Personalisation in Mobile Environment Based on papers and presentations of Catholijn Jonker, Vagan...

Personalisation in Mobile Environment

Based on papers and presentations of Catholijn Jonker, Vagan Terziyan, Jan Treur, Oleksandra Vitko and others

MIT Department, University of Jyväskylä

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Based on: http://www.cs.jyu.fi/ai/papers/CIA-2003-2.pdf

Catholijn Jonker Department of Artificial Intelligence Vrije Universiteit Amsterdam (the Netherlands) [email protected] http://www.cs.vu.nl/~jonker

Vagan Terziyan Department of Mathematical Information Technology University of Jyväskylä (Finland) [email protected] http://www.cs.jyu.fi/ai/vagan

Jan Treur Department of Artificial Intelligence Vrije Universiteit Amsterdam (the Netherlands) [email protected] http://www.cs.vu.nl/~treur

Jonker C., Terziyan V., Treur J., Temporal and Spatial Analysis to Personalize an Agent’s Dynamic Belief, Desire and Intention Profiles, In: M. Klush et al. (eds.), Cooperative Information Agents VII: Proceedings of the 7-th International Workshop on Cooperative Information Agents (CIA-2003), Helsinki, Finland, August 27-29, 2003, Lecture Notes in Artificial Intelligence, V. 2782, Springer-Verlag, pp. 289-315.

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Agent is Part of Intentional System

An agent is considered as a part of intentional system and thus it is an entity which appears to be the subject of beliefs, desires, intentions, etc;

An agent is assumed to decide to act and communicate based on its beliefs about its environment and its desires and intentions. These decisions, and the intentional notions by which they can be explained and predicted, generally depend on circumstances in the environment, and, in particular, on the information on where and when these circumstances acquired.

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Where We Might Need Spatial Considerations in an Intentional System ?

… in many applications where agent’s intentional profile essentially depends on its location in the environment, e.g.: for adaptive location-based “Push” services for

mobile customers; for intelligent tracking of terrorists; … etc.

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Adaptive Location-Based “Push” Services for Mobile Customers

It will be very helpful to have capabilities to predict in which places of the environment certain desires or intentions of a customer are likely to arise, to stimulate the arising of these intentions by providing the occurrence of circumstances that are likely to lead to them.

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Intelligent Tracking of e.g. Terrorists, etc.

Also we assume that it may be very helpful to have capabilities to predict in which places of the environment certain inappropriate desires or intentions are likely to arise, either: to avoid the arising of these intentions by preventing

the occurrence of circumstances that are likely to lead to them, or

if these circumstances cannot be avoided, by anticipating consequences of the intentions.

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Basic Ontologies Used Actual state of the external world: EWOntEWOnt ;

Observation observation_result(p) and communication (communicated_by(p,

C) ) input of an agent: InOnt InOnt ;

Output of an agent (decisions to do actions): OutOnt OutOnt ;

Agent internal ontology: IntOnt IntOnt ;

Agent interface ontology: InterfaceOntInterfaceOnt = InOntInOnt OutOntOutOnt ;

Agent ontology: AgOntAgOnt = InterfaceOntInterfaceOnt IntOntIntOnt ;

Overall ontology: OvOntOvOnt = EWOntEWOnt AgOnt AgOnt .

p – a state property of the external world;C – an agent who provides information about state property.

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Overall Trace The set of possible states of the overall ontology: IS(OvOntOvOnt).

An overall trace M over a time frame T is a sequence of states over the overall ontology OvOntOvOnt over time frame T: (Mt)t T in IS(OvOntOvOnt) .

States of agent A input / output interfaces and internal state at time t, given an overall trace M:

• state(M , t, input(A));• state(M , t, output(A));• state(M , t, internal(A)).

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Temporal Belief Statement

An agent believes a fact if and only if it received input about it in the past and the fact is not contradicted by later input of the opposite.

M W t1 [ ((M M , t1), t1)

t0 ≤ t1 [ Input(, t0, M ) t [t0, t1] Input(~, t, M ) ] ]

Temporal belief statement

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Desires

Given a desire, for each relevant action there is an additional reason, so that if both the desire is present and the agent believes the additional reason, then the intention to perform the action will be generated.

Every intention is based on a desire, i.e., no intention occurs without desire

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Intentions and Actions(temporal vs. spatial)

Under appropriate circumstances an intention leads to an action:

• an agent who intends to perform an action will execute the action immediately when immediately when an opportunity occurs;

• an agent who intends to perform an action will execute the action at the nearest known placeat the nearest known place where an opportunity occurs.

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Satisfaction Relation

is true in this state at time t:

state(M, t, input(A)) |= ,

where SPROP(InOntInOnt)

state properties based on ontology

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Spatial Language Elements

Location (x, y) has property p: location_has_property(x, y, p) ;

Agent A is present at location (x, y): location_has_property(x, y, present (A)) ;

For example: state(M, t, input(A)) |=

observation_result(location_has_property(x, y, p))

means that at time t the agent A's input has information that it observed that location (x, y) has property p.

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A Route

A route RR is defined as a mapping from distances d (on the route) to locations (x, y),

e.g., after 300 m on the route home_from_school you are at location (E, 5) on the map.

at_ distance_at_location (R, d, x, y)

at_ distance_at_location (home_from_school , 300, E, 5)

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Associated Route of “Walking” Agent A trace M of an agent walking in a city specifies an

associated route R(M) as follows: at route after distance d you are at location (x, y) if and only if a time point t exists such that at t agent A has walked d from the start and is present at location (x, y).

at_ distance_at_location (R(M), d, x, y) t [ state(M, t, EW) |= distance_from_start(d) location_has_property(x, y, present(A)) ]

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Temporal vs. Spatial Factors (Case 1) Person A (1981-1986 M.Sc. studies on Applied

Mathematics; 1987-2000 – Ph.D. studies on Artificial Intelligence; 2001-2002 – Project Work on Ontology Engineering);

Person B (M.Sc. studies on Applied Mathematics in University of Jyvaskyla; Ph.D. studies on Artificial Intelligence in Massachusetts Technological Institute; Project Work on Ontology Engineering in Vrije Universiteit Amsterdam).

spatial history here (i.e. second description) seems to be more informative than temporal history in a reasonable context.

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Temporal vs. Spatial Factors (Case 2)

Person A (10:00 wants a cup of coffee; 15:00 wants to eat; 19:00 wants to watch TV News; 23:00 wants to sleep);

Person B (wants a cup of coffee in train “Jyvaskyla-Helsinki” near Pasila station; wants to eat in Helsinki University Conference Room; wants to watch TV News in the Irish Pub in Downtown Helsinki; wants to sleep in “Scandic” Hotel).

alternatively temporal history here (i.e. first description) seems to provide more information about a person than the spatial one in a reasonable context.

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Predicting Agent’s States based on Spatial History

M1

M2

M3

M4

M5

Y

prediction of place and state X

Mnew

Given - set of routes M for the agent with observed agent state in different location points of each route; task – online prediction of agents next locations, BDI attributes and states for a new route.

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Relationships between BDI Notions

Preferences

Track ofAgent’s Routes

and States

IntentionsDesires

Beliefs

Actions

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Mobile Commerce (Location-Based Service)

Agent – mobile customer. Agent’s location – can be tracked by

positioning infrastructure. Observable agents actions – e.g. clickstream

(points of interest) on a map delivered to the mobile terminal, calls and downloads of information about points of interest, appropriate orders, reservations, payments, etc. - can be tracked by Location-Based Service (LBS).

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Multimeetmobile Project (2000-2001)

Information TechnologyResearch Institute(University of Jyvaskyla):Customer-oriented research anddevelopment in Information Technology

http://www.titu.jyu.fi/eindex.html

Multimeetmobile (MMM) Project(2000-2001):Location-Based Service System and TransactionManagement in Mobile Electronic Commerce

http://www.cs.jyu.fi/~mmm

Academy of FinlandProject (1999):Dynamic Integration ofClassification Algorithms

Mobile Location-Based Service (advanced personalization)

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M-Commerce LBS systemhttp://www.cs.jyu.fi/~mmm

In the framework of the Multi Meet Mobile(MMM) project at the University of Jyväskylä,a LBS pilot system, MMM Location-basedService system (MLS), has been developed.MLS is a general LBS system for mobileusers, offering map and navigation acrossmultiple geographically distributed servicesaccompanied with access to location-basedinformation through the map on terminal’sscreen. MLS is based on Java, XML and usesdynamic selection of services for customersbased on their profile and location.

Virrantaus K., Veijalainen J., Markkula J.,Katasonov A., Garmash A., Tirri H., Terziyan V.,Developing GIS-Supported Location-BasedServices, In: Proceedings of WGIS 2001 - FirstInternational Workshop on Web GeographicalInformation Systems, 3-6 December, 2001, Kyoto,Japan, pp. 423-432.

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A d a p t i v e i n t e r f a c e f o r M L S c l i e n t

O n l y p r e d i c t e d s e r v i c e s , f o r t h e c u s t o m e r w i t h k n o w n p r o f i l ea n d l o c a t i o n , w i l l b e d e l i v e r e d f r o m M L S a n d d i s p l a y e d a tt h e m o b i l e t e r m i n a l s c r e e n a s c l i c k a b l e “ p o i n t s o f i n t e r e s t ”

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Route-based personalization

Static Perspective Dynamic Perspective 2 2

I n d u c t i v e l e a r n i n g o f c u s t o m e rp r e f e r e n c e s w i t h i n t e g r a t i o n o f p r e d i c t o r s

rrmrr yxxx ,...,, 21

S a m p l e I n s t a n c e s

tmtt xxx ,...,, 21

y t

L e a r n i n g E n v i r o n m e n t

P 1 P 2 . . . P n

P r e d i c t o r s / C l a s s i f i e r s

T e r z i y a n V . , D y n a m i c I n t e g r a t i o n o f V i r t u a l P r e d i c t o r s , I n : L . I . K u n c h e v a , F .S t e i m a n n , C . H a e f k e , M . A l a d j e m , V . N o v a k ( E d s ) , P r o c e e d i n g s o f t h e I n t e r n a t i o n a l I C S CC o n g r e s s o n C o m p u t a t i o n a l I n t e l l i g e n c e : M e t h o d s a n d A p p l i c a t i o n s - C I M A ' 2 0 0 1 , B a n g o r ,W a l e s , U K , J u n e 1 9 - 2 2 , 2 0 0 1 , I C S C A c a d e m i c P r e s s , C a n a d a / T h e N e t h e r l a n d s , p p . 4 6 3 - 4 6 9 .

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Architecture of LBS system

Mobilenetwork

Geographical, spatial data

Positioning Service

Location-BasedService

Personal Trusted DeviceLocation-based data:

(1) services database (access history);(2) customers database (profiles)

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Positioning systems

GPS RECEIVER

Satelliteposition

(Sxi, Syi, Szi)

Antenneposition

(Ux, Uy, Uz)

Ri

Satellite-based positioning Base Station

Base Station

Base Station

Measurement of

error margin

Cellular network based positioning

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Opening a Connection to Location Service

Internet_____________

MailWWWMMM Map Service

SettingsSelect

MMM Map Service

MMMMap Service

Login ********Password ********

AcceptCancel

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Request and Receive Map from the Location Service

Welcome to

MMMMap Service

Request mapExit

MMMMap Service

Map deliveredPress OK button !

OKCancel

MMM Map Service

UpdateClose Zoom

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Selecting Point of Intereston the Map

MMM Map Service

UpdateClose Zoom

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Receiving Information Content Related to Point of Interest

MMMMap Service

Content deliveredPress OK button !

OKCancel

MMM Map Service

CallReturn to map

Hotel Alba ***

Address: Mattillaniemi A1Tel. GSM: 0504563872Rooms available:

Single: 380 FIMDouble: 450 FIM

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Contextual and Predictive Attributes

featureslocation

imim

featuresprofile

ii xxxx

1

21 ,...,... ,,

iy

Mobile customerdescription

Ordered service

Contextualattributes

Predictiveattributes

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Challenges of prediction in mobile environment

Main Subtasks in prediction process Feature selection Distance evaluation Classification (Prediction)

Goal of classification (prediction) in mobile e-commerce environment Given: customer’s profile and location features Goal: predict customer’s next order (based on prediction oh his

beliefs, desires, intentions, etc.)

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Feature Selection: to find the minimally sized feature subset that is sufficient for correct classification of the instance

Fea

ture

Sel

ecto

r

Sample InstancesSample Instances

rr yΧrr

' ΧΧΧ ,'rr y

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Distance Evaluation: to measure distance between instances based on their numerical or nominal attribute values

Distance Evaluator

imii xxx ,...,, 21 jmjj xxx ,...,, 21

ijd

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Distance between Two Instances with Heterogeneous Attributes (e.g. Preferences)

YyXxi

iii

ii

yxdYXD,,

2),(),(

i

ii

ii

ii

range

yx

yxi

yxd

:else

otherwise ,1

if ,0 - nominal is attributeth if

),(

where:

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Simple distance between Two Preferences with Heterogeneous Attributes (Example)

YyXxi

iii

ii

yxdYXD,,

2),(),(

i

ii

ii

ii

range

yx

yxi

yxd

:else

otherwise ,1

if ,0 - nominal is attributeth if

),(

where:

d (“white”, “red”) = 1

d (15°, 25°) = 10°/((+30°)-(+10°)) = 0.5

Wine Preference 1:

I prefer white wine served at 15° C

Wine Preference 2:

I prefer red wine served at 25° C

Importance:Wine color: ω1 = 0.7

Wine temperature: ω2 = 0.3

D (Wine_preference_1, Wine_preference_2) = √ (0.7• 1 + 0.3 • 0.5) ≈ 0.922

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Advanced distance between Two Preferences with Heterogeneous Attributes (Example) - 1

YyXxi

iii

ii

yxdYXD,,

2),(),(

where:

P(wine|colour = white) =

= 100 / 500 = 0.2

P(wine|colour = red) =

= 200 / 300 = 0.67

numerical. is attributeth if,||

nominal; is attributeth if,)]|()|([

),( 1

2

irange

yx

iycPxcP

yxd

i

ii

C

cii

ii

Domain objects: 1000 drinks;300 red, 500 white, 200 - other

Soft drinks: 600;100 red, 400 white, 100 - other

Wines: 400;200 red, 100 white, 100 - other

P(soft_drink|colour = white) =

= 400 / 500 = 0.8

P(soft drink|colour = red) =

= 100 / 300 = 0.33

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Advanced distance between Two Preferences with Heterogeneous Attributes (Example) - 2

YyXxi

iii

ii

yxdYXD,,

2),(),(

where:

P(wine|colour = white) = = 100 / 500 = 0.2

P(wine|colour = red) = = 200 / 300 = 0.67

numerical. is attributeth if,||

nominal; is attributeth if,)]|()|([

),( 1

2

irange

yx

iycPxcP

yxd

i

ii

C

cii

ii

P(soft_drink|colour = white) = = 400 / 500 = 0.8

P(soft drink|colour = red) = = 100 / 300 = 0.33

d (“white”, “red”) = √ [(P(soft_drink|colour = white) - P(soft drink|colour = red) )2 + + (P(wine|colour = white) - P(wine|colour = red) )2 ] = = √ [(0.8 – 0.33 )2 + (0.2 – 0.67 )2 ] ≈ 0.665

D (Wine_preference_1, Wine_preference_2) = √ (0.7• 0.665 + 0.3 • 0.5) ≈ 0.784

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Classification (Prediction): to predict class for a new instance based on its selected features and its location relatively to sample instances

Classification Processor

imii xxx ,...,, 21

iy

Sample Instances

Feature Selector

Distance Evaluator

37

Reed more in:Terziyan V., Dynamic Integration of Virtual Predictors, In: L.I. Kuncheva, F. Steimann, C. Haefke, M. Aladjem, V. Novak (Eds), Proceedings of the International ICSC Congress on Computational Intelligence: Methods and Applications- CIMA'2001, Bangor, Wales, UK, June 19 - 22, 2001, ICSC Academic Press, Canada/The Netherlands, pp. 463-469.

http://www.cs.jyu.fi/ai/papers/virtual_predictors.pdf

Puuronen S., Tsymbal A., Terziyan V., Distance Functions in Dynamic Integration of Data Mining Techniques, In: B.V. Dasarathy (ed.), Data Mining and Knowledge Discovery: Theory, Tools and Technology II, Proceedings of SPIE, Vol.4057, The Society of Photo-Optical Instrumentation Engineers, USA, 2000, pp. 22-32.

http://www.cs.jyu.fi/~alexey/distance.pdf

Skrypnik I., Terziyan V., Puuronen S., Tsymbal A., Learning Feature Selection for Medical Databases, In: Proc. of the 12th IEEE Symposium on Computer-Based Medical Systems CBMS'99, Stamford, USA, June 1999, IEEE CS Press, pp. 53-58.

http://dlib.computer.org/conferen/cbms/0234/pdf/02340053.pdf

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Prediction of Customer’s Actions

d1 d2

d3

d4

d5

here I washed my car

here I had nice wine here I had massage

here I had great pizza

here I made hair

I am here now.There are my recent preferences:1. I need to wash my car: 0.12. I want to drink some wine: 0.23. I need a massage: 0.24. I want to eat pizza: 0.85. I need to make my hair: 0.6 Make a guess what I will order now and where !

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Contextual Effect on Conditional Probability (1)

XX x1 x2 x3 x4 x5 x6 x7

predictive attributes contextual attributes

xk xr

Assume conditional dependence between predictive attributes

(causal relation between physical quantities)…

xt

… some contextual attribute may effect

directly the conditional dependence between

predictive attributes but not the attributes itself

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xk xr

xt

Xk1 : order flowers

Xk2 : order wine

Xr1 : visit football match

Xr2 : visit girlfriend

P1(Xr |Xk ) Xk1 Xk2

Xr10.3 0.9

Xr20.4 0.5

P2(Xr |Xk ) Xk1 Xk2

Xr10.1 0.2

Xr20.8 0.7

Xt1 : I am in Paris

Xt2 : I am in Moscow

Xk : Order present Xr : Make a visit

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X

Y

P(X)

P(Y)-? P(P(Y|X)|Z)

Z

P(Z) P(Y|X)

pk(Y|X)

P(P(Y|X))

•X ={x1, x2, …, xn} – predictive attribute

with n values;•Z ={z1, z2, …, zq} – contextual attribute

with q values;•P(Y|X) = {p1(Y|X), p2(Y|X), …, p r(Y|X)} –

conditional dependence attribute (random variable) between X and Y with r possible values;•P(P(Y|X)|Z) – conditional dependence between attribute Z and attribute P(Y|X);

})]|)|()|(()([

)()|({)(

1

1 1

q

mmkm

r

k

n

iiijkj

zZXYpXYPPzZP

xXPxXyYpyYP

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Contextual Effect on Unconditional Probability (1)

XX x1 x2 x3 x4 x5 x6 x7

predictive attributes contextual attributes

xk

Assume some predictive attribute is a random

variable with appropriate probability distribution

for its values…

xt

… some contextual attribute may effect

directly the probability distribution of the predictive attribute

x1 x2 x3x4

XXkk

P(XP(Xkk))

44

Contextual Effect on Unconditional Probability (2)

xk

xt

XXkk

PP11(X(Xkk))

Xk1 Xk2

0.2

0.7

XXkk

PP22(X(Xkk))

Xk1 Xk2

0.50.3

Xt1 : I am in Paris


Xk1 : order flowers

Xk2 : order wineXk : Order present

P( P (Xk ) | Xt ) Xt1 Xt2

P1(Xk ) 0.4 0.9

P2(Xk ) 0.6 0.1

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Contextual Effect on Unconditional Probability

X

Y

P(Y)-? P(P(X)|Z)

Z

P(Z)

P(X)

pk(X)

P(P(X))

P(Y|X)

X ={x1, x2, …, xn} – predictive attribute with n

values;

· Z ={z1, z2, …, zq} – contextual attribute with q values

and P(Z) – probability distribution for values of Z;

• P(X) = {p1(X), p2(X), …, pr(X)} – probability

distribution attribute for X (random variable) with r possible values (different possible probability distributions for X) and P(P(X)) is probability distribution for values of attribute P(X);

· P(Y|X) is a conditional probability distribution of Y given X;

· P(P(X)|Z) is a conditional probability distribution

for attribute P(X) given Z

})]|)()(()([

)()|({)(

1

1 1

q

mmkm

r

k

n

iikijj

zZXpXPPzZP

xXpxXyYPyYP

47

Two-level Bayesian Metanetwork for managing conditional dependencies

Contextual level

Predictive level A

B

X

Y

P(B|A) P(Y|X)

48

Example of Bayesian Metanetwork

The nodes of the 2nd-level network correspond to the conditional probabilities of the 1st-level network P(B|A) and P(Y|X). The arc in the 2nd-level network corresponds to the conditional probability P(P(Y|X)|P(B|A))

X

Y

P(X)

P(Y)-?

P(P(Y|X)|P(B|A))

A

B

P(A)

pr(B|A)

P(P(B|A)) P(B|A) P(Y|X)

pk(Y|X)

P(P(Y|X))

))]}.|()|(())|()|((|)|()|(([

)()|({)(

ABpABPPXYpABPPXYpXYPP

xXPxXyYpyYP

rr

rk

i kiijkj

49

Contextual level

Predictive level

2-level RelevanceRelevance Bayesian Metanetwork (for modelling relevant features’ selection)

50

General Case of Managing Relevance (1)

X1

Y

Probability

P(X1)

P(Y)-?

P(Y|X1,X2,…,XN)

Relevance

Ψ(X1)

XN

Probability

P(XN) Relevance

Ψ(XN) X2

Probability

P(X2) Relevance

Ψ(X2)

…

Predictive attributes:

X1 with values {x11,x12,…,x1nx1};

X2 with values {x21,x22,…,x2nx2};

…XN with values {xn1,xn2,…,xnnxn};

Target attribute:

Y with values {y1,y2,…,yny}.

Probabilities:

P(X1), P(X2),…, P(XN);P(Y|X1,X2,…,XN).

Relevancies:X1 = P((X1) = “yes”);

X2 = P((X2) = “yes”);

…XN = P((XN) = “yes”);

Goal: to estimate P(Y).

51

General Case of Managing Relevance (2)

X1

Y

Probability

P(X1)

P(Y)-?

P(Y|X1,X2,…,XN)

Relevance

Ψ(X1)

XN

Probability

P(XN) Relevance

Ψ(XN) X2

Probability

P(X2) Relevance

Ψ(X2)

…

1 2 )"")(()"")((

1

])1()(),...2,1|([...1

)(X X XN noXqq

XqyesXrr

XrN

s

XrPnxrXNXXYPnxs

YP

Probability

P(XN)

52

Bayesian Metanetworks for Advanced Personalization

3-level Bayesian Metanetwork forManaging Feature Relevance

X

Y

A

BQ

RSX

Y

A

B

Q

RS

2 -lev e l B ay esian M etan e tw o rk fo rm o d e llin g re lev an t fea tu res’ se lec tio n

C o n te x tu a l le ve l

P re d ic tiv e le v e l

Two-level Bayesian Metanetwork formanaging conditional dependencies

X

Y

A

BQ

RS

X

Y

A

B

Q

RS

T w o -lev e l B ay esian M etan e tw o rk fo rm an ag in g co n d itio n a l d ep en d en c ies

C o n te x tu a l le ve l

P re d ic tiv e le v e l

Terziyan V., Vitko O., Bayesian Metanetwork for Modelling User Preferences in Mobile Environment, In: German Conference on Artificial Intelligence (KI-2003), Hamburg, Germany, September 15-18, 2003.

http://www.cs.jyu.fi/ai/papers/KI-2003.pdf

53

When Bayesian Metanetworks ?

1. Bayesian Metanetwork can be considered as very powerful tool in cases where structure (or strengths) of causal relationships between observed parameters of an object essentially depends on context (e.g. external environment parameters);

2. Also it can be considered as a useful model for such an object, which diagnosis depends on different set of observed parameters depending on the context.

54

Spatial BDI Axiom

If a customer has absolutely same beliefs about content and quality of two different services, and such content and quality fits his recent desires, then this customer intents to select nearest one to get service from it.

55

Spatial Belief Axiom

Spatial Belief Axiom: customer believes QoS(q), i.e., that in the same location he can get likely the same quality of service q as he used to get in this location before (observation and communication results)

M W t1 [location_has_property(x, y, QoS(q))(M , t1)

t0 ≤ t1 [ Input(location_has_property(x, y, QoS(q)), t0, M )

t [t0, t1] Input(~location_has_property(x, y, QoS(q)), t, M)]]

56

Tracking Spatial Beliefs

Tracking spatial beliefs is based on the Spatial Belief Axiom: the customer’s beliefs can be tracked based on his current location coordinates by analyzing the history of his observations and actions (e.g., orders) in this or neighbour locations

57

Agent’s Spatial Belief Example

Red Light District

Why I am coming here once again?

…because I believe that I will get here the same fun as usual

58

Spatial Desire Axiom Spatial Desire Axiom: customer being elsewhere desires to get service q

in some location x,y, i.e., he believes QoS(q), “turns his view” to location x,y and also believes that there is no such service q1 with better quality closer on his route to x,y.

M W t, x, y, d, q { at_distance_at_location(R (M), d, x, y)

[ get_service(x, y, q)(M , t)

t0 ≤ t t1[t0, t] [ ( location_has_property(x, y, QoS(q))(M , t1) )

x1,y1, d1 [ ( at_distance_at_location(R (M), d1, x1, y1) (d > d1) )

q1 ( q1 < q location_has_property(x1, y1, QoS(q1))(M , t1) ) ]

t2[t0, t] ( click_location(x, y)(M , t2) ) ] },

where click_location(x, y)(M , t2) denotes customers action «click to point x,y on his terminal screen», i.e. «turning view»

to that point.

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Tracking Spatial Desires

Tracking spatial desires is based on Spatial Desire Axiom: the customer’s desires can be tracked based on types and coordinates of points of interest he clicks on the screen of mobile terminal

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Agent’s Spatial Desire Example

I am clicking tothe hotel pointon the screen.Why?

… because myrecent desire isto have

accommodation.

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Spatial Intention Axiom Spatial Intentions Axiom: customer intends to get some service at a

certain location, i.e. he desires to get this service, and he either

order/reserve this service online* or moves towards this service

location point**.

M W t, x, y, d, q {at_distance_at_location(R (M), d, x, y)

[ get_service(x, y, q)(M , t) ] t0 ≤ t t1[t0, t] [(get_service(x, y, q)(M, t1))

d1,d2 at_distance_at_location(R (M), d1, x, y)

[ t2[t1, t] [ (at_distance_at_location(R (M), d2, x, y) (d1 > d2 > d) )

t3[t1, t] ( order_service_online (q)( M , t3) ) ] ] ] }

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Tracking Spatial Intentions

Tracking spatial intentions: is based on Spatial Intentions Axiom: the customer’s intentions can be tracked either based on the evidence of his ordering/reserving the desired service online or based on sequentially decreasing distance between customer location and desired service location

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Agent’s Spatial Intentions ExamplesI made onlinereservation ofthe room in thishotel for 2-10November.Why?

… because one ofmy recentintentions is tolive in this hotelduring that time.

One of my desiresis to eat pizza andI am moving fasttowards RossoRestaurant. Why?

… because one ofmy intentions isto eat pizza inRosso just now.

*

**

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Spatial Action Axiom Spatial Actions Axiom: a customer performs an action of

getting some service, i.e. this service was intended, customer reaches the service location point and spends there at least minimal estimated time for this type of service or makes online electronic payment for it.

M W t, x, y, d, q { at_distance_at_location(R (M), d, x, y)

[get_service(x, y, q)(M , t) ] t0 ≤ t t1[t0, t] [(get_service(x, y, q)(M, t1))

at_distance_at_location(R (M), d1, x, y)

[ ti [t2, t3] [t1, t] [ ( at_distance_at_location(R (M), 0, x, y)

( t3 - t2 > min_time(q)) ) t3[t1, t] ( pay_for_service_online (q)( M , t3) ) ] ] ] }

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Tracking Spatial Actions

Tracking spatial actions is based on Spatial Actions Axiom, customer’s actions can be tracked either based on the evidence of his electronic payment for the intended service online or based on a fact that customers coordinates are the same as intended service coordinates during minimal estimated time for this type of service.

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Agent’s Spatial Action Example

One of myintentions was toeat pizza in“Rosso” andrecently I havespent in “Rosso”about half anhour. Why?

… because one ofactions I haverecentlyperformed is thatI have eatenpizza in Rosso.

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Tool for Mobile Customers vs. Tool for Agents

Tool for Mobile Customers: Location-Based Service NavigatorLocation-Based Service Navigator (LBSNLBSN) - helps its customers (mobile terminal users): (a) to navigate within unknown geographical locations,

(b) to access information resources of real world services located in neighbourhood areas.

Tool for Agents: Autonomous Sensor Support for AgentsAutonomous Sensor Support for Agents (ASSAASSA) - provides an “autonomous sensor” to an agent for observing environment around his location,

navigating within this environment, finding, communicating with, getting knowledge about, and services from other agents (services).

By keeping records of all transactions, ASSA is able to create really powerful collection of data about agents’ behaviour. Appropriate data mining and knowledge discovery algorithms can be applied to discover useful patterns of each agent spatio-temporal behaviour and use these patterns for

online prediction of agents’ preferences, beliefs, desires, intentions and actions.

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ASSA Benefits from LBSN (Changing Default Preferences)

Changing Default Preferences: LBNSLBNS: Input: built-in default preferences screen form,

which consists of scale slices and types of services to be shown for each scale slice; Action: customer edits and saves form; Output: updated default customer preferences; Comment: for example at a Street Network scale slice customer wants to see museums, hotels and restaurants only, at a City Network scale slice he prefers to see only gasoline stations, at a Country Network scale he prefers to see only embassies.

ASSAASSA gets explicitly agent’s preferences, which can be treated as a set of possible agent’s desires.

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ASSA Benefits from LBSN (Locating)

Locating: LBNSLBNS: Input: customer’s request ; Action: LBSN contacts

positioning infrastructure, requests to locate customer, gets coordinates and delivers to customer; Output: customer’s coordinates, four natural numbers (latitude, longitude, attitude, time point) according to agreed standard.

ASSAASSA gets explicitly agent’s location in different time

points, i.e. can collect agent’s routes within the environment and make grounded guesses based on this data about agent’s spatial BDI

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ASSA Benefits from LBSN (Showing Location)

Showing Location:• LBNSLBNS: Input: customer’s coordinates, scale of map

visualizing; Action: LBSN selects appropriate geographical data, prepares it and delivers to customer’s terminal; Output: screen with scaled map and pointed customer location on it; Comment: customer location supposes to be in the middle of screen, i.e. customer gets view of some scaled radius around his location.

• ASSAASSA gets explicitly the “picture of what the agent can see now”, i.e. can discover some peace of the agent’s knowledge .

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ASSA Benefits from LBSN (Showing Services)

Showing Services:• LBNSLBNS: Input: customer’s coordinates, scale of map visualizing;

Action: LBSN selects appropriate geographical and service data, prepares it and delivers to customer’s terminal; Output: screen with scaled map, pointed customer location on it and services, i.e. points of interest; Comment: all shown services are preliminary classified to types, displayed e.g. with different colours or different geometrical primitives for different type of service, and filtered against default user preferences.

• ASSAASSA gets explicitly data about “which services the agent can observe now”, i.e. can discover some peace of the agent’s knowledge.

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ASSA Benefits from LBSN (Zooming)

Zooming:• LBNSLBNS: Input: screen with scaled map and pointed

customer location on it, new scale of map visualizing; Action: LBSN updates map according to the new scale, prepares it and delivers to customer’s terminal; Output: screen with map updated according to a new scale and pointed customer location on it.

• ASSAASSA gets explicitly data about changing possible agents desires from one set of preferences to another one based on changes in scale. ASSA also gets new agent’s view to the neighbouring environment.

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ASSA Benefits from LBSN (Intelligent Zooming)

Intelligent Zooming:• LBNSLBNS: Input: screen with scaled map, pointed customer location and

services on it, new scale of map visualizing, preferences filter associated with new scale; Action: LBSN updates and filters appropriate service data according to the new scale, prepares it and delivers to customer’s terminal; Output: screen with map and services updated according to a new scale and preference filter and pointed customer location on it; Comment: independently of selected scale LBSN is able to show in screen only limited number of points of interest, that is why the more big scale is used the more service points will be refused to be selected by preference filter.

• ASSAASSA gets explicitly data about changing possible agents desires from one set of preferences to another one based on changes in scale. ASSA also gets new agent’s view to the services available in the neighbourhood.

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ASSA Benefits from LBSN (Showing Point)

Showing Point: LBNSLBNS: Input: screen with scaled map, pointed

customer location and services on it, customer’s click to certain point of interest; Action: LBSN selects and downloads appropriate online data about requested service, prepares it and delivers to customer’s terminal; Output: screen with online information about point of interest, e.g. recent offerings, prices, contact info, etc.

ASSAASSA gets explicitly focus of the agent’s view, i.e. gets data about agent’s desires, based on type of service the agent is observing now.

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ASSA Benefits from LBSN (Showing Route)

Showing Route: LBNSLBNS: Input: screen with scaled map, pointed customer

location and services on it, customer’s click to the point of interest on the map; Action: LBSN discovers optimal routes from customer’s location to selected point depending on map scale and available transport, prepares appropriate data and delivers it to customer’s terminal; Output: screen with map with highlighted routes between the two points for all available transport facilities.

ASSAASSA gets explicit information about agent’s desire to move towards the selected point and to get appropriate service.

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ASSA Benefits from LBSN (Call Point)

Call Point: LBNSLBNS: Input: screen with online information

about point of interest, customer’s click to “call” button; Action: LBSN via mobile terminal dials telephone number of selected service; Output: Customer is connected to the point of interest.

ASSAASSA gets explicit information about agent’s intentions to get more information about appropriate service and agent’s desire to get this service .

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ASSA Benefits from LBSN (Order Service)

Order Service: LBNSLBNS: Input: screen with online information

about point of interest, customer’s click to “order” button; Action: LBSN via mobile terminal connects customer with appropriate service ordering web page of the selected service; Output: screen with online order form from the selected service web site.

ASSAASSA gets explicit information about agent’s intentions to get selected service.

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Conclusions (1) We assumed that location of an agent effects on his beliefs

desires and intentions and that the history of observed agent’s mobility can be used to predict his future states;

Formal spatial route language used in this paper is introduced. The assumptions made on the notions belief, desire and intention, and the way of their interactions are discussed and formalised: formal relationships between the intentional notions, and the spatial behaviour of an agent are defined.

The case of using agent architecture for reasoning about the intentions of the customers of a mobile location-based service is also described.

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Conclusions (2) The approach introduced opens up a number of

possibilities for further work. For example various electronic commerce applications are interested in personalizing their services to the customers by predicting and utilizing customer preferences. For location-aware applications the agent-based analogy of modelling customers’ beliefs, desires and intention in tracked locations might be an important possibility.

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AcknowledgementsAgora Center (University of Jyvaskyla):Agora Center includes a network of good-quality research groups from various disciplines. These groups have numerous international contacts in their own research fields. Agora Center also coordinates and administrates research and development projects that are done in cooperation with different units of university, business life, public sector and other actors. The mutual vision is to develop future's knowledge society from the human point of view.

http://www.jyu.fi/agora-center/indexEng.html

InBCT Project (2000-2004):Innovations in Business, Communication and Technology

http://www.jyu.fi/agora-center/inbct.html

http://www.sonera.com/


http://www.yomi.fi/eng


http://www.metso.com/corporate/homeeng.nsf/FR?ReadForm

http://www.jsp.fi/jspf/index.html

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