Feeling AI (FAI) as a kind of Hybrid AI aimed to fill niche of service provided by robots is discussed. Using Hybrid AI based on symbolic and machine learning techniques combined with "Emotional Shell" and pretrained set of different skills is problematic due to limited computer facilities supporting robotic operating autonomously in real physical world. This article proposes, in contrast to symbolic approach, set up FAI on the cognitive response-making model borrowed from living beings that demonstrate the smart behavior in condition of the lack information using the simplest nervous net facilities. Based on findings of neurobiology and cognitive science about behavior of hydra, bee, and creature with higher nervous organization, the attention mechanism model supporting response-making is shown. Interaction of four cognitive models such as perception, drive, emotion, and attention when cognitive engine processes the knowledge presented by set of independent response prototypes is given. Possibilities of borrowed model formalized on fuzzy Certainty Factor (CF) are discussed on robotic application.
resources, and code pertaining to the skills that robots have already been taught [
In this paper, attention model borrowed from living being as one of function of lower reactive layer of cognitive structure to implement in new Hybrid AI model is proposed. Section 2 will present Section 3 describes three models of attention borrowed from living beings belonging to three levels of development: hydra with simplest nervous net, bee with more complex nervous system, and mammalian with develop mechanism processing data from sensors different modality. In section 4, experiment with robot layer blueprint is discussed. When robot makes decision, the step-by-step procedure using sophisticated attention mechanism which implement the scrutiny of environment to receive lack information for continue its motion is described.
Perception system of FAI blueprint based on cognitive perception model introduced early in [
Cognitive response-making model borrowed from living beings lying on the findings of scientists
in the field of neurobiology and cognitive psychology is underpinning to do this. Hypothetical
neuronal architecture of the mammalian brain where cognitive processes divided over different
layers is proposed in [
Studies of hydra behaviors scraped from different science sources have been summarized by ourselves and presented as structured set of its RPs. We have introduced the behavior elementary unit as a solitary RP (Fig. 1) and presented behavior over time interval as sequence of these units, named compound RP.
The solitary RP has been proposed in the form of triple: Sensory Template (ST), Response Making unit environment situation or inner states, the AT has been defined on a set of epitheliomuscular tissues (actuators) and RM presents function of neuron net connected ST with AT. In our model, the set of all RPs reported by neuroscientists had been presented by solitary RPs and divided onto subsets according to kinds of living being needs. We distinguished at least the next four hydra's needs: self-polyp contraction under to comfort need. Full list of behavior prototypes except above mentioned includes some more solitary reactive layer of FAI as a set independent RPs divided into k+1 subsets have been proposed. The lth subset includes RPs which serve lth need, only. In fact, no any partition into subsets is, it done to show that response-making engine when processing ith RP belonging to lth subset uses state local vectors which are associated with lth need (Fig. 1). But RPs are processed asynchronously and independent one from another: simultaneously gradual and independent, the Certainty Factors (CFs) of all three components of RP using feedback through emotion about degree of need satisfaction are tuned. Attention plays major role to support autonomous response making in conditions of uncertainty situations and incomplete knowledge.
The ST of RP (Fig. 1) is structured set of Knowledge Granules (KGs). It presents knowledge about
what ST is and how this template should be computed. The math foundation of presentation and
processing of KG is CF [
Attention mechanism implements two functions. First one f1attent computes CF of KG (ST) being under attention field focus and sends to response-making KG (Fig. 1) messages cfKGievent(t) when sensor data match to ST. Second function f2attent manages movements of attention field in space of all STs, decides which ST will be next under attention focus and computes strength of fields impact cfatten_field(t) on it.
The purpose of this article is explanation both above attention functions and examine theirs on examples of IM tasks. Before proceeding to the formal attention model, we will focus on examples ch should be borrowed into attention model. Then, we will discuss implementation of these principles into formal models of both attention functions f1attent and f2attent and will demonstrate their using when IM does response-making.
The living beings and IMs can use two kinds of sensors: the first perceives properties of environment
directly on the its body and the second perceives properties located on the distance from body. For
example, thermoreceptors perceive temperature in points of environment distributed over body
surface. Ultrasonic sensor obtains data from remote points of space. In spite of hydra hasn't last kind
of sensors it has capability to receive information from removed points by moves the parts of body,
namely, tentacles. An attention functions f1attent and f2attent in (1) do this as they manage the moving
attention focus over different locations of environment. When attention has been focused on some
of them the abstraction function fabstract in (1) answers to question "Does sensors data in this location
match to any ST?" So, scrutiny need is serviced by attention mechanism and head to find extra
information that satisfy RP of another need. Below in this subsection, condition of appearing of
scrutiny need that launches attention is discussed.
required behavior, the need should have capability to exhibit itself, for example, as some source of
nervous activity of living being. In our model the drive playing this role is presented by KG which
can be in different states from excited to inhibited. Examples of hydra show different types of inner
processes
and high-level causes inhibited drive' states of the feed need. Transition between states is gradual
process. The scrutiny need drive has another mechanism of exciting.
the assumption that hydra, despite their simplest neuro net, has mechanism of emotions. So,
longterm unsuccessful hunting of hungry hydra causes, firstly, non-hunting but scrutiny behavior, and,
Same
conclusion was figured out about another needs. These observations can be explained on the basis
of the mechanism of emotions [
When hydra explores environment, it on regular base repeats compounded RP consists of three steps behavior. At the first step, body elongation to maximum length is realized. Then hydra is nodding. And at third step it slowly extending their tentacles. It's worth noting that direction of nodding at second step at current time is different from been executing at same step previously. Direction of nodding is changed consistently from one implementation of nodding action to the next. This fact can be elucidated on the base of hypothesis that hydra owns mechanism of attention which manages the order of RPs are applied to explore the environment. Apparently, such behavior expands space around hydra where may be prey and therefore increases the likelihood of successful hunting. When scrutiny need actions give positive result (prey has been found and touched by tentacle) then the RPs of feed need begin to carry out. The first RP "Firing into the prey by tentacle's nematocysts" of This success ceases the negative emotion drive is fade. Otherwise, negative emotion is growing what entails increasing activation level of scrutiny need' drive, and, consequently, strength of attention field on next three-steps cycle of scrutiny behavior will increase, too. The elongation, nodding and extending actions will be more impressive and cover more space around hydra what increase the chance of well hunting. In this way, both the scrutiny need RPs and attention mechanism help to find RP. So, have being proposed following set of PRs of scrutiny need (body elongation (Pr1), nodding forward (Pr2), nodding left (Pr3), nodding right (Pr4), nodding back (Pr5)), and extending tentacles (Pr6)), and also two compound RPs (somersaulting (Pr7) and looping (Pr8)). For discussion of major idea, it is enough to consider only four prototypes of nodding in four different directions and two compounded prototypes of moving in space, namely, looping and somersaulting. In Fig. 2, Pr1, and Pr2, and so on are identifiers of relevant prototypes listed above.
In contrast to others needs, RPs (Fig. 1) maintaining the scrutiny need are endosensing type. It means when no one RPs matching to current environment state, consequently no "anything" that can from outside to trigger the action. In this case, it is required something internal process that activates an any action. In hydra this role plays the specific intrinsic processes, for example, concentration of molecules or ions of a certain type or electrical signal of neurons. These intrinsic processes have nature of field which affects neurons of living being. Thus, base concept of our model is attention field which triggers the special internal Sensitive Elements (SE) carrying out function of ST in exosensing RP (Fig. 1). In Fig. 2, SEs of endosensing RPs are stand for by number 1, 6, 9, and 10 inside circles. Two of them with identifiers 9 and 10 depict SEs present generalized knowledge about kinds of nodding (9) and movement (10) types. Other circuses with numbers 2, 3, 4, 5, 7, and 8 stand for KGs of exosensing RP. As well as ST of exosensing RP, a SE represent knowledge only about internal situation that can trigger the RP. However, in contrast to KGs presenting ST, a SE has two particularities. First, it gets excited state when satisfied both following conditions: the SE is under attention focus and internal situation matches to this SE. Second, when SE being under attention focus gets excited state, this shifts attention field such way that focus is located onto another nearest neighbor, as shown in Fig. 3. focus covers the next nearest SE with number 6 (Fig. 3.b).
In Fig. 3.c shown, next case when attention focus shifts further on SE with number 6 after SE 9
begins to match the situation and gets arousal state. Our conceptual model of attention based on data
collected in [
When honey bees searching out way to flower patches, the attention mechanism helps in same way
as well as it did for hydra. In contrast to the hydra, the structure of SEs specifying the
order of actions is not constant, it is created each time by memorizing waggle dance of scout bees
[
The rout from hive to the flower patches divided into two segments. First segment heads bees in direction at an angle 30° in relation to the direction to the sun. This direction the bees have to keep during two flight time intervals. Two sensitive elements SE1 and SE2 of knowledge structure in Fig. 4 are pointed this. After this, the direction of flight is changed so that bees have to move under 60° in relation to the sun during three times intervals. In Fig. 4, scrutiny need is activated by the emotion produced by forager bees need to carry out its mission. When next impetus of intrinsic periodical process excites the inputs of all SEs only SE1 will get excite state because it is under attention focus. The SE1 belong to RP with identifier 1 which action template attention field focus has been shifted from SE 1 to SE 2. After time period the next impetus of intrinsic periodical process excites the SE 2 and shifts attention field on SE 3. The SE 2 belongs to the same RP 1 with same action keeping previous mode of motion. So, both above examples show how attention shapes scrutiny behavior of hydra and bee. 3.4.
Mammals have more developed attention mechanism maintaining dynamic STs. It processes data
from sensors and creates dynamic process characteristics of which match with dynamic. Essentially,
attention builds footprint of ordered events and comperes it with the ST [
One of the possible approaches to present such dynamic ST is as follow. After detecting a spot, the eye fovea bypasses some shape along the black and white boundary. To model this, we pretend that mobile Inner Sensitive Matrix (ISM) with n x m sensors is moving along the spot boundary and produces path data in the form of turning angles and lengths of the path sections Fig. 5.
When detecting the boundary of the contour, the ISM movement is under control of system with
feedback based on data about deviation of center of ISM from boundary. For correction of deviations
from the boundary of the figure, PID control algorithm was used [
During movement the ISM generates sequence of events presenting the changes of motion (angles of turn and distances traveled to the turn). In the example, for the demonstration purposes, the accuracy of approximation of the boundary is adopted 15 degrees. In Fig. 6 and Fig. 7, grey-shaded circles graphically show zero- KGs, representing data from ISM . The identifiers inside circles are indicated by numbers corresponding to the angles of turn (on the left part of figures) and distances (four right KGs). The maximum angles of turn are 180° to the right. The right or left turns in the range is regarded as I KG with identifier 0.0. Turns to the right in the range [+7.5 ,+22.5 ] describes KG 0.15 and so on until 0.180. As ISM control algorithm corrects missteps within [-7.5°, +7.5°], these devi In Fig. 6 and Fig. 7, the SEs of the zero-level are given only for the right turn, for clarity. divided into four granules corresponding to 1, 2, 3, 4 units of distance. In Fig. 6 and Fig. 7 zero-level KGs 0.1, 0.2, 0.3, 0.4 represent knowledge about these . The KGs of upper levels present generalized knowledge about fragments of the path traveled by ISM. For example, at first level KG with the identifier 1.l represents the generalized knowledge about arbitrary path length (doesn't matter which of fourth possible length is). In Fig. 6, three of third level KGs with identifiers 3.l_90, 3.l_120, and 3.l_150 represent the knowledge about fragments of ISM path. For example, sense of KG 3.l_90 is "turns right on 90°are made after moving along straight section of some
In Fig. 6 and Fig. 7, colored circles show SEs which may be under attention field focus and are used by attention mechanism when it activates the state of ST of certain RP. Color arrows between these SEs show direction of movement of attention focus when corresponded SE receives activation of its input from intrinsic process. In contrast to attention models of simplest living being (hydra and bee in Fig. 2 and Fig. 4), the role of signals from unknown intrinsic periodical process plays events generated by KG
In Fig. 6, rectangle figure is represented by only four same KGs 3.l_90 and, bypass the rectangle figure by ISM have been reflecting into bypass by attention' focus of SEs 1-1, 2-1, 3-1, 4-1. Their activated states correspond to activation of ST presenting rectangle figure (rect). Similar, the bypass of right triangle figure with angles of 90°, 30° and 60° is represented by sequence of three SEs 3.l_90, 3.l_120, and 3.l_90 what activates the structure of three SEs 1-2, 2-2, 3-2, shown in green, by attention mechanism. The same reasoning may carry out about equilateral triangle with angles of 60° representing by a structure of three SEs 1-3, 2-3, 3-3, shown in blue. Fig. 7 shows the knowledge example used by attention mechanism when it is necessary to recognize visual figures with different length of straight segments. Two figures a rectangle with side lengths of 1 and 2 units and a rectangular triangle with angles of 90°, 30° and 60° and lengths of sides 1, 2 and 3 units. The structure of the four SEs 1 4, 2 4, 3 4, 4 4, highlighted in purple, describes rectangle. The structure of the four SEs 1 5, 2 5, 3 5, highlighted in orange, describes triangle. At the second level of knowledge structure the SEs, for example, 2.l_90, have sense of two SEs of zero level 0.90 and 0.1. Nothing else this stru Fig. 6.
supported robot appliance of attention mechanism in
FAI
The experiments are devoted to the study the cognitive mechanisms of attention and illustrated on
example of a warehouse robot (co-bot [
Ri IF event(rect) and CF_rout_rect is high and CF_direc_left is high and CF_response_makin_mission is high (2)
When co-bot is loaded by cargo which marked by delivery rout,
accorded to rout, for example, CF_rout_rect ( rout_rect =+1.0, tL = 0, tR = 0), where is certainty, tL is
time interval elapsed since the last change of certainty value; tR is the time interval elapsed since the
receiving of new data. inguistic variables in (2) CF_direc_left is computed on
the base of data from sensors about rotary platform position (where video camera is aimed on). The
rotary platform position is changed by attention mechanism, and else one component of STs in (2),
namely, event(rect) is directly shaped by attention, too. This stands for as soon as attention
mechanism recognizes sign of rectangle on visual picture received from camera it activates state of
SE with name rect. This event launches processing fuzzy rules including this component (2). Model
of processing this type of fuzzy rules is done in [
FAI with base cognitive layer version, as a kind of hybrid AI model, can be implemented on not powerful computer facilities that satisfier to small robotic applications destined autonomously carry out its mission in condition of lacks information. Attention cognitive function plays major role to reduce size of application. Scrutiny of environment under attention management permits replace huge KB about robot reactions in different possible situations without loss of autonomous response making. Presenting in this article the attention cognitive model borrowed from simplest living being is integrated with others cognitive functions such as needs, drives, emotions and perception into response making cognitive engine. In contrast to the symbolic approach when KB have to has prototypes for all situation to shape response, the cognitive engine overcome lack knowledge by simultaneously and independent tuning characteristics of existing set of prototypes using feedback through emotions about degree of need satisfaction. Attention mechanism as component of this process provides both the investigation of environment by changing behavior and improving possibility to match existing templates. Cognitive engine can carry out this job in real time on inner robot hardware facilities because proposed model of computations are small set of arithmetic operations per each prototype.
In the future, it is planned two ways of continue researches. In theory, we will construct the following adaptive layer of FAI by borrowing cognitive models from living being that has simplest brain in contrast to hydra that hasn't it. It will be jellyfish as object under research. In practice, we will expand the list of robot application under confirmation functionality and testing to develop the general recommendation and computer aided software for FAI apps design.