Knowledge Integration with

Keywords: Cognitive Modeling Conceptual Blending

Abstract In a subsequent paper, [Veale and O’Donogue, 1999] present the resulting computational model in more detail, which re- In this paper, we present our most recent work on lies mainly on the metaphor mapping engine, Sapper, to es- the integration of different domains of knowledge tablish a dynamic blend between two domains. This blend, into a single domain. The construction of this new rather than being an independent new domain, becomes a domain, the Blend, is inspired on the theory of Con- unifying set of correspondences of concepts from both do- [ ceptual Blending, of Fauconnier and Turner, Fau- mains, built according to a constructor space. As we can see, ] connier and Turner, 1998 , and is formalized in de- in this work, the blend is embeded in the structure mapping tail. As we will discuss later, a blend of two do- itself, instead of becoming an independent new domain. An- mains won’t consist of their sum or juxtaposition. other interesting work is that of [Leite et al.,2000], which ap- Instead, this new domain will have its own structure plies Dynamic Logic Programming [Alferes et al., 1998] to and semantics, which, on one side, brings problems metaphor based knowledge integration. In this work, the au- of interpretation and validation of emergent con- thors use a metaphor mapping function to obtain correspon- cepts, but on the other side represents a promising dences between the two input domains (the tenor and the ve- space for generation of new ideas and solutions. hicle) and generate a third one, the update, that contains the tenor and the projection of the vehicle such that inconsisten- 1Introduction cies between newly created facts are removed. Therefore, it yields a composition of the tenor and the vehicle, which, al- One particularly interesting feature of human cognition is the ¿ ability to manage with a considerably large amount of knowl- though only super cially, the authors call a blend. Each do- edge, coming from a wide variety of sources, semantically main consists of a logic program and the result will also be distant from each other. Yet, we seem to easily combine ap- a logic program. As the reader will notice, our approach is parently unrelated information and, according to [Guilford, quite compatible to this one, since the general architecture is 1967], this is a fundamental process in Human Creative Cog- similar: given two input domains, generate a third one recur- nition. Computationally, we argue that it would be useful to ring to a mapping function. The main difference lies in the use a multi-domain knowledge base in such a way that infor- blend itself, which in our case allows for co-reference of con- mation from one domain could be transferred and applied in cepts from both input domains at the same time, avoiding the other, different, domain. To be able to do so, it is essential necessity of giving priority of one domain over the other. ¿ to have some sort of unifying process, as suggested by the As far as we know, we present the rst formalization of Conceptual Blending Theory[Fauconnier and Turner, 1998]. this theory from a computational perspective. In section 3, Conceptual Blending (CB) is rapidly emerging as a ma- we describe our Blender in detail and apply it to a well known jor force in Cognitive Science, and is increasingly gathering structure mapping example: the heat-water of Dedre several researchers from a diversity of areas. This theory, Gentner’s paper[Gentner, 1983]. which combines a set of processes and principles applied to We feel our work as part of the continuum of evolution of entities named mental spaces, has brought some new light CB theory and its implementations. In our model, we expect to analogy, metaphor, counterfactual reasoning, natural lan- two input domains, each one de¿ned according to two levels: guage processing and creative cognition[Oakley and Coulson, the Domain Theory and the Domain Instances. A structure 1999]. In this paper, we will focus on some practical aspects mapping function is applied to the Domain Theory, which that are important to our contribution, which is essentially will then be the linking basis that allows the construction of centred on creative cognition and modeling. We can ¿nd pre- the blend. This will lead to the creation of a new domain, the vious valuable work on this topic. [Veale, 1997], for example, Blend, which will have its own Domain Theory and Domain presents his model, Pastiche, of CB and applies it to cinematic Instances, as we will discuss in section 4. borrowing. He exploits the domain of Hollywood ¿lm indus- In the next section, we will give an overview of the CB try, which is full of blending examples, to elucidate the re- theory to give the reader some necessary background to un- quirements of a computational model of metaphoric blending. derstand the paper. As we see from our experience, this new computer-virus domain has a life of its own, although we still perceive eas- ily the connections and dependencies to the input domains. Concepts like ”cracking the virus” are so deeply entrenched in this new domain that sentences such as ”the Ebola virus hasn’t been cracked yet” can only be interpreted from in its context. The Blend has emergent structure not provided by the in- puts. This happens in three (unrelated) ways[Fauconnier, 1997]: 1. Composition - Taken together, the projections from the Figure 1: Conceptual Blending of Input 1 with Input 2 inputs make new relations become available that did not exist in the separate inputs 2. Completion - Knowledge of background frames, cogni- 2 Conceptual Blending Theory tive and cultural models, allows the composite structure pro- jected into the blend from the inputs to be viewed as part of a Conceptual Blending was initially proposed by [Fauconnier larger self-contained structure in the blend. The pattern in the and Turner, 1998], and its value has been increasingly ac- blend triggered by the inherited structure is ”completed” into knowledged as a wider range of researchers is becoming in- the larger, emergent structure. terested in studying it. The works of [?][Sweetser and Dan- 3. Elaboration - The structure in the blend can then be cygier, 1999] [Coulson, 1997] and [Veale and O’Donogue, elaborated. This is ”running the blend”. It consists of cog- 1999] are examples of how this theory is an important con- nitive work performed within the blend, according to its own tribution to Linguistics, Creative Cognition, Analogy and emergent logic. Metaphor. To explain it in some detail, we must introduce It is also common to ¿nd blends in artistic . Muss- the concept of Mental Space. According to [Fauconnier and gorsky’s ”pictures of an exhibition”(inMusic)orKandinsky’s Turner, 1998], Mental Spaces are partial structures that pro- ”Improvisations” (in Visual Arts) are two examples of a com- liferate when we think and talk, allowing a ¿ne-grained parti- mon tendency of artists to seek for ideas in different domains. tioning of our discourse and knowledge structures. As we talk Blending is also common in scienti¿c discovery, an interest- or think, our reasoning focus Àows from space to space, trans- ing example of which is the relatively recent AI paradigm of porting and mapping concepts according to points of view, Evolutionary Computation, that results from blending new- presuppositions, beliefs, changes of mood or tense, analogi- Darwinist theories with the problem of function optimization. cal counterfactuals and so on, each giving birth to a different mental space. Blending is generally described as involving two input 3TheBlender mental spaces that, according to a given structure mapping, will generate a third one, called Blend. This new domain We will now present our model for the application of CB the- will maintain partial structure from the input domains and add ory to multi-domain integration. This model has the funda- emergent structure of its own. mental goal of blending two different domains into a new As can be seen in ¿gure 1, a generic space is also consid- third domain. Therefore, we consider a domain as a sub- ered. This can be seen as having a uni¿cation role, such that type of Fauconnier’s mental space and we generate a blend concepts mapped onto each other are considered as belonging through the application of a mapping between two input do- to the same, generic, concept. mains, recurring to an intermediate, mediating domain, the Take, as an example, the notion of computer virus. The generic domain. concept of virus comes from biology and its entry in dictio- Each domain consists of a combination of two kinds of naries generally corresponds to ”Any of various simple sub- knowledge: the Domain Theory and the Domain Instances. microscopic parasites of plants, animals (...) and that consist A Domain Theory is de¿ned through relational and pro- of a core of DNA or RNA surrounded by a protein coat. Un- cedural knowledge. Relational knowledge is represented in able to replicate without a host cell, viruses are typically not the form of a Concept Map that discriminates relationships considered living organisms.” between concepts of the domain. Computer science, a much more recent subject than biol- ogy, brought to life concepts like program, program execu- De¿nition 1 Let O be a Language and OF 5 O be a set of tion, user, terminal, etc. Quite surprisingly (at least initially), symbols (the conceptv). Let OU 5 O be another set of sym- these two domains were mapped onto each other in such a bols (the relations). A Concept Map FP is the tuple (OF, way that a whole new domain had emerged: the computer- OU, OFP), where OFP is a set of literals of the form virus domain. This is a Blend of Computer Science and Vi- [+\> ],, such that [ 5OU, \>] 5OF. rology. In this domain, a submicroscopic parasite piece of code (the virus) uses a coat (a program) to replicate and live, Example 2 Let us follow the example given in [Gentner, possibly causing damaging efects, in a host (the computer). 1983], and de¿ne the domain Gz +the water domain): OFz@ iwater, liquid, pipe, container, vial, beaker, structurally coherent with that association (e.g. ”if a com- pipe, pressure, physical_measure, physical_object, poser is a general, then concert-theatre is battle-theatre or- OUz @ {isa,have,transfer} chestra is army, musician is soldier, etc). Following these OFPz @ {isa(water, liquid), isa(pipe, container), ideas, we designed our metaphor mapping function, accord- isa(vial, container), isa(beaker, container), ing to the properties of injectivity and sistematicity: have(vial, liquid), have(beaker, liquid), transfer(pipe, liquid), isa(pressure, physical_measure), De¿nition 8 Given two concept maps FP4@+OF1, OU1> isa(container, physical_object)} OFP4)andFP5@+OF2, OU2>OFP5), a function *:O $Ois de¿ned as a Metaphor Mapping Function of The procedural part of a domain is a set of rules (in the FP4 on FP5 iff: form of Herbrand clauses) that explicit the inherent causality of the domain.  *+{,@| +, *Ã4+|,@{,i.e.,* is injective ;{ 5OF1,;| 5OF2: *+{,@| @,<{3 5OF1,<|3 5 Example 3 A rule for inferring that something is at the 3 3 3 OF2,{, 5 gaseous state could be: 3 state(X, gaseous):- ebulition_point(X,N), tempera- OFP4 a u5+| >|, 5 OFP5,i.e.,* is systematic A ture(X,T), T N. The ¿rst pair +{> |, may be seeded, i.e., suggested by an Now we can de¿ne what a Domain Theory is. external source, or randomly chosen from the set of pairs of concepts that share the same connection in the generic do- De¿nition 4 A Domain Theory is de¿ned as a pair +FP>U,, main, to which we call list of Candidates. where FP is a Concept Map containing concepts and re- lations relevant to the domain and U is a set of Herbrand De¿nition 9 Having the generic domain Gj de¿ned as clauses (the rules of the domain). +GWj> GLj,> with GWj and GLj de¿ned as above, and two domains D1 and D2, the list of candidates would be: A Domain Instance is an example of a (possibly practical) application of the domain.  Fdqglgdwhv @ i+{> |,={ 5OF1, | 5OF2,<} 5 OF u+{> }, 5 OFP4 ^ OFPj a u+|> }, 5 ¿ OD 5 O g, De nition 5 Let be a set of symbols (the atoms) Let OFP5 ^ OFPj}1 OI 5 O be a set of symbols(the functors) A Domain Instance can be represented in the form L+{4>{5> ===> {q,,whereL 5 Example 10 Suppose we have domain Gk -theheat domain OI,and{l 5OI^OD - with the following concept map OF @ i À k heat, ice, solid, liquid, coffee, metal_bar, Example 6 Consider now the example of the water ow cir- physical_measure, temperature, physical_object} cuit of Gentner’s paper, in which pressure difference between OU @ ilvd> kdyh> wudqvihuj the vial and the beaker originates the Àow of liquid from the k OFPk @ iisa(metal_bar,physical_object), beaker to the vial, through the pipe that connects them[Gen- isa(heat,physical_measure), transfer(metal_bar,heat), tner, 1983]. ,> OD isa(ice,solid), isa(coffee,liquid have(coffee,heat), w={vial, beaker, liquid, pipe}, isa(temperature,physical_measure)j OI w={instance,cause,greater_than, Àow, object, path, source, goal} and a small generic domain: OF @ i In this context, an instance could be: j object, information, physical_state instance(cause(greater_than(pressure(beaker), pres- physical_object, physical_measure} OU @ ilvdj sure(vial)), Àow(goal(vial), object(liquid), path(pipe), j OFP @ source(beaker))) j {isa(physical_measure,information), isa(phy- sical_object, object), isa(physical_state, information)} De¿nition 7 A domain D is the pair +GW> GL,,whereGW is GLj @ Uj @ ij GL a domain theory and is a set of instances. With Dj> Dk and Dz, the set of candidates would be: Now that we described what we mean by a domain, we can {(physical_state, physical_measure), (container, proceed to the central point of this model: the blending pro- metal_bar)} cess. In order to blend two spaces, we must have some kind of Choosing the ¿rst element and applying the de¿nition of correspondence between elements, i.e. there must be a map- Metaphor Mapping Function, a possible mapping would then ping between concepts of one domain onto the other. There be: *+sk|vlfdo_vwdwh,@sk|vlfdo_phdvxuh are already several interesting works around this theme, such *+oltxlg,@khdw [ ] as Tony Veale’s Sapper framework Veale, 1997 or Falken- *+yldo,@lfh [ heiner’s Structure Mapping Engine Brian Falkenhainer and *+frqwdlqhu,@vrolg ] Gentner, 1989 ,sowewon’t explain this subject in much de- *+ehdnhu,@friihh tail. We must refer, however, that our mapping function is *+slsh,@phwdo_edu essentially inspired in Sapper, a model for metaphor interpre- *+suhvvxuh,@whpshudwxuh tation that, given two concept networks (e.g. composer and general) and a metaphor that connects them (e.g. ”A com- poser is a general”), ¿nds a set of correspondences that are 1This is similar to Sapper’s triangulation rule. We redirect the reader to [Veale, 1997], [Veale and Keane, RE @ istate(X, gaseous):- ebulition_point(X,N), 1997] and [Brian Falkenhainer and Gentner, 1989] for a more pressuremtemperature(X,T), TAN.} detailed explanation of structure mapping algorithms. and DIE would end up as: The Blending projection is the main generating function DIE @ iinstance( cause( greater_than( pressure m of a new Blend. It applies the mapping function to concepts temperature(beakermcoffee), on both domains, projecting each concept, rule or domain in- pressure m temperature(vialmice)), Àow( goal(vial m ice), ob- stance to its new existence in the Blend. ject(liquid m heat), path(pipe m metal_bar), source(beaker m coffee)))} De¿nition 11 Given two domains G4 and G5,de¿nedina language O, and a metaphor mapping function *,aBlending In this framework, Composition naturally happens in the Projection  is the function : O $O5> such that: creation of the Blend while projecting relations and concepts. ¿ +{> {,> C| = *+{,@| Compound concepts are, by de nition, results of Composi- +{,@i if m +{> |, <| = *+{,@| tion, along with their projected relations (e.g. the new vial ice ,if have liquidmheat relation is entirely born from composition of To simplify, we represent +{> {, by a single {,and+{> |, by those four concepts). Elaboration or ”running the blend” is {m|= The signal ”m% means co-reference,i.e.,{m| means that the result of the new conclusions and explorations one can { and | both refer to the same concept (say the concept {|). take out of the blend (e.g. the new rule of RE in the previous The operator m is commutative,so{m| +, |m{= example allows for the association of the gaseous state to new concepts). Finally, Completion is achieved through the pro- To the systematic application of the blending projection to jection of those concepts that don’t get associated to others a set of concepts, we call a Blending Translation (when +{,@{, and the integration of the generic space. De¿nition 12 Given a Blending projection ,aBlending In the next section, we will examine this new domain in Translation  is the aplication of  to a literal, such that greater detail. 3 3 3 3 3 3 +O+{4>{5> ===> {q,, @ O +{4>{5> ===> {q,=O @ +O,>{l @ +{l, 4 The New Domain Finally, we can de¿ne what a Blend is. The new concepts, As explained before, the blend is an emergent domain that re- resulting from the blending projection, will ¿nd themselves sults from the integration of two input domains. It has struc- immersedinawebthatisamixedprojectionofbothinput ture and semantics different from the inputs. domains. In terms of their semantics, we can say concepts When the majority of the concepts of the input spaces are will have a new meaning, since new relations that did not translated unchanged to the blend, i.e., +{,@{, then the exist before can appear. structure and semantics of the blend will not be much more than the sum of its inputs’. On the other side, if there is a large ¿ E De nition 13 A Blend is the tuple number of mapping correspondences (which implies a large +G4>G5>Gj>*>>GE, G4 G5 Gj ,where , and are number of +{,@{m|), then the structure and semantics of *  domains, is a metaphor mapping function and is a the blend will be an approximation of a uni¿cation of the two GE Blending Translation operator. The domain is known as input domains. In the limit, if there is total correspondence ¿ the Blend Domain. It is de ned as follows: (i.e., ;{ 5 FP4 <| 5 FP5=*+{,@|), which means  GE @+GWE>GLE,,whereGWE @+FPE>UE, ^ that the two domains have the same structure, each concept GWj will have double semantics, (possibly) obvious in the eye of À  FP @ iF = F @ +F, a F 5 FP4 ^ FP5j a human (e.g. the domains of electricity and uid mechan- E E E ics). Although hard to measure, we assume the quality of the  UE @ iKE:-OE4>OE5> ===> OEq = K:-O4>O5> ===> Oq 5 blend to be a combination of its plausibility (do the new con- U4 ^ U5 a KE @ +K, a OEl @ +Ol,j cepts make sense?) and of its novelty (does the domain bring  GLE @ iLE+{E4>{E5> ===> {Eq,=LE+{4>{5> ===> {q, 5 anything new?). We believe there must be an equilibrium be- UL4 ^ UL5 a LE @ +L, a {El @ +{l,j^GLj tween these two properties to get high quality blends, which should have structure and semantics that are not just the sum or juxtaposition of the input domains. Example 14 The concept map CME of our heat and water Being dependent of the mapping function, at the begin- domains would therefore be: ning of the process, the quality of the blend is unpredictable, OFE@ ioiquidmheat, vialmice, containermsolid, beakerm since there are normally several possible structure mappings. coffee, pipemmetal_bar, physical_measure, pressurem Moreover, the dimensions of the concept maps can make temperature,physical_object, waterj^OFj this task computationally unfeasible. User chosen mappings OFPE @ {isa(pipemmetal_bar,physical_object), would be too demanding, especially in domains with con- isa(liquidmheat,physical_measure), isa(vialmice,con- siderably large concept maps. To cope with this problem, tainermsolid)> isa(beakermcoffee, liquidmheat), a seeding method can be used, in which one or more corre- have(vialmice,liquidmheat), have(beakermcoffee, liquidm spondences are initially stated, such that the mapping func- heat), isa(pressuremtemperature, physical_measure), tion has a basis to start with. This reduces the number of transfer(pipemmetal_bar,liquidmheat)} ^ OFPj possible mappings and allows for some control over the re- The rules would be sults. Another problem is that the concept maps themselves Finally, in which respects to instances, lets suppose we have the following: DI4={instance(v_arts,1,shapes,[shape(square, red,1,0),shape(square,blue,1,1),shape(square, green,1,2)]).} DI5={instance(music,1,chords,[chord(g,major, 1,0),chord(d,minor,1,1),chord(e,major,1,2)]).} Assuming we only had the previously given mapping, we would end up with: DIE={instance(v_arts, 1, shapes, [shape( square, redmmajor,1,0), shape(square,blue,1,1), shape(square, Figure 2: A blend of Music and Visual Arts greenmminor,1,2)]), instance(music,1, chords,[chord( g, redmmajor, 1, 0), chord( d, greenmminor,1,1),chord (e, must be reliable in the sense that, semantically, the closest redmmajor,1,2)]).} they are to the domain they represent, the better it will be for By themselves, these instances wouldn’t be much useful the blend. To deal with this question, we are applying work since only colormchord attributes were changed. But imagine from [Pereira and Cardoso, 2000], who propose an architec- a quite acceptable mapping2 also with correspondences of, ture to help a user build a concept map interactively that ap- say, g to square, d to circle, e to triangle. The ¿rst instance plies inductive learning to apprehend rules as the interaction could be interpreted musically as three g chords, the ¿rst ma- progresses. These rules are then applied to ask questions to jor, the second blue (i.e., sad and cold) and the third minor help ¿ll incomplete parts of the map. the second instance would be composition of a red square, a The new concepts, whatever the mapping determines, will green circle and a red triangle. be explained by the surrounding structure, i.e. by compo- Considering that our model and the Conceptual Blending sition, there are new relations that did not exist before, and Theory itself are designed for generic purposes, domain spe- even those concepts that are projected with no direct transfor- ci¿c validation is naturally not accounted for. Adding spe- mation to the blend are subject to change. ci¿c knowledge for each domain would be inconsistent with Example 15 Suppose the blend of music and visual arts do- the generic principle, and adding a constraint-based language mains, as suggested in ¿gure 2. would add, as far as we know, unwanted complexity to our In this blend, we ¿nd a set of new ”compound” concepts project. In fact, from our point of view, more than the do- (chordmcolor, majormred and minormgreen). In this new do- main, validation is dependent of the purpose and the way we main, to talk about a ”chord” is the same to talk about a want to explore the blend. ”color”. There are also the concepts of ”major color”,which As a consequence of being a novel domain with non- isthesamethingas”red chord”. Up to this point, we can’t validated data, a Blend tends to be fragile in the sense that say that the blend has brought new properties to the concept it is not guaranteed to be consistent or make sense at all. By of ”chord”.Onlysomekindof”different view” on knowledge now, we don’t expect our blends to be auto-suf¿cient or inde- has happened. Yet, concepts like ”blue”, which didn’tgeta pendent. On the opposite, we prefer to apply them as exten- counterpart on the mapping, become interesting and promis- sions to the domains we are working with, thus bringing new ing. Now, we can ¿nd a really new concept, that of ”blue knowledge to the problem at hand. This can be particularly chord”, which even has a clear explanation: ”A blue chord is useful in situations in which current knowledge reveals to be cold and sad”. We can also ¿nd new properties for concepts insuf¿cient, as when a jump to a slightly different knowledge like ”major chord”.Now,a”major chord” is also ”hot”. base is needed. An entire paper will be needed to demon- Now, suppose also that we have rules like: strate the and discuss the results, but we can say generally U4@isurshuw|+[> kdss|,= kdyh_pdq|+[> \ ,> that our Blender behaved with Gentner’s [Gentner, 1983] ex- lvd+\>pdmru,= . ample as described in section 2 (the new domain is physi- tprgdo+[,= kdyh+[> F,>lvd+F> pdmru,j cally acceptable, due to the fact that the input domains are  These two become translated as analogous in that sense) blending Music and Visual Arts do- UE @ isurshuw|+[> kdss|,= kdyh_pdq|+[> \ ,> mains yielded interesting ideas (such as that of ”blue chord” lvd+\>pdmrumuhg,= described in section 3) and in absolutely divergent domains, tprgdo+[,= kdyh+[> F,>lvd+F> pdmrumuhg,=j like Java Programming and Poetry, it tends to bring nonsense Which allows for two new conclusions in the blend, and quite easily: these are that something is happy if it has many (whatever the attached semantics chosen for ”have_many”) major or red Example 16 Blending the domains of Poetry and Java Pro- something and that something that has a major or red some- gramming yielded particularly nonsensical results3.The thing will not be modal. The latter is quite interesting since it introduces the concept of modality to Visual Arts according to a really new point of view: something isn’t modal if it has 2whichhappenedinsomeexperiments redinit. 3This example suffered a grammatical correction mapping: References *+eh,@dvvljqphqw [Alferes et al.,1998] J.J.Alferes,J.A.Leite,L.M.Pereira, *+qrw,@qhjdwlrq H. Przymusinska, and T. C. Przymusinski. Dynamic logic *+txhvwlrq,@vfrsh programming. In Procs. of (KR-98). Morgan Kaufmann, applied to Shakespeare’s ”To be or not to be...”, would al- 1998. low this interpretation: [Brian Falkenhainer and Gentner, 1989] Kenneth Forbus To assign or to negate assignment Brian Falkenhainer and Dedre Gentner. The structure- That is the scope mapping engine. Arti¿cial Intelligence, 41:1–63, 1989. As we’ve been verifying, an acceptable perspective on ex- [Coulson, 1997] Seana Coulson. Semantic Leaps: The Roleo ploring the blend is to assume that useful and unuseful knowl- of Frame-Shifting and Conceptual Blending in Mapping edge are intermixed and it is hard to separate them clearly. Construction. PhD thesis, UC San Diego, 1997. For this kind of data, we need methods with some noise tol- [Fauconnier and Turner, 1998] Gilles Fauconnier and Mark erance. As far as our research goes, Genetic Programming Turner. Conceptual integration networks. Cognitive Sci- and Case-Based Reasoning seem two reasonable techniques ence, 22(2):133–187, 1998. in which these kinds of domains can be useful and that guar- antee some tolerance to undesirable data in the new domain. [Fauconnier, 1997] Gilles Fauconnier. Mappings in Thought In GP, one can use the Blend as a basis for initial populations and Language. Cambridge University Press, 1997. or as a source for mutation operators. In CBR, the path we are [Gentner, 1983] Dedre Gentner. Structure mapping: A currently following is to use the Blend as an extension of the theoretical framework for analogy. Cognitive Science, case-base. This seems quite promising for domains in which 7(2):155–170, 1983. creativity is recognised as being fundamental, as in Music and [Guilford, 1967] J.P. Guilford. The Nature of Human Intelli- Visual Arts (in the previous section, we showed an example gence. McGraw-Hill, New York, 1967. of the way we are approaching this task). In fact, we believe CB to be an interesting source for studying Computational [Leite et al., 2000] J.A.Leite,F.C.Pereira,A.Cardoso,and Creativity, a view also supported by[Veale, 1997][Mandelblit, L. M. Pereira. Metaphorical mapping consistency via dy- 1997][Fauconnier, 1997] namic logic programming. In Procs. of AISB’00.AISB, 2000. [Mandelblit, 1997] Nili Mandelblit. Gramatical Blending: 5Conclusions Creative and Schematic Aspects in Sentence Processing This paper presented our work on multi-domain integration and Translation. PhD thesis, UC San Diego, 1997. based on the theory of Conceptual Blending of Fauconnier [Oakley and Coulson, 1999] Todd Oakley and Seana Coul- and Turner. As far as we know, our view and, most specif- son. Conceptual blending: Representations, principles, ically, our approach to its implementation is quite different and processes (theme session content description), 1999. from previous works on this subject. While it has been com- Available at http://bamse.ling.su.se/ cogling99/prop- mon to concentrate on the structure mapping function, which todd.html. in other models (e.g., [Veale and O’Donogue, 1999])embeds the whole newly created blend, we assume it as a, yet deter- [Pereira and Cardoso, 2000] Francisco C. Pereira and Amil- minant, part of the process, but logically separate from the car Cardoso. A module for automatic learning of concept blend. In our model, which expects two input domains, the maps. In Proceedings of Diagrams 2000. Springer, 2000. blend becomes a new third domain, with a new structure and [Sweetser and Dancygier, 1999] Eve Sweetser and Barbara semantics. As we’ve discussed, this new space is potentially Dancygier. Semantic overlap and space-blending. In full of new concepts and relations, although with a quality Procs. of Sixth International Cognitive Linguistics Con- hard to measure. We intend to make further developments at ference, 1999. this point, mainly integrating with [Leite et al., 2000]’s work [Veale and Keane, 1997] Tony Veale and Mark Keane. The in order to minimize inconsistency. competence of sub-optimal structure mapping on hard We presented what we believe to be a ¿rst formalization of . In Proceedings the International Joint Confer- a CB model, which, although certainly not complete in terms ence on Arti¿cial Intelligence (IJCAI’97). IJCAI, 1997. of the whole theory, may give some clari¿cation around this subject and stimulate further developments/corrections. [Veale and O’Donogue, 1999] Tony Veale and Diarmuid From the Creative Cognition point of view, we think CB O’Donogue. Computational models of conceptual integra- has an important role in the development of computational tion. In Procs. of Sixth International Cognitive Linguistics models of creativity. One of our main research subjects, we Conference, 1999. consider the development of a system capable of creating new [Veale, 1997] Tony Veale. Creativity as pastiche: A compu- ideas would certainly be an important advance both for AI re- tational treatment of metaphoric blends, with special ref- search and Cognitive Science. In this paper, we’ve sketched erence to cinematic ”borrowing”.InProcs. of Mind II: a method for generating extensions to knowledge bases and Computational Models of Creative Cognition, 1997. suggested its use with well-known paradigms, such as Ge- netic Programming or Case-Based Reasoning.