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Lakatos outlined a theory of mathematical discovery and justification, which suggests ways in which concepts, conjectures and proofs gradually evolve via interaction between mathematicians. Different mathematicians may have different interpretations of a conjecture, examples or counterexamples of it, and beliefs regarding its value or theoremhood. Through discussion, concepts are refined and conjectures and proofs modified. We hypothesise that: (i) it is possible to computationally represent Lakatos's theory, and (ii) it is useful to do so. In order to test our hypotheses we have developed a computational model of his theory. Our model is a multiagent dialogue system. Each agent has a copy of a pre-existing theory formation system, which can form concepts and make conjectures which empirically hold for the objects of interest supplied. Distributing the objects of interest between agents means that they form different theories, which they communicate to each other. Agents then find counterexamples and use methods identified by Lakatos to suggest modifications to conjectures, concept definitions and proofs. Our main aim is to provide a computational reading of Lakatos's theory, by interpreting it as a series of algorithms and implementing these algorithms as a computer program. This is the first systematic automated realisation of Lakatos's theory. We contribute to the computational philosophy of science by interpreting, clarifying and extending his theory. We also contribute by evaluating his theory, using our model to test hypotheses about it, and evaluating our extended computational theory on the basis of criteria proposed by several theorists. A further contribution is to automated theory formation and automated theorem proving. The process of refining conjectures, proofs and concept definitions requires a flexibility which is inherently useful in fields which handle ill-specified problems, such as theory formation. Similarly, the ability to automatically modify an open conjecture into one which can be proved, is a valuable contribution to automated theorem proving.

Målet med den här datorbaserade filosofiska utredningen inom kognitionsvetenskap är att utforska intentionalitet i kollektiva beteenden hos artificiella svärmar. Två definitioner av intentionalitet utforskades; som representationer hos agenter och som observerbara attribut hos agenter, även kallat intentional stance. För den representativa definitionen användes en modell av kollektiv intentionalitet som integrerar två olika ståndpunkter, singularståndpunkten och pluralståndpunkten av kollektiv intentionalitet. Modellen har fem villkor för intentionalitet enligt SharedPlans. Genom att använda Belief-Desire-Intention-modellen för intelligenta agenter operationaliserades villkoren till möjliga representationer. En implementation av en målinriktad artificiell svärm i NetLogo analyserades genom att studera hur väl den uppfyllde de operationaliserade villkoren. Fyra av fem villkor var uppfyllda. Flera simuleringar med olika hastighet genomfördes även under observation. Dessa visade att processen kunde delas upp i tre faser med olika egenskaper. Den utforskande fasen hade gemensam intentionalitet centrerad till ett fåtal aktiva individer. Beslutsfasen hade individuella intentioner som kunde stå i konflikt med varandra medan gemensamma intentioner strävade mot samma mål. I flyttfasen var de individuella intentionerna att förhålla sig till varandra, vilket fick gruppen att upplevas som en enhet med intentionen att flytta gruppen. Resultaten visade att intentionalitet kan observeras och analyseras hos den här artificiella svärmen. Däremot har svärmen inte kollektiv intentionalitet utifrån båda ståndpunkterna.