Welcome to my home-page
reasons, the motivated autonomous robot (a household as well as an
industry robot) must be able to learn and to adapt to changed
surrounding. I assume that the robot has some known Programs for
building visual and acoustic (may be also odor) patterns.
The robot must
have additionally the following adaptation abilities in order to handle
intelligently also in a changing surrounding:
i. recognize new features of an object,
ii. build the object model for a new object and insert it
into its knowledge base,
iii. build situation models for new important situations,
iv. adapt activity schemas for operating a new machine or device,
may be in new rooms,
v. build new activity schemas.
1. Learning new features of a known object
can recognize autonomously visual and acoustic (and odor) changes of an
object, for which it has an object model, and build new visual and
acoustic (odor, respectively) patterns using said programs for building
patterns, and insert these new patterns in the model of this object (in
the tree like knowledge base, see "1. Abstract object and situation
models ...."in "Intelligence
of the motivated...."). The robot can also recognize some habits or
states of an object and insert them into the object model; examples of
such features: "person x get up between 6:30 and 6:45 a.m.", person x
has breakfast at 7:15 o` clock", "device g1 does not work", "no water
is coming in the wash-machine when it is switched on", person x
is not in the flat from 7:50 a.m. to 5:00 p.m. from Monday to Friday".
However, many new
features of an object must be put into the robot by an authorized
person (the robot inserts then these features into the object model);
examples: "person y is ill", "child k1 is 11 years old", "children
younger than 11 years must not take matches and lighter into hands".
2. Recognizing/learning an unknown object
models in the knowledge base are stored in a tree like graph as its
nodes; near the top of the tree are very abstract object models (e. g.
for a machine, a container, a plant); near the bottom are concrete or
less abstract object models (s. "1. Abstract object and situation
models...." in "Intelligence
of the motivated...."). An object model has all features of the
model. When the robot notices an object, On, (visual or by means of
other sensors), it searches top down in the tree for the smallest
class, M(Ok), of objects to which object On belongs (i.e. finds the
object model M(Ok) for which On is an instance, and no successor of
M(Ok) is a model for On). In the case, if there is no concrete object
model for On (i.e. M(Ok) has successors) then the robot considers On as
a new (unknown) object and it builds
a) an object model, Ms(On), for the object On without help, or
b) object model, M(On), by means of a dialog with an authorized person.
In the case (a), the
robot builds the model Ms(On) on the basis of the model M(Ok); it
enters in Ms(On) only these features of the object On which it has
recognized. The model Ms(On) is inserted in the tree as a successor of
the model/node M(Ok). The model Ms(On) has all features of the object
models along the path from M(Ok) to the top; so the robot learns
additional features of the object On.
In the case (b), the robot
builds the model MOn) for the object On in a dialog with an authorized
person, Pz. First, Pz must accept that M(On) is an instance of M(Ok)
(the robot could also recognize that object On is an instance of an
other model, M(Or), too) and should be inserted as a successor of
M(Ok). If Pz does not accept it, the robot propose an other object
model, M(Od), for which On may be an instance. The person Pz may build
also a new abstract model, M(Oa), as a successor of M(Ok) (M(Od),
respectively) in a dialog with the robot, and then insert the model
M(On) as a successor of M(Oa). On the basis of the model M(Ok) (M(Oa),
respectively), the robot asks about features of the object On, and the
person Pz replies. In this way is build the object model M(On).
3. Learning new situations and activities
When the surrounding of the robot changes (e.g. the robot moves flat or
factory building, it should operate a new machine), new situations
appear to which the robot must react (i.e. perform activities). Thus,
it must adapt some situation models and activity schemas to the new
situations and activities. This adaptation (the learning of a new
activity) is carried out as follows.
Assume, the robot should operate a
new machine, Mn. It searches for and finds a machine, Ma, similar to
the machine Mn and which it can operate (i.e. has appropriate activity
schemas); the object model for the machine Mn has been built. The robot
adapts an activity schema, AMa, for operating Ma to activity
schema for operating the machine Mn by means of a dialog with an
authorized person; it asks and does the following:
i) with regard to a situation model in the activity AMa,
- which object models in a situation model (in the activity
schema AMa) must be replaced by new ones?
- at which distances are the objects in the new situation model
(location of objects)?
- inserts the recognized moving characteristics (position, moving
line, direction and speed) of objects in the new situation
ii) with regard to a sub-activity (of the activity schema AMa)
connected with an directed arc (SM1,SM2) (a sub-activity
contains a sequence of elementary activities and
- whether the sub-activity should be accepted, removed or
- in the last case, the robot asks, for each elementary
activity/operation, whether it should be accepted, removed, replaced
- in the last two cases, the person replies giving the elementary
activity which should be done at this place.
After the new activity schema has
been adapted to the new Machine Mn, it must be applied experimentally
to the machine Mn by the person and possibly carry on the adaptation.
In this way the robot can also build/learn new
activities with the help of an authorized person.
4. Should other learning methods be applied?
Some readers may think,
the robot learns not enough automatically - the new knowledge and
activities are put in the robot mainly by a human. I think the outlined
method for learning the robot are right for a robot which does not work
on the Moon or 1000 m under water, because even intelligent pupils and
students learn in a similar way - the knowledge and new abilities are
put into the brains of pupils/students by specialists (directly as
teachers or indirectly by means of books) with the active participation
of these pupils/students; only the recording of knowledge in the
considered (in (1), (2) and (3)) robot goes faster than into a human.
Are "robot schools" a
Also the developmental robots of J. Weng and Y. Zhang (in USA) and E.
Prassler (in Germany) learn only to some degree autonomously, because this learning is partially
supervised by a person; - also children do not learn automatically but
help of adults. The results of learning robots of Weng and Zhang, and
the expected learning results (in the project XPERO) of a robot of Prassler, are modest.
No better results are possible, as long as the
authors of such projects do not build in the foundation stones of intelligence (or intelligence algorithms) in a
robot ( a dog can learn only what its primitive intelligence algorithms
allow).Some foundation stones
of intelligence are build in the considered (in (1), (2) and (3))
motivated autonomous robot (see Intelligence of the motivated....). By
means of these intelligence properties, the robot can (as outlined
above) not only classify/identify unknown objects and build its models,
but also build new complex activities with the help of a person.
of view, the developmental robot approach is interesting, because, may
be very effective developmental algorithms could be created and
implemented in robots, so that they could explore/recognize situations
and do complex activities in its surrounding.
From practical point
of view, it is not controllable what an developmental robot learns,
what it does not learn and to what activities it is able; such robots
are not reliable, they even could behave aggressively towards humans;
of course, not in the next 5 years. However, when in the next 40 years,
as I said, very effective developmental algorithms (comprising all
important foundation stones of intelligence) would be
implemented in robots, then they could be dangerous for humans (but not
so dangerous as Kalashnikovs).
of reliable and human friendly robots ought to be on the alert. Copying
developmental biology and developmental psychology theories is not the
way to develop reliable robots.
humans need reliable, human friendly robots-servants - and the
motivated autonomous robot, which learns as said above, is such a one.
Modified: November 29, 2006