DyBaNeM: Bayesian Framework for Episodic Memory Modelling

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1 DyBaNeM: Bayesian Framework for Episodic Memory Modelling Rudolf Kadlec Cyril Brom Faculy of Mahemaics and Physics Charles Universiy in Prague, Czech Republic Absrac Episodic Memory (EM) plays a cenral role in cogniive archiecures and arificial agens ha need abiliy o remember and recall he pas. Mos of he currenly implemened EM sysems resemble logs of evens sored in a daabase wih indexes. However, hese sysems usually lack abiliies of human memory like hierarchical organizaion of episodes, reconsrucive memory rerieval and encoding of episodes wih respec o previously learn repeaed schemaa. Here, we presen a new general framework for EM modelling, DyBaNeM, ha has hese abiliies. From psychological poin of view, i builds on he Fuzzy-Trace Theory (FTT). On compuaional side, i uses formalism of Dynamic Bayesian Neworks (DBNs). We describe absrac encoding, sorage and rerieval algorihms, wo models we implemened wihin DyBaNeM, and evaluaion of he models performance using a daase resembling a log of human aciviy. Keywords: Episodic Memory; Fuzzy Trace Theory; Dynamic Bayesian Neworks. Inroducion EM (Tulving, 1983) is an umbrella erm for memory sysems operaing wih personal hisory of an eniy (as opposed o semanic facs or procedural rules). Mos EM sysems implemened o dae capialize on symbolic represenaions resembling indexed logs of evens. These daa srucures make i hard o implemen some of he mos remarkable feaures of human EM, such as hierarchical organizaion and reconsrucive rerieval. The goal of his paper is o presen a new framework for EM modelling, DyBaNeM, in which hese wo feaures are naurally emerging properies. We also presen wo models implemened wihin his framework. The framework is inspired by he FTT (Gallo, 2006). FTT hypohesizes wo parallel mechanisms ha encode incoming informaion: verbaim and gis. While verbaim encodes he surface-form of he informaion in deail, gis encodes he meaning in a coarsegrained way (Gallo, 2006), capializing on previously learn schemaa (Barle, 1932) of episodes and pars of episodes. There is also furher evidence ha people end o perceive episodes in a hierarchical fashion (Zacks & Tversky, 2001). Models buil wihin he DyBaNeM framework works as follows. Firs he schemaa of episodes are learned from annoaed examples modeling procedural learning during childhood. Once learn, he schemaa remain fixed, becoming an underlying srucure for encoding, sorage and episodic rerieval, as exemplified on Figure 1. Suppose ha Bob ries o remember wha Alice does. While observing her, he segmens wha she is doing ino a hierarchy of nesed episodes. This deduced hierarchy is used in encoding, where some of he mos ineresing deails are remembered. Some of he deails may be forgoen during sorage (such as o 3 on Figure 1). In he end, when Bob wans o re-ell wha he saw, he reconsrucs he whole sory using remembered knowledge (E2 T 0 = c0 2 T ;O 1 = o 1 ) and he previously learn schemaa. However, due o imperfec percepion, encoding and forgeing in sorage, he recalled evens may no compleely mach he original sory. In erms of FTT he iniially sored facs O 1 = o 1 and O 3 = o 3 are verbaim, since hey correspond o real observaions, and E2 T 0 = c0 2 T is a gis because his is Bob s inerpreaion of Alice s acions. Alice Bob Bob s percepion Bob s sorage Bob s encoding Bob s rerieval Deduced episodes Gis Verbaim ρ 0 ρ 1 ρ 2 ρ 3 ρ T a 1 0~T b 0 0~1 c 0 2~T o 0 o 1 o 2 o 3 o T a 1 0~T b 0 0~1 c 0 2~T o 0 o 1 o 2 o 3 o T E 0 2~T = c0 2~T O 1 = o 1 O 3 = o 3 O 3 = o 3 forgoen a 1 0~T b 0 0~1 c 0 2~T o 0 o 1 o 2 o 3 o T Hi Alice, I remember you doing o 1 so you probably were doing b 0 0~1 a ha ime. Episodic schemaa Sored Observed Reconsruced Realiy Figure 1: Percepion-encoding-sorage-rerieval cycle. Alice performs a sequence of acions, Bob perceives his and deduces Alice s episodic hierarchy, hen he sores encoded represenaion unil rerieval when he reconsrucs he original observaion wih he use of schemaa. Noaion x k i j means ha episode x on level k sared a ime i and ended a j.

2 The presened framework uses Bayesian formalism. I implemens boh he percepion (where Bob segmens Alice s behavior) and episode reconsrucion in rerieval wih he same class of probabilisic models DBN (Koller & Friedman, 2009). The underlying DBN is also heavily used in encoding. In every sep, differen probabilisic queries are performed over his model. Deails of he model ha had o be omied here due o space consrains are provided in (Kadlec & Brom, 2013b). While he DBN formalism is widely used, wha is new here is exploraion of is applicaion on EM modelling. FTT was previously implemened in a seing of saic scenes (Hemmer & Seyvers, 2009). Our work exends his general approach also o sequences of evens. In he nex secion we discuss assumpion of hierarchical decomposiion of behavior. Then we inroduce our Dy- BaNeM framework for EM modelling. In he end we evaluae i using a corpora of hierarchically organized aciviies and illusrae reconsrucive recall in he model. Hierarchical Aciviy Represenaion and Probabilisic Models One of he core assumpions of our framework is ha flow of evens can be represened in a hierarchy of so called episodes. This seem o be rue boh for humans (Zacks & Tversky, 2001) and for many compuer conrolled agens. Many formalisms popular for programming agens in boh academia and indusry use some kind of hierarchy, such as hierarchical finie sae machines or behavior rees. A he same ime, here is a body of lieraure on using DBNs for aciviy recogniion ha also uses he episode/aciviy hierarchy, e.g. (Bui, 2003; Blaylock & Allen, 2006). In aciviy recogniion, DBNs-based models can be used for deducing absrac episodes, ha is, for compuing poserior probabiliy of episodes condiioned on observaions. In EM modelling, we need also he inverse way; asking wha if queries: wha is a rerieved memory of a siuaion given he agen remembers wo paricular facs? Luckily, he DBNs are flexible enough o allow for his reverse pah. DyBaNeM: Probabilisic EM Framework In his secion we inroduce our EM framework. We sar wih auxiliary definiions needed for descripion of he model. Then we show how DBNs can be used for aciviy/episode recogniion and how he episodic schemaa are represened by wo paricular nework archiecures. Finally, we presen he algorihms of encoding, sorage and rerieval. Noaion. Uppercase leers will denoe random variables (e.g. X,Y, O) whereas lowercase leers denoe heir values (e.g. x, y, o). Probabiliy mass funcion (PMF) of random variable X will be denoed by P(X). When X is discree, P(X) will be also used o refer o a able specifying he PMF. Domain of X will be denoed as D(X). Noaion X i: j will be a shorhand for sequence of variables X i,x i+1...x j, analogically x i: j will be a sequence of values of hose variables. The subscrip will usually denoe ime. M will be a probabilisic model and V is a se of all random variables in he model. Formalizing he episodic represenaion, world sae, and inpus/oupus. In his secion we formalize wha represenaion of episodes and world sae is assumed by he Dy- BaNeM framework. Definiion 1 Episode is a sequence (possibly of lengh 1) of observaions or more fine-grained episodes (sub-episodes) ha has a clear beginning and an end. Noe ha episodes may be hierarchically organized. Definiion 2 Episodic schema is a general paern specifying how insances of episodes of he same class look like. For insance, an episodic schema (cf. he noion of scrip or memory organizaion packe (Schank, 1999)) migh require every episode derivable from his schema o sar by even a, hen going eiher o even b or c and ending by d. Definiion 3 Episodic race ε 0:n is a uple e 0,e 1...e n represening a hierarchy of episodes a ime ; e 0 is he currenly acive lowes level episode, e 1 is is direc paren episode and e n is he roo episode in he hierarchy of deph n. Example of an episodic race can be ε 0:n 0 = WALK,COMMUT E and ε 0:n 1 = GO BY BUS, COMMUT E. The noaion of episodic race reflecs he fac ha an agen s behavior has ofen hierarchical naure. Our model uses probabilisic represenaion, hence even if here is only one objecively valid episodic race for every agen a each ime sep, inpu of he EM model will be a probabiliy disribuion. Le E i denoes a random variable represening a belief abou an episode on level i a ime. While he rue value of E i is, say, e, i he PMF enables us o cope wih possible uncerainy in percepion and memory recall. Definiion 4 Probabilisic episodic race E 0:n is a uple of random variables E 0,E 1...E n represening an agen s belief abou wha happened a ime. Analogically E0: 0:n denoes probabilisic episodic race over muliple ime seps. The following daa srucure represens an agen s rue percepion of he environmen sae. Definiion 5 Le ρ denoes observable environmenal properies a ime. For insance, ρ can hold aomic acions execued by an observed agen (and possibly oher hings oo), e.g. ρ 0 = STAND ST ILL, ρ 1 = GET TO BUS. Analogically o E 0:n and ε 0:n, O is a random variable represening belief abou observaion ρ. Fig. 2 shows how hese definiions ranslae o an example DBN srucure. In his paper, we describe implemenaion of wo DBN-based models and he figure depics boh of hem. Surprise. In encoding, he framework works wih quaniy measuring difference beween he expeced real sae of a random variable and is expeced sae given he remembered facs. We call his quaniy surprise. In Bayesian framework

3 E 0:n :+1 (n+1) h level of episodes 2 nd level of episodes 1 s level of episodes Observaions E 0:n O :+1 E n E 1 E 0 O H n H 1 H 0 F n F 1 F 0 E n +1 E 1 +1 E 0 +1 O +1 H n +1 H 1 +1 H 0 +1 F n +1 F 1 +1 F 0 +1 Figure 2: An example of a DBN s srucure ogeher wih our noaion. Solid lines show nework archiecure of CHMM (Blaylock & Allen, 2006) model, whereas when he doed drawing is added we obain nework of AHMEM (Bui, 2003) model. surprise can be defined as difference beween prior and poserior probabiliy disribuions. We adop approach of (Ii & Baldi, 2009) who propose o use Kullback-Leibler (KL) divergence (Kullback, 1959) o measure surprise. Definiion 6 KL divergence (Kullback, 1959) of wo PMFs P(X) and P(Y ), where D(X) = D(Y ) is defined as: KL(P(X) P(Y )) = x D(X) P(X = x) P(X = x)ln P(Y = x) We use noaion wih o sress direcionaliy of KL divergence; noe ha i is no symmerical. We will use KL divergence as a core ool of our framework. Anoher opion migh be e.g. he Kolmogorov-Smirnov es. Learning schemaa Episodic schemaa are represened by parameers ˆθ of a DBN. Expressiveness of schemaa depends on he srucure of a model a hand. We will suppose ha he DBN s opology is fixed. Thus learning schemaa will reduce o well known parameer learning mehods. DBN wih unobserved nodes has o be learn by Expecaion-Maximizaion algorihm (EM algorihm), opologies wihou unobserved nodes are learn by couning he sufficien saisics (Koller & Friedman, 2009). In our case examples of episodes ha we wan o use for schemaa learning will be denoed by D = {d 1,d 2...d n } where each d i can be one day of an agen s live, or any oher appropriae ime window. d i iself is a sequence of ime equidisan examples c, ha is, d i = {c i 0,ci 1...ci i }. Each c i is a uple ε 0:n,ρ, i conains an episodic race and observable sae of he environmen. DBN Archiecures For compuing probabiliies, our framework makes i possible o use any DBN archiecure where some nodes represen observaions and some probabilisic episodic race. In his paper we use wo archiecures, simple CHMM (Blaylock & Allen, 2006) and more complex AHMEM (Bui, 2003). As we said he schemaa are represened by parameer ˆθ, ha is, by all condiional probabiliy mass funcions (CPMFs) of he DBN s nodes. Expressiveness of he schemaa depends on he srucure of DBN. In CHMM episodic schemaa encode probabiliy of an episode given previous episode on he same level in he hierarchy and also given is paren episode (P(E i E 1 i,ei+1 )). This is one of possible hierarchical exensions of a well known Hidden Markov Model (HMM). CHMM is learn by couning he sufficien saisic. AHMEM is an augmenaion of CHMM ha exends each episodic schema wih a limied inernal sae (memory) represened by a probabilisic finie sae machine (PFSM) wih erminal saes. This auomaon is represened by random variables F i and H i in Fig. 2. This adds even more richness o he represened schemaa. H i represens ransiion beween saes of he PFSM ( D(H i ) is he number of he PFSM s saes). F i is a variable indicaing ha some saes of he PFSM are erminal, hus he episode has finished (D(F i ) = {erminal, nonerminal}). Advaages of his model are described in (Bui, 2003). Downside of he expressive AHMEM models is ha hey are compuaionally more expensive han CHMM. Since AHMEM conains unobservable variables H i, i has o be learn by EM algorihm. Encoding The encoding algorihm compues a lis of mems on he basis of he agen s percepion, Per 0:T, of he siuaion o be remembered. Per 0:T is a se of PMFs such ha Per 0:T = { f X : X Observable}, where f X is PMF for each variable X of ineres. Now we have o disinguish wha scenario we are going o use: 1. Observable = O 0:T Bob is going o encode Alice s aciviy whose ε Alice is hidden o Bob, neverheless Bob perceives Alice s aomic acions ha are conained in ρ Alice. This is he use-case described in Figure 1. We inroduce an observaion uncerainy by defining f O smoohed 1 ρ ; P(E 0:n 0:T ) will be deduced during he encoding algorihm. 2. Observable = E 0:n 0:T O 0:T Bob is going o encode his own aciviy, in his case he episodic race ε Bob is available since Bob knows wha he waned o do. Values of f O are compued as above and f E i smoohed e i. Algorihm 1 is a skeleon of he encoding procedure. The inpu of he algorihm is Per 0:T, where he ime window 0 : T is arbirary. In our work we use ime window of one day. The oupu is a lis of mems encoding his inerval. 1 For deails of smoohing see (Kadlec & Brom, 2013b)

4 Algorihm 1 General schema of encoding algorihm Require: Per 0:T PMFs represening he agen s percepion of he siuaion (i.e. smoohed observaions) Require: M probabilisic model represening learned schemaa 1: procedure ENCODING(Per 0:T,M ) 2: mems empy Lis of mems is empy 3: while EncodingIsNoGoodEnough do 4: X GeMem(M,Per 0:T,mems) 5: x max MLO PM (X mems) 6: mems.add(x = x max ) 7: end while 8: reurn mems 9: end procedure The algorihm runs in a loop ha erminaes once he EncodingIsNoGoodEnough funcion is false. There are more possible sop crieria. We use mems < K because his models limied memory for each day. Oher possibiliies are described in (Kadlec & Brom, 2013b). In each cycle, he GeMem funcion reurns he variable X i ha will be remembered. The MLO funcion (mos likely oucome) is defined as: MLO PM (X evidence) argmaxp(x = x evidence) (1) x D(X) This means ha we ge he mos probable value for X and add his assignmen o he lis of mems. 2 In he end he procedure reurns he lis of mems. We have developed wo varians of he GeMem funcion, each has is jusificaion. In he firs case, he idea is o look for a variable whose observed PMF and PMF in he consruced memory differs he mos. This variable has he highes surprise and hence i should be useful o remember i. This sraegy will be called rerospecive maximum surprise (RMaxS). I is rerospecive since i assumes ha he agen has all observaions in a shor erm memory sore and, e.g., a he end of he day, he rerospecively encodes he whole experience. RMaxS sraegy can be formalized as: X argmaxkl(p M (Y Per 0:T ) P M (Y mems)) (2) Y VOI where P(Y Per 0:T ) P(Y X = f X : f X Per 0:T ), we condiion he probabiliy on all observaions. VOI V is a se of random variables of ineres whose value can be remembered by he model. There can be some variables in he DBN ha we do no wan o remember since hey are hardly inerpreable for human. In our implemenaion we used VOI = E0:T 0:n O 0:T. The alernaive sraegy called rerospecive minimum overall surprise (RMinOS) assumes a more sophisicaed pro- 2 Maximum a poseriori (MAP) (Koller & Friedman, 2009) query would be more appropriae, we use MLO for simpliciy. cess. RMinOS picks he variable value pair whose knowledge minimizes surprise from he original sae of each Y VOI o is recalled sae. The following equaion capures his idea: X argmin Y VOI Z V KL (P M (Z Per 0:T ) P M (Z Y = ȳ,mems)) (3) where ȳ MLO PM (Y mems). Ideally, one should minimize KL divergence from P(E0:T 0:n,O 0:T Per 0:T ) P(E0:T 0:n,O 0:T Y = ȳ,mems); however, ha would require compuing he whole join probabiliy. Thus, we use summaion of surprise in each variable insead. Noe ha we remember only he mos probable value, including he ime index, in he mems lis insead of he whole disribuion. This helps o make mems clearly inerpreable. However full disribuions can be used in he mems as well. Sorage and forgeing During sorage, he mems can undergo opional ime decayed forgeing. The following equaion shows relaion beween age of he mem m, is iniial srengh S and is reenion R (e is Euler s number) (Anderson, 1983): R(m) = e S. The iniial srengh S of mem m can be derived from he value of KL divergence in Eq. 2 or from he value of he sum in Eq. 3, respecively. Once R(m) decreases under he hreshold β f orge, m will be deleed from he lis of mems and will no conribue o recall of he memory. Rerieval Rerieval is a simple process of combining he schemaa wih mems. Algorihm 2 shows his. Algorihm 2 Rerieval Require: k cue for obaining he episode 1: procedure RETRIEVAL(k, M ) 2: mems gememsfor(k) Lis of mems associaed wih he cue 3: reurn {P M (Y mems) : Y VOI} 4: end procedure We simply obain he lis of mems for search cue k, which can be, e.g., a ime inerval. Then we use assignmens in he mems lis as an evidence for he probabilisic model. The resuling PMFs for all variables of ineres are reurned as a reconsruced memory for he cue k. Implemenaion As a proof-of-concep of he DyBaNeM framework, we implemened wo models, AHMEM and CHMM, and wo encoding sraegies RMaxS and RMinOS. The model was implemened in Java and is freely available for download 3. For 3 Projec s homepage hp://code.google.com/p/dybanem

5 belief propagaion in DBNs, SMILE 4 reasoning engine for graphical probabilisic models was used. Evaluaion Aim. We esed how he number of sored mems affecs recall accuracy when a model is applied o a daase resembling human aciviy. Muliple archiecures of he underlying probabilisic models were used o creae several model varians. We used boh CHMM and AHMEM wih one or wo levels of episodes. The CHMM models will be denoed as CHMM 1 or CHMM 2, respecively. In he AHMEM we also variaed he number of saes ( D(H i ) ) of he PFSM consising par of episodic schemaa. AHMEMs l denoes a model wih l levels and s saes of inernal memory. We esed AHMEM2 1, 1 8 and 2 2. AHMEM2 8 required more han 1.5 GB RAM ha was dedicaed o es and hence i was no esed. For each probabilisic model we esed boh RMaxS and RMinOS encoding sraegies and encoded one, wo and hree mems in urn. Daase. We used Monroe plan corpus (Blaylock & Allen, 2005) which is similar o real human aciviy corpus. Monroe corpus is an arificially generaed corpus of hierarchical aciviies creaed by a randomized hierarchical ask nework (HTN) planner. The domain describes rescue operaions like cuing up a fallen ree o clear a road, ransporing wounded o a hospial, ec. The corpus conains up o 9 levels of episodes hierarchy, we shorened hese episodic races o conain only he aomic acions and one or wo levels of episodes (based on he DBN used). The corpus feaures 28 aomic acions and 43 episodes. Mehod. For he purpose of he experimen we spli he sream of episodic races ino sequences of 10, each of which can be viewed as one day of aciviies. The schemaa were learned on 152 days. We esed accuracy of recall of aomic acions ha happened in one of randomly picked 20 days (ou of 152 days). Inference over he DBN was performed by exac clusering algorihm (Huang & Darwiche, 1996). Resuls. Tab. 1 summarizes recall performances of he models. We use a simple crude accuracy measure ha measures how many acions were correcly recalled a correc ime. Only he mos probable acion a each sep was used. Discussion. To undersand how well he models perform we can consruc a simplisic baseline model. The baseline model would use mems only for soring observaions (remembering only verbaim, no gis) and recall he mos frequen aomic acion for he ime seps where no mem was sored. Therefore he schema in his model would be represened only by he mos frequen acion in he corpus, which is NAV IGAT E V EHICLE appearing in 40% of ime seps. Thus he baseline model would score (40 + mems 10)% accuracy on average since we encoded only sequences of 10 observaions for each day, hus remembering one mem would increase accuracy by 10%. All models performed 4 SMILE was developed by he Decision Sysems Laboraory of he Universiy of Pisburgh and is available a hp://genie.sis.pi.edu/ RMaxS RMinOS mems arch AHMEM2 1 55% 75% 93% 57% 77% 92% AHMEM8 1 67% 88% 97% 64% 90% 98% AHMEM % 78% 93% 53% 75% 93% CHMM 1 53% 70% 85% 56% 71% 86% CHMM 2 57% 69% 85% 59% 72% 87% Table 1: Resuls of he recall experimen for all esed models, encoding sraegies and he number of sored mems. beer han he baseline. As expeced, on average, i holds ha more complex probabilisic models beer capure he episodic schemaa and hence have a higher recall accuracy. In our experimen, AHMEM8 1, he model wih he highes number of parameers, dominaes all he oher archiecures. On he oher hand here are only suble differences in he encoding sraegies RMaxS and RMinOS. Since RMaxS requires less compuaional ime, i should be preferred a leas on domains similar o ours. Example of recall. We now demonsrae how he number of sored mems affecs he recalled sequence. In an encoded example day only direc observaions (values of O ) ended sored in he mems, however his does no have o be rue in general. Fig. 3 shows probabiliy disribuions when considering differen number of mems for recall of aciviies from he example day. The mems are sored according o he order in which hey were creaed by he encoding algorihm. Hence we can visualize how he forgeing would affec recall since he hird mem is he leas significan one and i will be forgoen as he firs, whereas he firs mem will be forgoen as he las. Afer forgeing all he mems he model would reurn NAV IGAT E V EHICLE for each ime poin giving us 30% accuracy because his acion appears hree imes in his paricular sequence. This would be he same accuracy as he baseline s. Wih one mem a remembered episodic schema is acivaed and accuracy grows o 50% (10% more han he baseline). The second mem furher specifies he acivaed schema and changes he mos probable acion no only in = 9, which is he mem iself, bu also in = 3,5,6 and 8, and accuracy rises o 100% (50% more han he baseline). The hird mem removes he las uncerainy a = 4. Conclusion We presened DyBaNeM probabilisic framework for EM modelling. Evaluaion has shown ha increasingly complex DBN archiecures provide beer episodic schemaa and ha RMaxS sraegy is sufficien for good encoding. Our model can be used in general purpose cogniive archiecures as well as in virual agens where i can increase an agen s believabiliy (Kadlec & Brom, 2013a). We can see he framework as a ool for lossy compression of sequences of evens, hus long living agens can plausibly sore more memories in he

6 Figure 3: Recall of observaion probabiliies for 10 ime seps (one day) in AHMEM8 1 + RMaxS model wih increasing number of mems used o reconsruc he sequence. Level of gray indicaes probabiliy of each aomic acion a ha ime sep. The darker he color is he more probable he acion is. The firs figure shows P(O ) for each ime sep in he remembered sequence when only schemaa are used, his is wha he model would answer afer forgeing all mems; he second shows P(O O 0 = CLEAN HAZARD), recall wih one mem; he hird P(O O 0 = CLEAN HAZARD,O 9 = CUT T REE) and he fourh P(O O 0 = CLEAN HAZARD,O 9 = CUT T REE,O 4 = REMOV E WIRE). The mems are marked by circles, all he oher values are derived from he schema ha is mos probable given he recalled mems. Only he relevan acions are shown. same space. However he used probabilisic model has o be chosen carefully since compuaional complexiy of inference in DBNs is high. On he oher hand here are efficien approximaion echniques ha can speed up he compuaion. Our fuure work include learning he models using real human corpora, e.g. (Kadlec & Brom, 2011), and comparing he models oupus wih daa from psychological experimens.. Acknowledgmens This research was parially suppored by SVV projec number and by gran GACR P103/10/1287. We hank o xkcd.org for drawings of Alice and Bob. References Anderson, J. (1983). A spreading acivaion heory of memory. Journal of verbal learning and verbal behavior, 22, Barle, F. (1932). Remembering: A Sudy in Experimenal and Social Psychology. Cambridge, England: Cambridge Universiy Press. Blaylock, N., & Allen, J. (2005). Generaing Arificial Corpora for Plan Recogniion. In L. Ardissono, P. Brna, & A. Mirovic (Eds.), Proceedings of he 10h inernaional conference on user modeling (UM 05) (pp ). Edinburgh: Springer. Corpus is available from Blaylock, N., & Allen, J. (2006). Fas hierarchical goal schema recogniion. Proceedings of he Naional Conference on Arificial Inelligence (AAAI 2006), Bui, H. (2003). A general model for online probabilisic plan recogniion. Inernaional Join Conference on Arificial Inelligence, Gallo, D. (2006). Associaive illusions of memory: False memory research in DRM and relaed asks. Psychology Press. Hemmer, P., & Seyvers, M. (2009). Inegraing Episodic and Semanic Informaion in Memory for Naural Scenes. CogSci, Huang, C., & Darwiche, A. (1996). Inference in belief neworks: A procedural guide. Inernaional journal of approximae reasoning(15), Ii, L., & Baldi, P. (2009). Bayesian surprise aracs human aenion. Vision research, 49(10), Kadlec, R., & Brom, C. (2011). Towards an auomaic diary : an aciviy recogniion from daa colleced by a mobile phone. In IJCAI workshop on Space, Time and Ambien Inelligence (pp ). Kadlec, R., & Brom, C. (2013a). DyBaNeM : Bayesian Episodic Memory Framework for Inelligen Virual Agens. Inelligen Virual Agens 2013, in press. Kadlec, R., & Brom, C. (2013b). Unifying episodic memory models for arificial agens wih aciviy recogniion problem and compression algorihms : review of recen work and prospecs (Tech. Rep.). Koller, D., & Friedman, N. (2009). Probabilisic graphical models: principles and echniques. The MIT Press. Kullback, S. (1959). Saisics and informaion heory. J. Wiley and Sons, New York. Schank, R. C. (1999). Dynamic memory revisied. Cambridge Universiy Press. Tulving, E. (1983). Elemens Of Episodic Memory. Clarendon Press Oxford. Zacks, J. M., & Tversky, B. (2001). Even srucure in percepion and concepion. Psychological bullein, 127(1), 3 21.