CID (Knowledge, Uncertainty, Data)

This research top­ic cov­ers upstream aspects of the pro­cess­ing of knowl­edge and data. We devel­op for­malisms, mod­els and algo­rithms for manip­u­lat­ing sym­bol­ic knowl­edge and numer­i­cal data, using dif­fer­ent types of log­ic (propo­si­tion­al, descrip­tive, modal, etc.) and statistics.

• Knowl­edge mod­el­ling for cap­i­tal­iz­ing, explain­ing and reasoning
• Man­ag­ing uncer­tain­ty in the pro­cess­ing of weak data
• Pru­dent inference
• Inter­ac­tive learning 
• Learn­ing struc­tured data

This research top­ic seeks to build a bridge between, on the one hand, work on sym­bol­ic knowl­edge rep­re­sen­ta­tion and human-sys­tem inter­ac­tion, and on the oth­er, numer­i­cal meth­ods for deal­ing with uncer­tain­ty and sta­tis­ti­cal learn­ing. The objec­tive is to design sys­tems capa­ble of adapt­ing auto­mat­i­cal­ly and dynam­i­cal­ly both to the humans that use them and to the envi­ron­ment they oper­ate in.