NASA Tech Briefs, June 2007
ticipate possible sequences of events and
learns strategies for the solving of prob-
lems that it has not encountered but
that could occur.
The architecture is still undergoing
development. An SES has been imple-
mented in the NASA Robonaut (a de-
velopmental anthropomorphic robot
intended to serve as an astronaut’s as-
sistant). Production-quality software
to implement the architecture has not
yet been written. A major problem in
writing this software is that of efficient
representation of sensory data in vec-
tor spaces.
This work was done by Alan Peters of
Vanderbilt University for Johnson Space
Center.
In accordance with Public Law 96-517,
the contractor has elected to retain title to this
invention. Inquiries concerning rights for its
commercial use should be addressed to:
Christopher D. McKinney, Director
Office of Technology Transfer 
and Enterprise Development
Vanderbilt University
1207 17th Avenue South, Suite 105
Nashville, TN  37212
Phone: (615) 343-2430
E-mail: chris.mckinney@vanderbilt.edu
Refer to MSC-23489, volume and number
of this NASA Tech Briefs issue, and the
page number.
The Hypothetical Scenario Generator
for Fault-tolerant Diagnostics (HSG) is
an algorithm being developed in con-
junction with other components of arti-
ficial-intelligence systems for automated
diagnosis and prognosis of faults in
spacecraft, aircraft, and other complex
engineering systems. By incorporating
prognostic capabilities along with ad-
vanced diagnostic capabilities, these de-
velopments hold promise to increase the
safety and affordability of the affected
engineering systems by making it possi-
ble to obtain timely and accurate infor-
mation on the statuses of the systems
and predicting impending failures well
in advance.
Prognosis is tightly coupled with diag-
nosis. The simplest approach to progno-
sis by an artificial-intelligence system in-
volves the use of a diagnostic engine in a
controlled feedback loop to project
from the current state of the affected en-
gineering system to future states that are
elements of scenarios that are discov-
ered hypothetically. A hypothetical-sce-
nario generator is a key element of this
approach. A hypothetical-scenario gen-
erator accepts, as its input, information
on the current state of the engineering
system. Then, by means of model-based
reasoning techniques, it returns a dis-
junctive list of fault scenarios that could
be reached from the current state.
The HSG is a specific instance of a hy-
pothetical-scenario generator that im-
plements an innovative approach for
performing diagnostic reasoning when
data are missing. The special purpose
served by the HSG is to (1) look for all
possible ways in which the present state
of the engineering system can be
mapped with respect to a given model
and (2) generate a prioritized set of fu-
ture possible states and the scenarios of
which they are parts. The HSG models a
potential fault scenario as an ordered
disjunctive tree of conjunctive conse-
quences, wherein the ordering is based
upon the likelihood that a particular
conjunctive path will be taken for the
given set of inputs. The computation of
likelihood is based partly on a numerical
ranking of the degree of completeness
of data with respect to satisfaction of the
antecedent conditions of prognostic
rules. The results from the HSG are then
used by a model-based artificial-intelli-
gence subsystem to predict realistic sce-
narios and states.
To avoid the need to create special
models to generate hypothetical scenar-
ios, the HSG uses the same model that is
used to perform fault-detection and
other diagnostic functions but interprets
the results generated by the model in a
manner unique to the generation of hy-
pothetical scenarios. An important addi-
tional advantage of this approach is that
a future state can be diagnosed by the
same model as that used to diagnose the
current state.
This work was done by Mark James of Cal-
tech for NASA’s Jet Propulsion Laboratory.
Further information is contained in a TSP
(see page 1).
The software used in this innovation is
available for commercial licensing. Please
contact Karina Edmonds of the California
Institute of Technology at (626) 395-2322.
Refer to NPO-42516.
Hypothetical Scenario Generator for Fault-Tolerant Diagnosis
This is a means of performing diagnostic reasoning when data are missing.
NASA’s Jet Propulsion Laboratory, Pasadena, California


Algorithm Would Enable Robots to Solve Problems Creatively

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