These are my recent Pin​board​.in links:

• [1201.5604] Dis­crete and Fuzzy Dynam­i­cal Genetic Pro­gram­ming in the XCSF Learn­ing Clas­si­fier System

  “A num­ber of rep­re­sen­ta­tion schemes have been pre­sented for use within Learn­ing Clas­si­fier Sys­tems, rang­ing from binary encod­ings to neural net­works. This paper presents results from an inves­ti­ga­tion into using dis­crete and fuzzy dynam­i­cal sys­tem rep­re­sen­ta­tions within the XCSF Learn­ing Clas­si­fier Sys­tem. In par­tic­u­lar, asyn­chro­nous Ran­dom Boolean Net­works are used to rep­re­sent the tra­di­tional condition-​​action pro­duc­tion sys­tem rules in the dis­crete case and asyn­chro­nous Fuzzy Logic Net­works in the continuous-​​valued case. It is shown pos­si­ble to use self-​​adaptive, open-​​ended evo­lu­tion to design an ensem­ble of such dynam­i­cal sys­tems within XCSF to solve a num­ber of well-​​known test problems.”
  
  Kauffman-​​networks learning-​​classifier-​​systems genetic-​​programming nudge-​​targets inter­est­ing

• [1201.4899] I Like Her more than You: Self-​​determined Communities

  “In this paper we define what we call an affin­ity sys­tem, which is a set of indi­vid­u­als, each with a vec­tor char­ac­ter­iz­ing its pref­er­ence for all other indi­vid­u­als in the set. The pref­er­ence of a mem­ber can be given either by a rank­ing of all mem­bers or by a weighted vec­tor that defines the degrees of its affin­ity to oth­ers. Affin­ity sys­tems are use­ful for mod­el­ing social sys­tems as well as gen­eral data sets, as social inter­ac­tions are often deter­mined by affini­ties among the mem­bers. We also define a nat­ural notion of (poten­tially over­lap­ping) com­mu­ni­ties in an affin­ity sys­tem, in which the mem­bers of a given com­mu­nity col­lec­tively pre­fer each other to any­one else out­side the com­mu­nity. Thus these com­mu­ni­ties are “self-​​determined” or “self-​​certified” by the affin­ity sys­tem. We pro­vide a tight poly­no­mial bound on the num­ber of self-​​determined com­mu­ni­ties as a func­tion of the robust­ness of the com­mu­nity. More­over, we present a polynomial-​​time algo­rithm for enu­mer­at­ing these com­mu­ni­ties, as well as a local algo­rithm with a strong sto­chas­tic per­for­mance guar­an­tee that can find a com­mu­nity in time nearly lin­ear in the of size the community.…”
  
  network-​​theory social-​​capital social-​​dynamics self-​​assembly agent-​​based graph-​​theory algo­rithms com­plex­ol­ogy nudge-​​targets

• [1201.5076] Tech­ni­cal Report #SEHIR-IE-VA-12–1: Opti­mal Obsta­cle Place­ment with Disambiguations

  “We intro­duce the opti­mal obsta­cle place­ment with dis­am­bigua­tions prob­lem wherein the goal is to place true obsta­cles in an envi­ron­ment clut­tered with false obsta­cles so as to max­i­mize the total tra­ver­sal length of a nav­i­gat­ing agent (NAVA). Prior to the tra­ver­sal, NAVA is given loca­tion infor­ma­tion and prob­a­bilis­tic esti­mates of each disk-​​shaped hin­drance (here­inafter referred to as disk) being a true obsta­cle. The NAVA can dis­am­biguate a disk’s sta­tus only when sit­u­ated on its bound­ary. There exists an obsta­cle plac­ing agent (OPA) that locates obsta­cles prior to NAVA’s tra­ver­sal. The goal of OPA is to place true obsta­cles in between the clut­ter in such a way that NAVA’s tra­ver­sal length is max­i­mized in a game-​​theoretic sense.…”
  
  agent-​​based game-​​theory robot­ics disambiguation-​​design nudge-​​targets military-​​applications algo­rithms

• [1010.5017] Col­lec­tive motion

  “We review the obser­va­tions and the basic laws describ­ing the essen­tial aspects of col­lec­tive motion — being one of the most com­mon and spec­tac­u­lar man­i­fes­ta­tion of coor­di­nated behav­ior. Our aim is to pro­vide a bal­anced dis­cus­sion of the var­i­ous facets of this highly mul­ti­dis­ci­pli­nary field, includ­ing exper­i­ments, math­e­mat­i­cal meth­ods and mod­els for sim­u­la­tions, so that read­ers with a vari­ety of back­ground could get both the basics and a broader, more detailed pic­ture of the field. The obser­va­tions we report on include sys­tems con­sist­ing of units rang­ing from macro­mol­e­cules through metal­lic rods and robots to groups of ani­mals and peo­ple. Some empha­sis is put on mod­els that are sim­ple and real­is­tic enough to repro­duce the numer­ous related obser­va­tions and are use­ful for devel­op­ing con­cepts for a bet­ter under­stand­ing of the com­plex­ity of sys­tems con­sist­ing of many simul­ta­ne­ously mov­ing enti­ties. As such, these mod­els allow the estab­lish­ing of a few fun­da­men­tal prin­ci­ples of flock­ing. In par­tic­u­lar, it is demon­strated, that in spite of con­sid­er­able dif­fer­ences, a num­ber of deep analo­gies exist between equi­lib­rium sta­tis­ti­cal physics sys­tems and those made of self-​​propelled (in most cases liv­ing) units. In both cases only a few well defined macroscopic/​collective states occur and the tran­si­tions between these states fol­low a sim­i­lar sce­nario, involv­ing dis­con­ti­nu­ity and alge­braic divergences.”
  
  emer­gence emergent-​​design biol­ogy ethol­ogy com­plex­ol­ogy mod­els artificial-​​life nudge-​​targets

• [1201.5568] Dynamic trees for stream­ing and mas­sive data contexts

  “Data col­lec­tion at a mas­sive scale is becom­ing ubiq­ui­tous in a wide vari­ety of set­tings, from vast offline data­bases to stream­ing real-​​time infor­ma­tion. Learn­ing algo­rithms deployed in such con­texts must rely on single-​​pass infer­ence, where the data his­tory is never revis­ited. In stream­ing con­texts, learn­ing must also be tem­po­rally adap­tive to remain up-​​to-​​date against unfore­seen changes in the data gen­er­at­ing mech­a­nism. Although rapidly grow­ing, the online Bayesian infer­ence lit­er­a­ture remains chal­lenged by mas­sive data and tran­sient, evolv­ing data streams. Non-​​parametric mod­el­ling tech­niques can prove par­tic­u­larly ill-​​suited, as the com­plex­ity of the model is allowed to increase with the sam­ple size. In this work, we take steps to over­come these chal­lenges by port­ing stan­dard stream­ing tech­niques, like data dis­card­ing and down­weight­ing, into a fully Bayesian frame­work via the use of infor­ma­tive pri­ors and active learn­ing heuris­tics. We show­case our meth­ods by aug­ment­ing a mod­ern non-​​parametric mod­el­ling frame­work, dynamic trees, and illus­trate its per­for­mance on a num­ber of prac­ti­cal exam­ples. The end prod­uct is a pow­er­ful stream­ing regres­sion and clas­si­fi­ca­tion tool, whose per­for­mance com­pares favourably to the state-​​of-​​the-​​art.”
  
  data-​​analysis learning-​​from-​​data algo­rithms drinking-​​from-​​the-​​firehose nudge data-​​mining