Machine Understanding - a new area of research aimed at building thinking/understanding machines

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1 achne Understandng - a new area of research amed at buldng thnkng/understandng machnes Zbgnew Les and agdalena Les St. Queen Jadwga Research Insttute of Understandng, elbourne, Australa sqru@outlook.com Abstract In ths paper machne understandng, that s referrng to a new area of research the am of whch s to nvestgate the possblty of buldng a machne wth the ablty to thnk and understand, s presented. achne Understandng, the term ntroduced by the authors to denote understandng by a machne, s the frst attempt to establsh the scentfc method to nvestgate the complexty of understandng problem, and s based on the results of phlosophcal nvestgatons and assumptons of the logcal postvsts. achne Understandng, defned n the context of both human understandng and exstng systems that can be regarded as the smplest understandng systems, s based on the development of the shape understandng system (SUS) and on the assumpton that the results of understandng by the machne (SUS) can be evaluated accordng to the rules appled for evaluaton of human understandng. achne understandng refers to the categorcal structure of learned knowledge and one of the most complex problems that s solved wthn ths framework s understandng of vsual obects (vsual understandng). In ths paper only some aspects of vsual understandng, as examples of understandng process, are presented. The frst stage of vsual understandng nvolves perceptual reasonng that conssts of the perceptual categorcal reasonng and vsual reasonng. The vsual reasonng conssts of assgned reasonng that assgns the perceved obect to one of the shape categores. The assgned reasonng conssts of the consecutve stages of reasonng where at each stage of reasonng the specfc data are acqured based on the results of the reasonng at prevous stages. Keywords machne understandng; vsual understandng; vsual thnkng; perceptual reasonng; assgned reasonng; mage understandng, I. INTRODUCTION Ths Wener s book ] that lad the theoretcal foundaton for servomechansms, analog computng, artfcal ntellgence and neuroscence explots an old paradgm where trust n mathematcal modelng s the bass for development of the scentfc approach n explanng natural phenomena and desgnng complex machnes. However, there are very complex problems such as understandng or thnkng, where ths approach cannot be appled and for ths reason the new area of research the am of whch s to nvestgate the possblty of buldng a machne wth the ablty to thnk and understand was proposed by authors 11], 1], 13]. achne Understandng s defned n the context of both human understandng and exstng systems that can be regarded as the smplest understandng systems. The problems related to human understandng were dscussed n a more detal n 11], 1], 13] and n ths paper only man ponts of these problems are presented and most of references can be accessed from our books. Understandng, the result of thnkng, nvolves processes such as learnng, problem solvng, percepton, and reasonng, and requres abltes such as ntellgence. There s no unque defnton of human understandng and comparson of human understandng and machne understandng s based on the results of phlosophcal nvestgatons, and not on the results of scentfc research. Some problems related to human understandng are topcs of research n the area of psychology, lngustcs, cogntve scence or artfcal ntellgence, however there are also problems that are not subected to scentfc methodology (emprcal research). Human understandng was dfferently defned durng the long perod of phlosophcal nqures. One vew s that perceved obect and dea are a key to understand human understandng (Plato, Arstotle, Lock, Berkeley, Lebntz or Kant (see e.g. 5], 14], 6], 1], 10], 9]). For Plato 19] understandng s graspng of deas and the dea refers to partcular thngs n the emprcal world that are mperfect reflectons of that dea. For Arstotle understandng s connected wth percepton were deas (concepts) are extracted from perceved data based on the abstracton and generalzaton. For Locke 14] understandng s graspng of the relatons between deas and for Kant 9] understandng begns by means of obects whch affect our senses, produce representatons, rouse our powers of understandng nto actvty (to compare, to connect, to separate) and to convert the raw materal of our sensuous mpressons nto the knowledge of obects (deas). For Husserl 16] meanng of the obect s a key for understandng. Husserl, when stll absorbed wth an obect, ponted to the meanng of the obect as ts essental cogntve ngredent. He ntroduced dstncton between natural and phenomenologcal modes of understandng. Natural understandng s based on the percepton that consttutes the known realty whereas phenomenologcal understandng s based on phenomenologcal reducton that s based on conscousness of any gven obect that dscerns ts meanng as an ntentonal obect. For Frege, Wttgensten and Russell language s a key for understandng and formal language and mathematcal modelng were an mportant components of understandng. For Russell understandng s connected wth searchng for an deal language for representng the scentfc facts and Wttgensten 3] developed a comprehensve system of logcal atomsm as a formal language of scence. For analytc phlosophy (logcal postvsm) 16] understandng s based on logcal clarfcaton of thoughts by analyss of the logcal form of phlosophcal propostons and usng formal logcal methods to develop an emprcst account of ISSN: X 13 Volume, 017

2 knowledge. Logcal postvsts adopted the verfcaton prncple accordng to whch every meanngful statement s ether analytc or can be verfed by experment, and reected many tradtonal problems of phlosophy as meanngless. For hermeneutcs phlosophers (Schleermacher, Gadamer, Hedegger) nterpretaton of the text s a key to understandng. Hermeneutcs, as the art of understandng the wrtten dscourse of another person correctly, was ntally appled to the nterpretaton of scrpture and emerged as a theory of human understandng through the work of Schleermacher and Dlthey. For Schleermacher understandng of the text s to fnd the author's ntentons whereas for Gadamer 4] the context of nterpretaton determnes a text's meanng and reveals somethng about the socal context n whch texts were formed. For phlosophers such as Hobbes or Spnoza bran and ts functonng s a key for understandng. They beleved that humans are determnstc machnes wth understandng explanable by scentfc methods. odern phlosophers (logcal behavorsm or functonalsm) regarded the problem of understandng as the problem of mnd functons. Functonalsm dentfes mental states wth bran states and explans understandng n terms of cogntve theory that tred to explan human understandng by comparng the mnd to a sophstcated computer system. achne understandng s defned n the context of both human understandng and exstng systems that can be regarded as the smplest understandng systems. Smple understandng systems are bult n the areas of expert systems, mage understandng, language understandng, or robotcs. Expert systems 7] are computer systems that emulate the decsonmakng ablty of a human expert and are the frst computer systems that solve problems that requre understandng of the selected fragments of knowledge. The term mage understandng ], 17], 1] refers to a computatonal nformaton processng approach to mage nterpretaton and knowledge-based nterpretatons of vsual scenes that transform pctoral nputs nto commonly understood descrptons or symbols. Image understandng systems, bult n order to nterpret the perceved obect or nterpret an mage, are based on the research n the area of computer vson and mage understandng. Language understandng s an area of research that deals wth understandng of a text as the product of the lngustc actvty of the mnd. The natural language understandng systems 0], 18], 15], 8], 3] usually consst of the subsystems that perform the specfc tasks such as lexcal analyss, syntactc analyss, semantc analyss, dscourse analyss or pragmatc analyss. Neural Networks 4], a set of smple computatonal unts (nodes, neurons) that are hghly nterconnected, whch attempts to model the capabltes of the human bran, can be also regarded as the smplest understandng systems. The most popular neural network n pattern recognton s the feed forward multlayered network, wth the back propagaton algorthm as the tranng method. II. ACHINE UNDERSTANDING achne understandng refers to the new area of research the am of whch s to nvestgate the possblty of buldng a machne wth the ablty to thnk and understand. The term machne understandng, ntroduced by the authors 13], denotes the process of understandng by the machne SUS (Shape Understandng System). A machne, n order to be able to understand, needs to mtate the way n whch humans understand the world and language (text). SUS as the machne that s desgned to have an ablty to thnk and understand needs to learn both knowledge and sklls. Learnng knowledge and sklls, whch supples materal for thought that leads to understandng, s called knowledge mplementaton (see 1] for descrpton). achne understandng stresses the dependence of learnng and understandng processes and s based on the assumpton that the results of understandng by the machne (SUS) can be evaluated accordng to the rules appled for evaluaton of human understandng. It s assumed that to understand means to be able to solve a problem and to gve the relevant explanatons. achne understandng refers also to the categorcal structure of learned knowledge. The shape categores, presented n 11] and 1], are bass for the ntutve graspng of the sense of perceved obects whereas the basc abstract categores, descrbed n 13], are appled durng abstracton n the problem solvng when the perceved obect s assgned (transformed) nto the vsual general abstract categores such as the crcle category or the rectangle category, and nto the basc abstract categores such as the obect category or the movement category. achne understandng refers to dfferent ontologcal categores of obects: a vsual obect, a real world obect, a sgn, a sensory obect, or a text obect, descrbed n 1], 13]. The sensory obect category s a specal category derved from the category of vsual obects. A sensory obect s the obect that s named based on a set of measurements that refer to the attrbutes of the category to whch the obect s assgned e.g. the category of mneral obects. The text category s referrng to any form of the text and s dvded nto four dfferent specfc categores: the text-query category, the text-task category, the dctonary-text category, and the long-text category (see 1], 13] for descrpton). achne understandng, followng the way of scentfc understandng, s based on the basc abstract categores such as the set category, the element category, or the belongng category that are defned n the area of set theory based on adopted axoms, as descrbed n 13]. The basc abstract categores are represented as the obects on the SUS normalzed perceptual vsual feld (the rectangle on whch all perceved obects are proected). These vsual representatons that refer to the SUS ntuton can be utlzed durng explanatory process and make t possble to found understandng on the strong ntutve bass. achne understandng s based on the assumpton that the results of understandng by a machne can be evaluated and compared to the results of human understandng. If understandng s defned as the ablty to solve problems, then assumng that problems (tasks) are well defned, the understandng (ablty to understand) can be tested by testng whether these problems can be solved by the machne (SUS). In ths context machne understandng can be regarded as problem solvng, however t s assumed that the machne to be able to understand needs also the ablty to explan how to solve a problem. The most mportant part of evaluaton of the machne s (SUS) ablty to understand s to formulate the problems and to use these problems to test f the machne (SUS) s able to solve those problems. Examples of problems ISSN: X 133 Volume, 017

3 that are solved durng machne understandng (problem solvng) such as the namng, solvng the vsual problems (perceptual problems, vsual analogy problems or spatal problems), the problems of the sgns nterpretaton, the problems of text nterpretaton and explanatory problems, are descrbed n 13]. achne understandng s regarded as the problem solvng and explanaton can be also regarded as solvng the problem of explanng known facts, perceved obects, solved tasks or nterpreted texts. The specal class of problems used for testng the results of learnng at school (texttasks), s descrbed n 11], 1], 13]. However, n order to test f the text presented to SUS s understood there s a need to formulate the specal text-tasks n the form of questons, computng problems or explanatory problems. For example, n order to test the degree of understandng of the mneralogcal dctonary text SUS can be asked the questons what s the name of the mneral that s represented by chemcal formula ZnO? or explan why malachte s green? III. VISUAL UNDERSTANDING IN ACHINE UNDERSTANDING achne understandng refers to the categorcal structure of learned knowledge and one of the most complex problems that s solved wthn ths framework s understandng of a vsual obect (vsual understandng) that s based on the development of the Shape Understandng System (SUS). In ths paper some aspects of vsual understandng are presented to show the complex reasonng process. The frst stage of vsual understandng nvolves perceptual reasonng that conssts of perceptual categorcal reasonng and vsual reasonng. The vsual reasonng conssts of the assgned reasonng that assgns the perceved obect to one of the shape categores. In contrast to exstng approaches n AI, where usually reasonng s ndependent of the acqured data needed n reasonng process, assgned reasonng conssts of the consecutve stages of reasonng where at each stage of reasonng the specfc data are acqured based on the results of reasonng at prevous stages. The vsual reasonng usually assgns the name from one of the vsual obect categores to the perceved obect. The vsual obect, after namng, s nterpreted based on knowledge of ontologcal vsual categores and knowledge of the knowledge scheme. Categores of vsual obects are establshed based on the assumpton that a vsual obect exsts and can be perceved by the accessble techncal tools (see 11]). The notaton of basc categorcal knowledge s based on a categorcal chan. The categorcal chan s a seres of categores, derved from the categores of vsual obects or categores of body of knowledge, showng the herarchcal dependence of knowledge. The categorcal chan derved from the categores of vsual obects s gven as follows:... {,,...}, where the categores are O derved from the category of vsual obects. The category at O the frst level of the categorcal chan s called the perceptual category of a vsual obect. The category at the second level of the categorcal chan s called the structural category of the vsual obect. The ontologcal category v begns from the thrd level of the categorcal chan. The symbol denotes movng to the next level of the categorcal chan and {,,...,} denotes dfferent categores at the same level of the categorcal chan. The perceptual categores and structural categores are assocated wth the vsual appearances of obects and are represented by vsual knowledge. The structural element category can represent both the vsual and sensory obects. Knowledge of the specfc category derved from the gven vsual category such as the symbol category s learned by SUS at the prototype level Ontologcal vsual categores have herarchcal structure and at the bottom of each categorcal chan s the prototype category. The prototype s defned durng learnng process at the level for whch the tranng exemplars are avalable. The prototype s represented by all vsual representatves of the specfc category and t s assumed that learned vsual knowledge s coverng the vsual doman prototype. Understandng of a vsual obect (vsual understandng) s a very complex problem and nvolves perceptual reasonng that conssts of perceptual categorcal reasonng and vsual reasonng. The perceptual reasonng s appled durng namng and learnng processes. Hgher level understandng processes nvolve the reasonng that s based on the prevously learned non-vsual knowledge. The vsual reasonng s part of the hgher level vsual reasonng used n the namng process where all learned non-vsual knowledge that s connected wth the category to whch the name s assgned to the obect s accessble. Durng the vsual reasonng (namng) the name of one of the learned vsual categores s assgned to the perceved obect. Namng of the vsual or sensory obects s to solve the problem of fndng the meanng of the obects. When the obect s named ts meanng conssts of all learned knowledge that s lnked to the category to whch the named obect belongs. For example, understandng sgns s to solve the problem of fndng the meanng of sgns or symbols and to solve ths problem the nterpretaton that s based on the learned codng system s utlzed. Smlarly, understandng a text s to solve the problem of fndng the meanng of the text 1], 13]. The perceptual categorcal reasonng, the frst stage of the perceptual reasonng, s related to the SUS perceptual vsual feld where an obect s assgned to one of the perceptual and structural categores. The perceptual category reflects perceptual propertes of the obect, determnes the vsual reasonng process and s dvded nto a slhouette, a lnedrawng, a colour obect, or a shaded obect. The method of assgnng of the obect to one of the perceptual categores, based on the hstogram, depends on a number of peaks n hstogram. The obect s assgned to the lne drawng or slhouette category f the hstogram has one peak, t s assgned to the colour category f hstogram has more than two clearly vsble peaks and t s assgned to the shaded category f hstogram has no clearly vsble peaks. An obect s assgned to the lne drawng category f the obect s assgned to the thn class category. In a smlar way the obect s assgned to one of the structural categores. The structural category refers to the complexty of the vsual representatons of an obect and s dvded nto the element category, the pattern category, the pcture category or the anmaton category. Assgnng to one of the pattern categores s based on computaton of a number of obects. An obect from the pattern category s the obect that s assgned to the slhouette, lne drawng or colour category. An ISSN: X 134 Volume, 017

4 obect from the shaded perceptual category s usually assgned to the pcture category. The element category s the basc structural category that s used durng namng of an obect. The shape categores (classes) are derved based on the vsual attrbutes of the vsual obect that refers to the geometrcal and topologcal propertes of the obect. The shape categores (classes) are descrbed n 11], 1]. An mportant part of vsual understandng s percevng of a 3D obect and nterpretng the obect n terms of the 3D geometrcal fgure or n terms of the real world obect. Understandng of the obect from the real world ontologcal category requres usually nterpretng t as a 3D obect. SUS understands a real world obect as the obects extracted from an mage n the SUS perceptual vsual feld. The real world obect s usually extracted from the obect that s assgned to the pcture category. SUS can only dfferentate between a real world obect and the photograph (pcture) of ths obect by obtanng addtonal sensory nformaton. There s the assumpton of ntentonalty, that means, SUS knows (assumes) that the photograph s the mage obtaned by lookng at the real world obect. The dfferent backgrounds requre applyng the dfferent segmentaton methods to extract the obect from the background. Knowng an obect (name of obect) that we are lookng for makes the searchng for the obect and extractng t more easy task. As t was descrbed, the frst part of the vsual understandng nvolves perceptual reasonng that conssts of perceptual categorcal reasonng and vsual reasonng. The vsual reasonng conssts of the assgned reasonng that assgns the perceved obect to one of the shape categores and s based on the shape understandng method 11]. A member of the shape category s called an archetype. The archetype of the class, s an deal realzaton of the shape (vsual obect) n the two-dmensonal Eucldean space E. An exemplar e E of the class s a bnary realzaton of an archetype n the dscrete space. The exemplar s one of the regons of a bnary mage. The bnary mage s regarded as a set of pxels on the dscrete grd (,). The vsual obect o, that s perceved by SUS, s transformed by the perceptual transformaton : ( o) u nto the phantom u that s the D representaton (e.g. photograph) of the obect o. The phantom u s transformed nto a set of crtcal ponts by the sensory transformaton symbolc descrpton n the form of a strng fnally nto a symbolc name K ( ). : ( u ) and next nto a R, and The assgned reasonng s the most mportant part of the perceptual reasonng. The assgned reasonng conssts of the consecutve stages of reasonng where a perceved obect s at frst transformed nto a set of crtcal ponts and next nto the symbolc name. In order to fulfll the requred task of acqurng the data and processng t n order to obtan a set of descrptors, a processng method s used. The processng method apples an mage transformaton n order to transform the data nto one of the data types. The mage transformaton s the mappng from the one set, called the doman of mappng, nto another one called a set of mappng values. The descrptor transformaton s appled to fnd a set of descrptors used to assgn the perceved obect to one of the possble classes. A reasonng process that s part of a vsual reasonng process s performed passng the consecutve stages of reasonng. Durng each stage of reasonng a sequence of mage transformatons s appled n order to fnd a set of descrptors. The sequence of mage transformatons : that are used n reasonng process can be wrtten as: 0 1 : 1 :,., : or as a composte gven as... :, where 1 1 denotes one of the mage transformatons and denotes the sequental operator. Although t was assumed that a vsual obect s represented by a bnary mage t s not the cause of a serous lmtaton to the presented method. The vsual obect that conssts of parts of dfferent colours s assgned nto one of the colour classes and durng processng stages these parts are nterpreted as the new vsual obects. The assgned reasonng nvolves transformaton of the descrpton of an examned obect s when passng stages N, where s the 0 begnnng stage, N s the fnal stage of the reasonng process and denotes the move to the next stage of reasonng. At each stage of the reasonng the followng operatons are performed: the processng transformaton transforms the set of crtcal ponts 1 :, the descrptor transformaton computes descrptors ( ), an examned obect s s assgned to one of the possble classes T ] s ]. Durng reasonng process, a perceved obect s frst transformed nto a set of crtcal ponts and next nto the symbolc name. The symbolc name s extracted from a symbolc descrpton. The symbolc descrpton s an ntermedate form that has many addtonal specfc data about the perceved phantom. The symbolc descrpton s used to k reason about the specfc categores to whch the obect can belong. For example, the obect O1 s transformed nto a symbolc descrpton n the form of the followng strng"a3] L3 AE ] S79 B100,99,99 A60,61,60 ]]{ L3 O ] S 5 B58,100,57 A9,30,10 ]}{ L3 O ] S5 B57,100,58 A30,9,1 0 ]}{ L3 O ] S53 B100,58,57 A9,10,30 ]}". Next, the symbolc descrpton s transformed nto the symbolc name gven as the strng A3_L3_AE_L3_O_L3_O_L3_O. achne understandng s strctly connected wth learnng of new knowledge. SUS ablty to understand depends on the effectveness of learnng process and learnng of new knowledge depends on the SUS ablty to understand. SUS learnng s called the knowledge mplementaton (see 1]) and s concerned wth two man aspects of human learnng: learnng of the vsual knowledge n the context of the categorcal structure of the learned categores of vsual obects and learnng of the knowledge that s connected wth understandng of the content of the text. Process of learnng conssts of acqurng of the new knowledge and learnng of the new sklls. Durng learnng of the vsual knowledge ISSN: X 135 Volume, 017

5 (knowledge mplementaton) the generalzaton, the specfcaton, the schematzaton and the vsual abstracton s mportant part of the learnng of the vsual knowledge. Understandng the vsual obects from one of the ontologcal categores requres learnng of the vsual concepts of ths category. The ontologcal category s gven by ts name n and s represented by a set of vsual obects called the vsual representatves of the category o) { o, o,..., o }. Vsual ( 1 n knowledge of the category s learned as a vsual concept represented as a set of symbolc names,,..., }. It c { 1 n s assumed that a set v (o) represents all vsual aspects of the category v. Durng learnng of the knowledge of vsual obects, at frst, the representatve sample of obects from the category u v s selected, then for each obect the symbolc name s obtaned and fnally the vsual concept of ths category as a set of symbolc names c ( v ) { 1,,..., n} s learned. For selected category v the vsual concept s obtaned n the followng stages of the learnng For all u u, 1,..., n, u v do: 1. Transform a phantom u nto ts dgtal representaton usng a perceptual transformaton ( u ). o k For each perform reasonng: k 0. Assume 0 ]. 0, At the -th stage ] assume that an examned obect o s assgned to the class ]. k 1 Apply the processng transformaton: :. k 1 Apply the descrptor transformaton: ( ). Apply the rule: T ] o ]. h h 1 If ] s the fnal stage, assume ]. If <n, =+1 goto 1 else END. else =+1, goto. As a result of applyng ths algorthm the vsual concept ( ) { 1,,..., n} s obtaned. An example learnng of the vsual concept of members of the arrow category: the result - a vsual concept { Q 3 ), ( 4, 3 ), (, 3 ), 3 5 (L C L L L }. Arr L Durng understandng of an obect u, the perceved obect u s transformed nto the symbolc name and next a learned set of symbolc names s searched to fnd the symbolc h k h k name of the category that was learned prevously. Understandng process can be represented as: for =0 to K f then n N, where n N s the name of the -th category and a set N { n 1,..., n } s a set of all names of categores to whch the obect can belong, and s a number of names n the set N. After namng, all non-vsual knowledge, that was prevously learned for the category, s now accessble and can be used n the thnkng/understandng process. Understandng of an obect s performed at two levels, the ntermedate level and ontologcal level. At the ntermedate level of understandng the obect s descrbed n terms of the shape classes. The descrpton of the obect at ntermedate level refers to the symbolc name. For example, for the obect, the symbolc name (n SUS notaton) A3_L3_AE_L3_O_L3_O_L3_O conssts of two parts. The frst part A3 gves a general descrpton of the class that means that the obect s the acyclc obect wth three holes. The second part L3_AE_L3_O_L3_O_L3_O gves a specfc descrpton of the obect. The fnal descrpton of the obect, at the ntermedate level of understandng, s gven n the form of the lngustc descrpton: acyclc obect wth three holes. At the ontologcal level, the obect s assgned to one of the ontologcal categores n namng process. Namng not only attaches the name to the perceved obect but also connects the obect wth all knowledge that s relevant to the name of the obect. any names from dfferent categores can be attached to the same obect and namng can be gven at many dfferent ontologcal levels. In order to assgn the obect to the specfc ontologcal category, nformaton ncluded n a symbolc descrpton s used to obtan the addtonal data needed n the reasonng process. For example, an obect can be nterpreted as a symbol eye of dragon when addtonal relaton all three holes are equal s establshed. In the case of the obect O1 the sze of holes s gven n the strng form as S5, S5, S53, as part of the symbolc descrpton. The obect O1 can be also nterpreted as a mathematcal obect (sold pyramd) or as a real world obect (a model of a pyramd). In ths paper only the perceptual reasonng that s the key process of vsual understandng s presented. Vsual understandng, however, s very complex process that nvolves solvng vsual problems such as solvng vsual ntellgence test or vsual dagnoss. The mportant part of the problem solvng s to fnd a sutable form of the problem representaton. The vsual representaton, as one of the forms of the problem representaton, can be used as the problem tself (e.g. namng), as the schematc representaton of the problem (e.g. solvng task wth electrc crcuts), as the magery transformaton (e.g. solvng task plannng robot acton) or as the explanatory process (e.g. explanng a soluton). Vsual understandng, regarded as a problem solvng process, can be descrbed by a sequence of sub-processes and expressed as follows: ( v) u - > (u) -> R -> T R -> ] a, where at frst the problem transformaton ISSN: X 136 Volume, 017

6 ( v) u transforms a gven member of the problem category nto the vsual form (phantom), next the sequence of transformatons R -> T R transforms the nternal representaton gven as a set of crtcal ponts nto the symbolc names (mage transformatons), and at the end, the soluton s obtaned by applyng the vsual nference. Examples of the vsual problem solvng are gven n 11], 1]. Non-vsual problem solvng such as solvng the educatonal tasks or nterpretng the text s presented n 1], 13]. IV. CONCLUSION In ths paper some aspects of vsual understandng, as an mportant part of machne understandng that s referrng to the new area of research the am of whch s to nvestgate the possblty of buldng a machne wth the ablty to thnk and understand, s presented. The result of our research shows that there s possblty to buld the machne wth the ablty to thnk and understand based on the framework descrbed n ths paper and our books. Presented vsual understandng, that nvolves the perceptual categorcal reasonng and vsual reasonng, makes t possble to combne the non-vsual and the vsual knowledge to perform complex hgher level reasonng process. Some aspects of vsual understandng were also presented to show the complexty of the reasonng process. In contrast to exstng approaches n AI, where reasonng s usually ndependent on the acqured data needed n reasonng process, the assgned reasonng conssts of the consecutve stages of reasonng where at each stage of reasonng the specfc data are acqured based on the result of the reasonng of prevous stages. As t was ndcated n ths paper, machne understandng not only nvestgates the possblty of buldng a machne wth the ablty to thnk and understand but also makes t possble to study the selected aspects of understandng and provdes the sutable model of understandng that can be approached usng scentfc methods. However, t s mportant to stress that machne understandng can only to some extend approxmate human understandng and requres very good programmng sklls n C++ and knowledge of algorthms from the deferent domans such as numercal methods, computatonal geometry, graph theory, mage processng, or sgnal processng. 1] Berkeley G (1996) Prncples of Human Knowledge and Three Dalogues. The World's Classcs. Oxford Unversty Press, Oxford ] Bnford T (198) Survey of odel-based Image Analyss Systems. Internatonal Journal of Robotcs Research 1 (1): ] Dale R, osl, H., Somers H (000) Handbook of Natural Language Processng. 4] Gadamar H (1989) Truth and ethod. Crossroad, New York 5] Glson E (1991) The Sprt of edaeval Phlosophy. Unversty of Notre Dame Press, London 6] Hume D (1975) Enqures Concernng Human Understandng and Concernng the Prncples of orals. Oxford Unversty Press, Oxford 7] Jackson P (1998) Introducton To Expert Systems 3edn. Addson Wesley, 8] Jurafsky D, artn JH (008) Speech and Language Processng. edn. Prentce Hall, 9] Kant I (1996) Crtque of Pure Reason. The Everyman Lbrary. Everyman, London 10] Lebntz GW (1996) New Essay on Human Understandng. Cambrdge Texts n the Hstory of Phlosophy. Cambrdge Unversty Press, Glasgow 11] Les Z, Les (008) Shape Understandng System. The frst steps toward the vsual thnkng machnes., vol 86. Studes n Computatonal Intellgence. Sprnger-Verlag, Berln 1] Les Z, Les (013) Shape Understandng System - Knowledge Implementaton and Learnng, vol 45. Studes n Computatonal Intellgence. Sprnger-Verlag, Berln 13] Les Z, Les (015) Shape Understandng System: achne Understandng and Human Understandng, vol 588. Studes n Computatonal Intellgence. Sprnger-Verlag, Berln 14] Locke J (1961) An Essay Concernng Human Understandng. Dent, London 15] annng C, and Schütze, H.; (1999) Foundatons of Statstcal Natural Language Processng. The IT Press, Cambrdge, A 16] artnch AP (001) A Companon to Analytc Phlosophy. In. Blackwell, Oxford, 17] atsuyama T, and Hwang, V. (1990) SIGA: a Knowledge-based Aeral Image Understandng System. Plenum Press, New York 18] tkov R (00) Oxford Handbook of Computatonal Lgustcs. Oxford Unversty Press, Oxford 19] Plato (1993) Republc (trans: Waterfeld R). Oxford Unversty Press, Oxford 0] Radford A (1988) Transformatonal Grammar: An Introducton. Cambrdge Unversty Press, Cambrdge 1] Ullman S, Rchards W (1989) Image Understandng. Ablex Publshng Corporaton, Norwood ] Wener N (1948) Cybernetcs: or Control and Communcaton n the Anmal and the achne. A: Technology Press, Boston 3] Wttgensten L (1961) Tractatus Logco-Phlosophcus (trans: Pears D, cgunness BF). Routledge & Kegan Paul, London 4] Zurada J (199) Introducton to Artfcal Neural Systems. West Publshng Company, St. Paul, nnesota REFERENCES ISSN: X 137 Volume, 017