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The build in intelligence of the motivated autonomous robot
There are many papers about robot intelligence. Some ideas of
making robot intelligent are rather naive. I consider intelligence of
the motivated autonomous robot in other way - as outlined below, I
build in some foundation
stones of intelligence in the robot.
The following components of the motivated autonomous robot
are the basis for its intelligent behavior:
* a tree like structured knowledge base of object and
* emotional patterns associated to said object and
* activity schemas which enable the robot to perform
* the motivation of the robot to execute an activity,
* the perceiving sub-system which focuses its attention on
the most important objects and situations (at present).
Abstract object and situation models in the knowledge base
and its intelligent updating
The structure of the knowledge base is such that each object or
situation model is a node in a tree like graph. The successors of an
object node are more concrete object models than the predecessor model;
they are instantiations of the predecessor object model; e.g. the
successor of the object node "apple" are models of different kinds of
apples, whereas the "apple" node (it is the model of the class of
apples) is a successor of the node/model "fruit" (it is the model of
the class of fruits). An object model has all properties of its
predecessor model/node. this enables to have very abstract object
models in the knowledge base. Abstract object and situation models are
a foundation stone of intelligence, because, when the robot perceives
an object which it has not seen, it can recognize/identify this object
as belonging to a class of objects i.e. as being a case of an abstract
object model - thus, in this way intelligent identify before not known
in the case of a new object, which model is not in the knowledge base,
the robot can build the model of this object (doing additional
activities to identify more features of this object) and insert this
model into the appropriate place in the tree knowledge base; in this
way the robot intelligent enlarge its knowledge about its surrounding.
In the similar way not known situations can be classified (identified
as being a case of an abstract situation model). Thus, when the robot
perceives an unknown dangerous situation then it would probably
recognize it as belonging to a class of dangerous situations - thus,
intelligent recognize the not exactly known current surrounding.
The robot can also verify whether a recognized concrete object has
all/or new properties with regard to its model in the knowledge base
and update this object model -thus, intelligent reacting of changes of
in said way the robot can adapt intelligently its knowledge base to a
new surrounding; e.g. when a household robot changes the flat or a new
furniture are in the flat.
The knowledge base is used also for associative perception of object
and situations, in the following way: when the robot perceives
an object as a visual image, by a program for visual identification,
then it searches, in the knowledge base, for the object model which
visual pattern best matches with the identified image; it searches from
the top along the path where the visual image best matches to the
visual patterns in models connected to the nodes. After it found the
object model having the best matched visual pattern, it associates the
identified image with this object model, thus with all properties of
this model. This is a simple solution of the associative problem when
robot perceives an object, which before was not solved.
Valuation of objects and situations by means of emotional stimulus
To each object and
situation model may be associated a stimulus
pattern with respect to a need of the robot. This enables to calculate
the current stimulus intensity of a perceived object or situation. This
stimulus intensity measures how good (if it is positive) or bad (if it
is negative) and how important (how great is the absolute value) the
perceived object or situation is at present - thus, it can intelligent
valuate objects and situations, which enable the robot to ignore not
important, at present, objects and situations and consider only the
most important ones.
3. Situations, activity schema and the motivation of the robot
Usually in papers, a robot considers its surrounding as an
appearance of objects. This leads to unintelligent behavior; examples:
"if tap (faucet) open then close it", if a book on a floor then take it
and put on the shelf (also when e.g. children use it)".
A robot must consider its
current surrounding as an appearance of situations, otherwise it
cannot handle intelligently. The autonomous robot reacts (i.e. does
activities) only to situations, as outlined below.
An activity schema of the robot is a description of a complex
activity of the robot, e.g. the activity schema for preparing a lunch,
the activity schema for cleaning a room. Such activity schema may have
control sub-activities which enable the robot to operate and control
other machines. An activity schema has initial and goal situations. The
robots starts the execution of an activity from one of its initial
situation, and this execution should lead to one of the goal
situations; if it does not achieve a goal situation then the execution
of this activity failed. Thus, the robot does an activity only if it is
in an initial situation of this activity.
However, usually the robot is in several situations which are initial
situations of some activity schemas. In order to decide which activity
it should execute, at present, it uses a procedure, which calculates
the current motivation to execute an activity. This motivation
procedure is based on said in §2 valuation of
stimulus intensities of some situations (especially the initial, goal
and other end situations) of the activity schema. By means of this
motivation, taking into account the time in which an activity must be
done, the robot determines the activity which it is going to execute -
it does the activity which execution is most important at present. Thus
the robot determines intelligently which activity it currently does.
Such decision problem have only full-autonomous robots; not
full-autonomous robots are not confronted with such problem.
4. Focusing its attention on the most important objects and
When perceiving objects and situations in its current surrounding,
the robot focuses its attention and perception on these objects and
situations which are most important at present, i.e. which have great
negative or positive stimulus intensities (see 2. Valuation of
objects...). This is one of the basis for intelligent behavior of
the robot, because it handles the most important situations and ignores
Robot which observes all situations in its surrounding with equal
attention cannot observe efficiently actually important situations,
which leads to handling an unimportant situation and ignoring an
important one - this is a very unintelligent behavior.
The perceiving sub-system of the robot stores/remembers all objects and
situations in its actually perceived surrounding, so that, e.g. it
knows which objects are in the living room when it has come from it to
the kitchen. Such knowledge is necessary to behave intelligently.
5. Understanding and using a very
We may connect names to object and situation models in the knowledge
base (s. "1.
Abstract...") in different languages, e.g. to the model of an apple
the words "apple" and "Apfel". The meaning of such name/word is the
associated object or situation model. For simple clauses, we could
build abstract situation patterns. The robot would understand such
clauses and could formulate clauses according to said situation
patterns. Thus, the robot could understand and use a very primitive
However, the understanding of and using a language by the robot is very
limited, because (a) the knowledge base contains only models of object
and situations with which it is directly confronted, and (b) we do not
know the rules and language patterns according to which a human builds
sentences - the robot has not such intelligence.
September 24, 2006