A Formal Approach for Contextual Planning Management: Application to Smart Campus Environment.

Transcription

1 A Forml Approch for Contextul Plnning Mngement: Appliction to Smrt Cmpus Environment Ahmed-Chwki Chouche, Aml El Fllh Seghrouchni, Jen-Michel Ilié, Djmel Eddine Sïdouni To cite this version: Ahmed-Chwki Chouche, Aml El Fllh Seghrouchni, Jen-Michel Ilié, Djmel Eddine Sïdouni. A Forml Approch for Contextul Plnning Mngement: Appliction to Smrt Cmpus Environment. Bzzn, An L.C.; Pichr, Krim. the 14th edition of the Ibero-Americn Conference on Artificil Intelligence, Nov 2014, Sntigo, Chile. Springer, Advnces in Artificil Intelligence - IBERAMIA 2014, 8864, pp , 2014, Lecture Notes in Artificil Intelligence. < < / _64>. <hl > HAL Id: hl Submitted on 9 Dec 2014 HAL is multi-disciplinry open ccess rchive for the deposit nd dissemintion of scientific reserch documents, whether they re published or not. The documents my come from teching nd reserch institutions in Frnce or brod, or from public or privte reserch centers. L rchive ouverte pluridisciplinire HAL, est destinée u dépôt et à l diffusion de documents scientifiques de niveu recherche, publiés ou non, émnnt des étblissements d enseignement et de recherche frnçis ou étrngers, des lbortoires publics ou privés.

2 A Forml Approch for Contextul Plnning Mngement: Appliction to Smrt Cmpus Environment Ahmed-Chwki Chouche 1,2, Aml El Fllh Seghrouchni 1, Jen-Michel Ilié 1 nd Djmel Eddine Sïdouni 2 1 LIP6 Lbortory, University of Pierre nd Mrie Curie 4 Plce Jussieu, Pris, Frnce {hmed.chouche,ml.elfllh,jen-michel.ilie}@lip6.fr 2 MISC Lbortory, University Constntine 2 Ali Mendjeli Cmpus, Constntine, Algeri sidouni@misc-umc.org Abstrct. In this pper, we ddress the building of mbient systems s utonomous nd context-wre intelligent gents. The originl contribution is n lgebric lnguge, nmely AgLOTOS, used to utomticlly build the pln of n gent from its intentions. As plns re formlly conceived s structured set of concurrent processes, we show how to define guidnce service, helping the gent to mximize the stisfction of its intentions. The underlying structure, clled Contextul Plnning System (CPS), tkes the contextul informtion into ccount to predict the bility to execute the processes in plns. The lst prt of the pper tlks bout our current experiment integrting the proposed technique to ssist user in smrt cmpus university. Keywords: context-wreness, BDI gent, plnning lnguge, plnning guidnce, smrt cmpus. 1 Introduction Multi-gent System (MAS) pproches offer interesting frmeworks for the development of mbient intelligence (AmI) systems, since their gents re considered s intelligent, proctive nd utonomous [1]. This pper introduces n efficient plnning mngement process into the rchitecture of the gent. In prticulr, we im t offering to ech AmI gent, powerful predictive service. Like in other recent MAS pproches, e.g. [2, 3], which re dedicted to the plnning nd the vlidtion of gents in MAS, we focus on one gent rther thn on the whole MAS. Regrding AmI systems, this eses us to consider whtever dynmic fetures in the environment for the gents nd to propose solutions consistent with the openness of the system. Belief-Desire-Intention (BDI) re well-known intentionl structure emphsizing the resoning tsks of the gent, up to obtin rtionlity properties. Such properties re relly pprecite in AmI systems since this enforces the confidence

3 on the system. The uthors of [4] took profit from the fct tht the pln of BDI gent cn be derived from its intentions, themselves resulting from the resoning of the BDI interpreter [5]. In this context, the AgLOTOS lnguge ws defined to specify the plns ccordingly to the following two criteri: (1) enhnce the modulr nd concurrent spects relted to the execution of plns, up to see the pln s composed of concurrent processes, (2) hndle the well-ordered composition of intentions, i.e. n gent cn ttribute weights to privilege execution with respect to some intentions. In this pper, the AgLOTOS lnguge is considered gin but its semntics is enriched to utomticlly produce stte trnsition structure, nmely Contextul Plnning System (CPS for short). The im is to cpture the evolution of the pln in predictive wy, mening tht ctions re supposed to be run successfully, but lso tht the context of the gent cn evolve under the execution of ctions. Automtic serches on this structure will llow us to propose guidnce services, prticulrly helpful for the decision of gent in expected contexts. Moreover, we im t showing how our forml pproch cn be embedded in the development of the AmI gents. To our opinion, this contributes to mking the opertionl bridge between AmI softwre engineering nd forml pproches. Our project consisting in the design of smrt university cmpus is the very right plce to embed our AmI gents rchitecture through flot of smrt-devices dedicted to ssisting users. In this project, the discovery of physicl loctions nd the moves of users re tken into ccount. Unlike pure MAS pproches, this cnnot be reduced to socil problem nd communiction between softwre gents. The outline of the pper is the following: Section 2 reclls the gent softwre rchitecture we consider, nmely the HoA rchitecture, nd its specific plnning lnguge used to ssocite plns with intentions. In Section 3, contextul plnning mngement is presented bsed on the building of the CPS structure. Section 4 detils the concrete stges of the smrt-cmpus project to conceive the AmI systems. A relistic scenrio is given s n illustrtion of the concepts proposed in the pper. The lst section concludes nd brings out our next perspectives. 2 The HoA Architecture nd its Plnning Lnguge Figure 1 highlights the gent rchitecture we consider for AmI systems. Clled Higher-order Agent rchitecture (HoA), it enhnces cler seprtion in three processes: The Context process is in chrge of the context informtion of the gent. It is triggered by new perceptions of the environment nd lso by internl events informing bout the executions of ctions. At low level, it is in chrge of observing the reliztion of the ction executions, in order to stte they re successfully chieved or not. The Mentl process corresponds to the resoning prt of the gent. It is notified by the context process so tht it cn be wre of the importnt context

4 Execution Context BDI Perc. Context Process Evt Plnning Mentl Process Process LibP internl event Actions Environment Fig. 1. Higher-order Agent rchitecture chnges nd cn provoke possible revisions of the beliefs (B), desires (D), nd intentions (I) dt. As highlighted in Figure 1, the mentl process represents the resoning mechnism, which mnges the BDI sttes of the gent. Triggered by the perceived events, it updtes the B,D nd I structures. In order to orgnize its selected intentions, the mentl process is ble to schedule them by ssociting with ech one given weight. The Plnning process is clled by the mentl process. Helped by librry of plns (LibP), it minly produces pln of ctions from the set of weighted intentions, but lso offers some services relted to the mngement of plns (see Section 3). Mentl process I Plnning process P Agent pln I = {i w0 0, iw1 1 {, },..., iwj j } P P P 0 1 j Intention plns P 0,0 P 0,1 P 0,k Elementry plns E 0,0 E 0,1 E 0,k Behvior expressions Fig. 2. Agent plnning structure In our pproch for ech BDI stte, the pln of the gent is described by using the AgLOTOS lnguge, s detiled in [4]. The lnguge itself extends the LOTOS lnguge [6] in order to specify concurrency between ctions in plns. In ddition s schemed by Figure 2, it refers to two level plnning structures: (1) the Agent pln is mde of sub-plns clled Intentions plns, ech one dedicted to chieve the ssocited selected intention; (2) ech intention pln is n lternte of severl sub-plns, clled Elementry plns, extrcted from the LibP librry. This llows one to consider different wys to chieve the ssocited intention.

5 Further, we ssume tht the LibP librry is indexed by the set of ll the possible intentions for the gent. Syntx of Elementry Plns. Ech elementry pln is specified by pir composed of nme to identify it nd n AgLOTOS expression to feture its behvior. Consider tht the nmes of elementry plns re rnged over P, Q,... nd tht the set of ll the possible behvior expressions is denoted E, rnged over E, F,... The AgLOTOS elementry expressions re written by composing (observble) ctions through the LOTOS opertors. The syntx of n elementry pln P is defined inductively s follows: P ::= E E ::= exit stop ; E E E ( O) hide L in E Elementry pln H ::= move(l) (H O, l Θ) x!(ν) x?(ν) (x Λ, ν M) = {, [L],, [ ],, [> } The expression of n elementry pln refers to (finite) set O of observble ctions which re prcticlly described s instntited predictes, below rnged over, b,... This set includes the subset H of the so-clled AmI primitives which represent the mobility nd communiction, bsed on the two following ssumptions bout the AmI system: (1) every gent cn perceive the enter nd leve of other gents in the AmI system, (2) it cn suggest some move between the AmI system loctions nd (3) it cn communicte with nother gent in the system. In the syntx, the primitive move(l) is used to represent the move of the ssisted user to some loction l (l Θ, finite set of loctions). The ction x!(ν) specifies the emission to the gent x (x Λ, the set of neighbor gents) of the messge ν (ν M, the set of possible messges), wheres, the expression x?(ν) mens tht the messge ν is received from some gent x. In ddition, two non-observble ctions re lso introduced, so tht the totl set of ctions is denoted Act = O {, δ}, where / O is the internl ction nd δ / O is prticulr observble ction which fetures the successful termintion of the considered elementry pln. The bsic expression stop specifies pln behvior without possible evolution nd exit represents the successful termintion of some pln. In the syntx, the set represents the stndrd LOTOS opertors: E [ ] E specifies non-deterministic choice, hide L in E hiding of the ctions of L tht pper in E with L being ny subset of O, E E sequentil composition nd E [> E the interruption. The LOTOS prllel composition, denoted E [L] E cn model both synchronous composition, E E if L = O, nd synchronous composition, E E if L =. In fct, the AgLOTOS lnguge exhibits rich expressivity such tht the sequentil executions of plns ppers to be only prticulr cse. Syntx of Agent Plns. The building of n gent pln requires dding the following AgLOTOS opertors to compose some elementry plns:

6 t the gent pln level, the prllel nd the sequentil composition opertors re used to build n gent pln from the intentions of the gent nd the ssocited weights. the lternte composition opertor, denoted, llows to specify n lternte of elementry plns. In prticulr, n intention is stisfied iff t lest one of the ssocited elementry plns is successfully terminted. Let P be the set of nmes used to identify the possible intention plns: P P nd let P be the set of nmes qulifying the possible gent plns: P P. P ::= P P P Intention pln P ::= P P P P P Agent pln With respect to the set of intentions I of the gent, the gent pln is formed in two steps: (1) by n extrction mechnism of elementry plns from the LibP librry, (2) by using the composition functions clled options nd pln: options : I P, yields for ny i I, n intention pln of the form P i = P libp (i) P. pln : 2 I P, cretes the finl gent pln P from the set of weighted intentions I. Depending on how I is ordered, the intention plns yielded by the different mppings P i = options(i) (i I) re composed by using the AgLOTOS composition opertors nd. The function weight : I N tht defines the weights of the intentions, in fct yields the wy to compose the corresponding intention plns. The intention plns corresponding to the sme weight re composed by using the concurrent prllel opertor. In contrst, the intention plns corresponding to distinct weights re ordered by using the sequentil opertor. For instnce, let I = {i 1 0, i 2 1, i 1 2, i 0 3} be the considered set of intentions, such tht the superscript informtion denotes weight vlue, nd let P 0, P 1, P 2, P 3 be their corresponding intention plns, the constructed gent pln could be viewed (t pln nme level) s: pln(i) = P 1 ( P 0 P 2 ) P 3. A Simple AmI Exmple. Let us consider the following AmI scenrio presented in [7], where Alice nd Bob re two users of some University, ech one ssisted by HoA softwre gent. The proposed problem of Alice is tht she cnnot mke the two following tsks in the sme time: (1) to meet with Bob in the loction l 1, nd (2) to get her exm copies from the loction l 2. Clerly, the Alice s desires re conflicting since Alice cnnot be in two distinct loctions simultneously. However, fter hving perceived tht Bob is in l 2, mening in the sme loction s the exm copies, Alice sks for his help to bring her the copies. The intentions of Alice nd Bob re specified seprtely within their respective gents. These lst ones cn pervsively coordinte to help chieving the intentions of their ssisted users. Here, the ctions in plns re simply expressed by using instntited predictes, like get copies(l 2 ). Intention plns re composed from elementry plns which re viewed s concurrent processes, terminted by exit, l LOTOS.

7 Alice s scenrio I A = {meeting(bob, l 1), sking(bob, get copies(l 2))} P A = Bob!(get copies(l 2)); exit meet(bob); exit Bob s scenrio I B = {meeting(alice, l 1), getting copies(l 2)} P B = get copies(l 2); exit move(l 1); meet(alice); exit The mentl process of n HoA gent cn order its set of intentions, ccording to some preferences of the ssisted user. For instnce, the intention set relted to Alice I A = {meeting(bob, l 1 ), sking(bob, get copies(l 2 ))} cn be ordered such tht weight(meeting(bob, l 1 )) < weight(sking(bob, get copies(l 2 )). The corresponding gent pln expression of Alice is: P A = Bob!(get copies(l 2 )); exit meet(bob); exit, which is built by using the options nd pln mppings. Py ttention tht some ctions cn be processed concurrently, so is the cse in the gent pln P B, for the intention plns get copies(l 2 ); exit nd move(l 1 ); meet(alice); exit. 3 Contextul Plnning System We show now how to build the Contextul Plnning System, denoted CPS for short, from the specifiction of n gent pln. It is trnsition system representing ll the possible evolutions of the pln. The building of these lst ones re formlly driven by semntics of AgLOTOS constrined by contextul informtion. As service instnce tht cn be defined t the plnning process level, guidnce mechnism is defined, tht works over the evolutions represented by the CPS. Building of the Contextul Plnning System. The AgLOTOS opertionl semntics is bsiclly derived from the one of LOTOS. A pir (E, P ) represents process identified by P, such tht its behvior expression is E. Bsic LOTOS semntics is detiled in [7] which formlizes how process cn evolve under the execution of ctions. In prticulr, the rule P :=E E E, specifies how P E (E, P ) pir is chnged to (E, P ) under ny ction. Actully, P := E mens to consider ny (E, P ) source pir nd P E mens chnging E to E for P under the execution of. As fr s AgLOTOS is concerned, these rules lso represent the opertionl semntics of elementry plns, viewed s processes. The next definition specifies how the expression of n gent pln is formed compositionlly from the expressions of the intentions plns of the gent, themselves built from n lternte of elementry plns nd their behvior expressions. With respect to some gent pln P, we introduce notion of configurtion of plns in order to specify tht prt of the pln cn lredy be executed. Further, the nottion [P ] represents the configurtion of the gent pln P, it is n AgLO- TOS expression, which is obtined by composition of the different intention pln configurtions of the gent, like (E, P ).

8 Definition 1. Any gent pln configurtion [P ] hs generic representtion defined by the following two rules: P ::= P P ::= k=1..n P k P k ::=E k [P ]::=( k=1..n E k, P ) P ::=P 1 P 2 {, } [P ]::=[P 1 ] [P 2 ] The plnning stte of the gent is now defined contextully, tking into ccount the gent loction nd the termintion informtion bout the different intention plns defined for the gent. Definition 2. A (contextul) plnning stte is tuple (C, l, T ), where C is n gent pln configurtion [P ], l corresponds to n expected loction for the gent, nd T is the subset of intention plns which will be terminted in this stte. Tble 1. Semntic rules of intention nd gent pln configurtions Intention pln level (Action) E E (E, P ) O {} (E, P ) E (E, P ) δ E (E, P ) Agent pln level (Action) (Communiction) (Mobility) (Sequence) (Prllel) C C (C,l,T ) C x!(ν) C (C,l,T ) O {} (C,l,T ) x!(ν) x Λ (C,l,T ) C move(l ) C l l (C,l,T ) move(l ) (C,l,T ) C 1 C 1 C 1 C 2 O {} C 1 C 2 C 1 C 1 O {} C 1 C 2 C 1 C 2 C 1 C 1 O {} C 2 C 1 C 2 C 1 (C,l,T ) C C C (C,l,T { P }) x?(ν) C (C,l,T ) x?(ν) x Λ (C,l,T ) C C move(l) (C,l,T ) (C,l,T ) C 1 C 1 C 2 C 1 C 1 C 2 C 1 C 2 C 1 C 1 C 1 C 2 C 1 C 1 C 2 C 1 C 2 C 1 Tble 1 shows the opertionl semntic rules defining the possible plnning stte chnges for the gent. These rules re pplied to produce the CP S, from n initil plnning stte, e.g. ([P ], l, ), mening tht the gent is initilly t

9 loction l, nd its pln configurtion is [P ]. There re two kinds of trnsition rules: Intention pln level: When n intention pln is ssumed to be treted, the left hnd side trnsition (C 1,, P, C 2 ), denoted C 1 C 2, expresses chnge of intention pln configurtion, from C 1 to C 2, nd ssumes the execution of the ction from E E nd P := E. The right hnd side trnsition highlights the termintion cse, keeping trce of the intention pln P tht is going to be terminted. By clling CN the set of ll the possible intention pln configurtions for the gent, the trnsition reltion is subset of CN O {} P CN. For ske of clrity, the trnsition (C 1,, nil, C 2 ) is simply denoted C 1 C 2. Observe tht due to the fct we consider predictive guidnce in this pper, only expected successful executions re tken into ccount, thus bstrcting tht pln my fil. Moreover, the semntics of the lternte opertor is reduced to simple non-deterministic choice of LOTOS: k=1..n E k [ ] k=1..n E k, in order to possibly tke into ccount every elementry pln to chieve the corresponding intention. Agent pln level: the possible chnges of the plnning sttes, like (C, l, T ), re expressed t this level. In the Communiction rules, the ction send x!(ν) (resp. receive x?(ν)) is constrined by the discovery of the gent x in its neighborhood. In the Mobility rule, the effect of the move(l ) ction yields the gent to be plced in l. The Action rules refer to the ones of the intention pln level. The left hnd side one exhibits the cse of regulr ction, wheres the right hnd side one specifies the termintion cse of some intention pln, which is dded to T. The building of the CPS tkes the three following contextul informtion into ccount: (1) the reched loction in plnning stte, (2) the set of intention plns tht re terminted when reching plnning stte, nd (3) more globlly, the set Λ of neighbors currently known by the gent. Definition 3. Let I be set of weighted intentions for the gent. The Contextul Plnning System (CP S) is lbeled kripke structure S, s 0, T r, L, T where: S is the set of (contextul) plnning sttes, s 0 = ([P ], l, ) S is the initil plnning stte of the gent, such tht [P ] is the gent pln configurtion of the gent nd l represents its current loction, T r S O {} S is the set of trnsitions which re denoted s s, L : S Θ is the loction lbeling function, T : S 2 P is the termintion lbeling function which cptures the terminted intention plns. Appliction to the scenrio. We reconsider the scenrio of Section 2. The pirs (E m, P m ) nd (E g, P g ) re two intention pln configurtions corresponding to Bob. The first one corresponds to the intention meeting(alice, l 1 ) nd the second one to getting copies(l 2 ), such tht E m = move(l 1 ); meet(alice); exit nd E g = get copies(l 2 ); exit.

10 getc s 0 {l 2 } move {l 2 } s 1 move getc s 2 {l 1 } meeting {l 2, P 1 } s 3 s 4 {l 1 } s 5 {l 1 } meeting getc move {l 1, P 1 } s 6 s 7 {l 1 } s 8 getc meeting {l 1, P 1 } s 9 s 10 {l 1, P 2 } {l 1, P 2 } s 11 {l 1, P 1, P 2 } Fig. 3. The CP S B corresponding to the gent pln P B The CPS corresponding to Bob, denoted CP S B, is illustrted in Figure 3. It is built from the initil CPS stte, s 0 = ([P B ], l 2, ), tking into ccount the current loction l 2 of Bob. In the figure, the dshed edges represent the unrelizble trnsitions from the sttes s {s 2, s 5, s 8 }, becuse pre(getc) = l 2 L(s). In CP S, the trnsitions from ny stte s only represent ctions tht re relizble. Like in STRIPS description lnguge [3], ctions to be executed re modeled by instntited predictes submitted to preconditions nd effects. In this pper, the preconditions only concern the contextul informtion known in tht stte. Let pre() be the precondition of ny ction, then pre(x!(ν)) = pre(x?(ν)) = (x Λ) nd for ny other ction, pre((l)) = l L(s). Plnning Guidnce. In order to guide the ssisted user, the plnning process cn select n execution trce through the CP S such tht the number of intention pln termintions is mximized, in respect to the mpping T of the plnning sttes. This cn be cptured with the notion of Mximum trce, bsed on trce mpping end : Σ 2 P used to specify the set end(σ) of the termintion ctions tht occur in trce σ Σ. From n lgorithmicl point of view, the configurtions hving the mximum number of terminted intention plns could be strightforwrdly detected by prsing the CP S structure, with regrds to the set of terminted intention plns of ech built plnning stte. By lbeling these sttes with specific proposition MAX, the serch of mximum trces is reduced to the trces which stisfies the (LTL) temporl logic property AF(MAX). Considering gin CP S B corresponding to Bob, n exmple of mximum trce derived from s 0 is the following, expressing tht Bob should get the copies before moving to the meeting with Alice: ((E g, P g) (E m, P m), l 2, ) getc ((E g, P g) (E m, P m), l 2, ) ((E m, P m), l 2, { P g}) P g move ((E m, P m), l 1, { P g}) meet ((E m, P m), l 1, { P g}) ((stop, P m), l 1, { P g, P m}) P m

11 4 Experimenttion: The Smrt-Cmpus Project We experiment our gent-bsed pproch in distributed system project clled Smrt-Cmpus. Our im is to design powerful system tht ssists users in their ctivities within complex university cmpus to better interct nd dpt to users needs nd demnds. This project is in progress but we concretely equip flot of Android Smrt-Devices 3 (SD) by the smrt-cmpus ppliction. In this ppliction, the softwre rchitecture is composed of n HoA gent nd specific grphicl user interfce (GUI) to interct with the user to be ssisted. Hence, this llows us n explicit presenttion of the resoning of n gent nd the concrete use of the guidnce service driven by the mentl process, ccording to the chnge of context process informtion. From smrt-cmpus rchitecture, we now scheme the deployment of the smrt-cmpus ppliction in the SD, nd the wy to develop the HoA gent min processes. Fig. 4. Smrt-cmpus rchitecture Smrt-Cmpus Architecture. The cmpus system is concretized by the smrtcmpus strting service which utomticlly runs the smrt-cmpus ppliction nd connects the SD to the "CAMPUS" network, through one of the possible WiFi Access Points (AP). As illustrted in Figure 4, the SD cn utomticlly ccess to the server SC Directory which is viewed s middlewre mintining the persistence of contextul informtion like the discovery nd the loctions of other users (through their SD) nd objects concerning the cmpus. The strting service is lso dedicted to declre the public informtion of the user to the server, in prticulr its loction. One of the specificity of this project is tht the HoA gent embedded in the SD remins utonomous when the SC directory cnnot be reched or when the user is exiting the cmpus. It cn continue ssisting the user, due to the context informtion nd persistent dt previously stored in the SD, cn be pervsively updted with the help of other neighbor gents. Context Process. The context process is bsed on services currently implemented over the smrt-cmpus rchitecture, bsed on physicl locliztion nd ()synchronous communiction mechnisms. They re supported by the smrtdevice API fcilities, in prticulr the WiFi API. As n exmple, the nvigtion 3 Devices: Google Nexus 5, 7 Android 4.4 KitKt (API level 19).

12 service tkes profit from the underlyied locliztion service to determine on the fly, the position nd the move of the ssisted user. Observe tht the locliztion service must work over the cmpus ground s well s the different stirs of the buildings. The best locliztion indoor technique is currently reserch in progress e.g. [8]. Currently, we use different WiFi ccess points within the cmpus to compute the geogrphicl loctions, since this works in both indoor nd outdoor loctions. Anywy, the locliztion process requires tune clibrtion phse to store specific informtion in the SC directory, concerning set of physicl reference points tht must be selected over the cmpus, s mentioned in the fingerprinting pproch. In our cse, informtion includes the physicl loction of the reference point (GPS), its symbolic nme (plce/room/corridor) nd bove ll the perceived signl ttenution (RSSI 4 ) from tht loction, in respect to the different WiFi ccess points. The locliztion service on the SD cn then compre its proper perceptions of the WiFi ttenution in respect to the sme references stored in the SC directory, so tht to deduce n pproximtion of its position through sttisticl computtions nd triltertion concepts. Mentl nd Plnning Processes To interct with the ssisted user, the GUI is n importnt issue of our ppliction. Figure 5 brings out n instnce of three relevnt screenshots of the developed GUI. Bob is here the ssisted user, being notified on his SD in rel time, of the evolution of its intentions, its current loction nd the (best) direction to meet Alice. Fig. 5. Smrt-cmpus scenrio The first one (left hnd side) shows the current weighted intentions mnged by the mentl process, coming from the ssisted user desires or the pervsive ctivity of the HoA gent. The second screen is debug view showing the gent pln nd ll the possible CPS trces. The contextul guidnce service llows the gent to ssist 4 RSSI: Received Signl Strength Indiction

13 the user in relizing his desires in proposing different lterntives of plns, optionlly inducing the proposition of sptil pths. The lst screen (right hnd side) highlights the used nvigtion interfce showing globl view of the cmpus during the execution of the Bob s scenrio. As specific GUI, grphicl mps re modern nd useful interfces for the users. The ppliction is ble to mnge the mps of the cmpus, over which dditionl lyers re used to render mps interctive nd to show different loctions nd pths. 5 Conclusion The lgebric lnguge AgLOTOS ppers to be powerful wy to express n AmI gent pln s set of concurrent processes, helped by n dpted pln librry describing elementry plns. The proposed opertionl semntics of AgLOTOS llows one to build Contextul Plnning System (CPS), for ny BDI stte of the gent. In respect to the current set of the gent intentions, the CPS structure llows to evlute ll the possible plns. Despite the concurrent execution of plns, the predictive mechnism we propose, llows to guide the gent contextully, over its next possible executions. The problem to serch n optiml solution mximizing the number of (sub) plns to be executed, is reduced to rechbility problem over the CPS structure. In the smrt cmpus project, the presented forml predictive technique is pplied to ssist users in their dily ctivities, bsed on bsic contextul informtion corresponding to sptil loction nd dynmic neighborhood. Hence, it cn lso be viewed s concrete sptil guidnce over the cmpus. References [1] Olru, A., Flore, A.M., El Fllh Seghrouchni, A.: A context-wre multi-gent system s middlewre for mbient intelligence. MONET 18(3) (2013) [2] Srdin, S., de Silv, L., Pdghm, L.: Hierrchicl plnning in BDI gent progrmming lnguges: forml pproch. In: AAMAS 06. (2006) [3] Meneguzzi, F., Zorzo, A.F., d Cost Mór, M., Luck, M.: Incorporting plnning into BDI gents. Sclble Computing: Prctice nd Experience 8 (2007) [4] Chouche, A.C., El Fllh Seghrouchni, A., Ilié, J.M., Sïdouni, D.E.: A dynmicl pln revising for mbient systems. Procedi Computer Science 32 (2014) [5] Ro, A.S., Georgeff, M.P.: An bstrct rchitecture for rtionl gents. In Nebel, B., Rich, C., Swrtout, W.R., eds.: KR, Morgn Kufmnn (1992) [6] Brinksm, E., ed.: ISO 8807, LOTOS - A Forml Description Technique Bsed on the Temporl Ordering of Observtionl Behviour. (1988) [7] Chouche, A.C., El Fllh Seghrouchni, A., Ilié, J.M., Sïdouni, D.E.: A Higherorder Agent model for mbient systems. Procedi Computer Science 21 (2013) [8] Glvn-Tejd, C.E., Grc-Vzquez, J.P., Grc-Cej, E., Crrsco-Jimnez, J.C., Bren, R.F.: Evlution of four clssifiers s cost function for indoor loction systems. Procedi Computer Science 32(0) (2014)