Semantics in Multi-agent Systems

Chapter 7Semantics in Multi-Agent Systems

Nicoletta Fornara, Gordan Ježic, Mario Kušek, Ignac Lovrek, Vedran Podobnik,Krunoslav Tržec

Abstract In this chapter we discuss how semantic technologies in general and spe-cific Semantic Web standards in particular can contribute to the goal of achievinginteroperability between independent, loosely coupled, heterogeneous, autonomoussoftware components (i.e. agents) and for the realization of open interaction sys-tems. In particular we will discuss how those technologies have been used for thedefinition of the semantics of agent communication languages, for the definition ofnorms and policies used to regulate interactions in open frameworks, and for defin-ing efficient mechanisms for matching demands (i.e., content they need) to supplies(i.e., available content) in Telecommunication networks. In particular regarding thislast type of application we describe a techno-economic approach for solving thematching problem, by means of a multi-agent system representing an electronicmarketplace. Its functionality is realized by applying a semantic-aware content dis-covery model with two-level filtering in order to finally recommend a ranked set

Nicoletta FornaraUniversità della Svizzera italiana, via G. Buffi 13, 6900 Lugano, Switzerland, e-mail:[email protected]

Gordan JežicUniversity of Zagreb, Faculty of Electrical Engineering and Computing, Croatia, e-mail: [email protected]

Mario KušekUniversity of Zagreb, Faculty of Electrical Engineering and Computing, Croatia, e-mail: [email protected]

Ignac LovrekUniversity of Zagreb, Faculty of Electrical Engineering and Computing, Croatia, e-mail: [email protected]

Vedran PodobnikUniversity of Zagreb, Faculty of Electrical Engineering and Computing, Croatia, e-mail: [email protected]

Krunoslav TržecEricsson Nikola Tesla, Croatia, e-mail: [email protected]

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Fornara, Nicoletta; Jezic, Gordan; Kusek, Mario; Lovrek, Ignac; Podobnik, Vedran; Trzec, Krunoslav. Semantics in Multi-agent Systems. Agreement Technologies / Ossowski, Sascha, editor(s). Dordrecht: Springer, 2013, pp. 115-136.

114 N. Fornara et al.

of eligible content to the users in response to their requests. The filtering processesnot only consider the semantic information associated with the available content,but also ratings regarding the actual performance of businesses that act as contentproviders as well as the prices paid by businesses for advertising their content.

7.1 Introduction

In this chapter we discuss and report some examples of how semantic technolo-gies in general and specific Semantic Web standards in particular can contribute tothe goal of achieving interoperability between independent, loosely coupled, het-erogeneous, autonomous software components (that we call agents). These compo-nents need to interact, negotiate, compete, or collaborate in order to reach their owngoals in an open framework, that is, in a framework where those software agentsdynamically start or stop to interact with other agents without being specificallyprogrammed for interacting with a specific counterpart. Examples of applicationdomains where this ability is fundamental are eCommerce and eProcurement (forexample for the specification of B2B or B2C electronic auctions or e-markets wheredifferent parties may buy or sell products in [34, 23]), eBusiness (for example for thedynamic creation of supply chains or virtual enterprises [28, 5]), and resource shar-ing systems (for example systems for data, video, audio, or photo sharing [4, 26]).

The problem of interoperability between autonomous components in an openframework has the following two crucial characteristics:

• no assumptions can be made about the internal structure of the interacting par-ties and about their willingness to satisfy the rules, the norms, the interactionprotocols, or the agreements reached with other agents;

• the interacting agents for planning their future communicative and non commu-nicative actions need to have an expectation on the future actions of the otheragents and therefore they need to be able to assume that every agent will de-rive the same conclusions from the information received. Therefore they need toshare a common semantics for the meaning of the exchanged messages.

In order that the interaction among autonomous parties may lead to states havingsome global desirable properties, it is crucial to constrain agents’ actions with a setof norms, rules, or protocols.

In this chapter in Section 7.2 we will present and discuss how Semantic WebTechnologies are used for modeling and reasoning on the content of agent com-municative acts, on the specification of Artificial Institutions, and on norms andpolicies definition and enforcement (see Chapter 18 and Part III for more detailson these concepts). In Section 7.3 we will present and discuss how Semantic WebTechnologies are used for tackling one of the fundamental problem of open B2Ce-markets: the problem of searching for possible matches between requested andavailable products, where products consists of content delivered over a network bytelecommunication services.

7 Semantics in Multi-Agent Systems 115

7.2 Semantic Technologies for ACL, Institutions, and Normsspecification

One possible proposal, for the realization of interoperability in an open framework,is to define an application-independent format for the communication of informa-tion (abstract and concrete syntax), as for instance the one proposed in FIPA-ACL1

(Agent Communication Language) and most importantly a commonly accepted se-mantics. Usually the semantics of messages is defined compositionally by com-bining the semantics of the type of the message (as for instance promise, request,inform, agree, refuse) that is application independent, with the semantics of thecontent of the message that may be partially application independent and partiallyapplication dependent.

In the definition of the semantics of those components an important role maybe played by semantic technologies. One important advantage of adopting Seman-tic Web technologies is that they are increasingly used in Internet applications andtherefore it would be easier to achieve a high degree of interoperability of data andapplications. Moreover, given that Semantic Web technologies are becoming widelyused in innovative applications it will become much easier to teach them to softwareengineers than convince them to learn and use a logic language adopted by a limitedgroup of researchers. One important standard Semantic Web language is OWL De-scription Logic (DL)2. The adoption of this language as a formal language for thespecification of messages and their semantics has many advantages: thanks to thefact that it is decidable it is supported by many reasoners (like FaCT++3, Pellet4,Racer Pro5, HermiT6), there are many tools for OWL ontology editing (like Pro-tégé, NeOn), and there are libraries for automatic OWL ontology management (likeOWL-API, KAON)7.

Examples of existing approaches that use semantic technologies for the formal-ization of the content language of FIPA-ACL are: the proposal of using RDF ascontent language of FIPA-ACL [10], the proposal of using the Darpa Agent MarkupLanguage (DAML) language for expressing the content of messages [43], the pro-posal of using OWL DL as content language of FIPA-ACL [35], and the proposalof using OWL DL as content language of a commitment-based ACL whose syntaxis compatible with FIPA-ACL [15].

A crucial requirement in open system is that the semantics of different types ofcommunicative acts and of their content part has to be strongly independent of theinternal structure of the interacting agents. The semantics of FIPA-ACL presents the

1 http://www.fipa.org/repository/aclspecs.html2 http://www.w3.org/2007/OWL/wiki/OWL_Working_Group3 http://owl.man.ac.uk/factplusplus/4 http://clarkparsia.com/pellet/5 http://www.racer-systems.com/products/racerpro/6 http://hermit-reasoner.com/7 W3C list of reasoners, editors, development environments, APIs: http://www.w3.org/2007/OWL/wiki/Implementations


problem of relying heavily on the BDI model of agents and of not taking into ac-count the normative consequences of message exchanges. A successful approach tosolving this problem consists in formalizing the effects of making a communicativeact under specified conditions with the creation of a new object: the social commit-ment between the speaker and the hearer having a certain content and condition. For-mal proposals to treat communicative acts in terms of commitments and to monitortheir state on the basis of the agents’ actions can be found in [6, 37, 12, 40]. In par-ticular in [15] a proposal of using OWL DL as content language of a commitment-based ACL and for expressing the semantics of promise communicative acts is pre-sented.

However, expressing the meaning of certain types of communicative acts in termsof social commitments is not enough for completely representing their semantics,that is, for representing all the consequences of sending or receiving certain com-municative acts for the future actions of the agents. The point is: why an agent thatis a debtor for certain social commitments should plan its actions in order to fulfillor violate them? One possible answer to this question could involve proposing toformalize the institutional framework where the interaction takes place, and there-fore specify the consequences in terms of reward or sanctions for the fulfilment orviolation of social commitments. It is important to remark that in order to be able toapply those rewards or sanctions, it is also necessary to define and realize a moni-toring component able to detect the fulfilment or violation of social commitments.

The definition of a shared institutional framework is also a requirement for defin-ing the meaning of an important type of communicative act: the declarations. Forexample in an electronic auction the agent playing the role of auctioneer may de-clare open a run of an auction or declare the winner of the run. The institutionalframework can be used to define institutional attributes (for example the state of anauction) and to define the semantics of a declarative communicative act by meansof the changes brought about by this act in the value of institutional attributes, ifcertain conditions (for example having the required institutional power) hold [13].

Nevertheless, for effectively realizing interoperability among autonomous soft-ware agents in open, distributed, and competitive scenarios, the definition of a com-monly accepted communication language and of an institutional framework thatspecifies sanctions and institutional concepts may not be enough. As previouslyremarked, in order to plan their actions the interacting agents need to have an ex-pectation of the future evolution of the state of the interaction. This is possible ifthe interacting parties commonly accept a set of rules or norms used to define theobligations, prohibitions, permissions of the interacting parties. Some of them maybe created and negotiated at run-time by interacting agents with enough reasoningcapabilities, but given that negotiating all those rules from scratch may be very ex-pensive in terms of the number of interactions required, and it can be done only byvery complex agents, the more complex norms may be completely or partially (atleast their structure or template) specified at design time.

It is fundamental to express those norms using a declarative formal languagebecause this makes it possible to represent them as data, instead of coding them inthe software, with the advantage of making it possible to add, remove, or change


the norms that regulate the interaction both at design time or at run-time, with-out the need to reprogram the interacting agents. Moreover this makes it possible,in principle, to realize agents able to automatically reason about the consequencesof their actions and able to interact within different systems without the need tobe reprogrammed. Finally their formal specification makes it possible to realizean application-independent monitoring component able to keep track of the state ofnorms on the basis of the events that happen in the system, and an enforcement com-ponent capable of reacting to norms fulfillment or violation on the basis of specificenforcement rules.

Semantic Web languages play a crucial role as languages for the declarative spec-ification of norms. For example in [14, 11] OWL 2 DL and SWRL rules are usedto represent and monitor norms and obligations. In those works given that SemanticWeb technologies are not devised for modelling and monitoring the state of dynamicsystems two problems are tackled: one is related to performing temporal reasoningan important problem given that OWL has no temporal operators; another one isrelated to successfully monitoring obligations with deadline, that is deducing thatwhen the deadline has elapsed an obligation has to be permanently fulfilled or vio-lated despite the open-world assumption of OWL logic. In [20] Semantic Web lan-guages are used to represent norm-governed organizations allowing norm conflict(i.e., an action being simultaneously obliged and prohibited), to be captured andstudied. Another example is the OWL-based representation of policies presented in[36], it enables both policy-governed decision making and policy analysis withinthe bounds of decidability.

A crucial open problem related to the choice of using Semantic Web Technolo-gies, and in particular OWL, as formal languages for the specification and devel-opment of fundamental components of agreement technologies is the problem ofunderstanding what part of those components it is better and possible to represent inontologies in order to be able to reason on it and what part of those components it isbetter to represent in an external application because current semantic web standardsdo not support its representation. In what follows, some of the issues raised in thissection are illustrated by an application in the domain of semantic-aware contentdiscovery.

7.3 Semantic-aware Content Discovery in TelecommunicationEnvironment

Discovery is the process of searching for possible matches between requested andavailable products. It is especially important for efficient trading when products donot represent commodities, i.e., their value is not characterized only by their price.An example of such product is content. Efficient discovery processes should identifyall the supplies that can fulfill a given demand to some extent, and then propose themost promising ones [24, 32, 30]. Just a few years ago, discovery relied on simplekeyword matching. However, nowadays discovery is becoming grounded on novel


mechanisms which exploit the semantics of content descriptions. Since these novelmechanisms may lead to a plethora of possible matches, mediation between con-tent requesters (users) and content providers (businesses) is one of the most difficultproblems faced in real world B2C e-markets [27, 31]. Thus, the notion of matchranking becomes very important, so matches can be ordered according to some cri-teria. If supplies and demands were described by simple strings, the only possiblematch would be identity, resulting in an all-or-nothing approach to matchmakingand ignoring the fact that supplies and demands also have a semantic dimension.This semantic dimension of content could be exploited in order to evaluate “in-teresting” inexact matches [24]. Exact (full) matches are usually rare and the truediscovery process is aimed at providing a ranked list of the most eligible matches,thus leveraging further interaction [7]. Most approaches suggested for semantic dis-covery to use standard DL reasoning to determine whether one description matchesanother. None of these solutions exploit implicit semantics, i.e., patterns and/or rel-ative frequencies of descriptions computed by techniques such as data mining, lin-guistics, or content-based information retrieval. In order to exploit these techniques,Klusch et al. use the OWLS-MX [18, 19], a hybrid semantic matching tool whichcombines DL-based reasoning with approximate matching based on syntactic infor-mation retrieval (IR) similarity computations.

Telecommunication services can be defined as a service which consists of contentdelivered over network resources. Today, a remarkable selection of diverse content isoffered in form of various telecommunication services to users. Consequently, usersrequire efficient mechanisms which can match demands (i.e., content they need) tosupplies (i.e., available content) [29]. Here we describe a techno-economic approachto solving this problem, implemented through a multi-agent system representing anelectronic marketplace. Stakeholders and processes on the electronic marketplaceare based on Telco 2.0 [41] business model – users act as content buyers, contentproviders as content sellers and telecommunication operators (i.e., telcos) as bro-kers. The functionality of presented agent-mediated electronic marketplace is real-ized by applying a semantic-aware content discovery model which uses two-levelfiltration of available content before a final ranked set of eligible content is rec-ommended to users in response to their requests. The filtration processes do notonly consider the semantic information associated with available content, but alsoconsider ratings regarding the actual performance of businesses that act as contentproviders (with respect to both price and quality) and the prices paid by businessesfor advertising their content.

By introducing SPPCA (Semantic Pay-Per-Click Agent) auction, we enable con-tent providers to contact telcos and advertise semantic descriptions of the contentthey provide. Consequently, users can utilize the telco’s service of two-level filtra-tion of advertised content to efficiently discover the most suitable. In the first levelof filtration, the broker (i.e., the telco) applies a semantic-based mechanism whichcompares content requested by users to those advertised by content providers (i.e.,ranked semantic matchmaking). The content which pass the first level of filtrationis then considered at the second level. Here information regarding the actual per-formance of content providers with respect to both price and quality is considered


in conjunction with the prices bid by content providers in the SPPCA auction. Atthe end, a final ranked set of eligible content is chosen and proposed to the user.The following question may arise here: why does the broker propose the ranked setof eligible content to the user and not just the top-ranked eligible content (or, inother words, why not select just the first content from the top of the list represent-ing the ranked set and then buy that content from corresponding content provider)?Although the latter could be a possible solution, this would violate the CBB (Con-sumer Buying Behavior) model [16] for transactions in the B2C e-markets becauseit omits the negotiation phase which should happen after the brokering phases andbefore the purchase and delivery phase. Therefore, the broker proposes the rankedset of eligible content to the user to enable the user to contact more than one contentprovider and negotiate the terms of purchase with them. When the negotiation phaseis completed, the user chooses one content provider and buys the content from it.The chosen content provider can be the content provider of the top-ranked content inthe ranked set of eligible content proposed by the broker, but can also be the contentprovider of lower-ranked content (e.g., the content provider of third-ranked contentoffers the user the lowest purchase price during the negotiation and the user choosesthis content provider because for him/her it is only important that the content is sim-ilar to the requested one and that the price is as low as possible). As it is going tobe later explained, we are using Contract-Net protocol for the negotiation betweenusers and content providers.

It is important to highlight the fact that telcos, who represent brokers in theproposed service e-market, do not base their recommendations solely on seman-tic matchmaking, but they also consider the actual performance of businesses whichact as content providers, with respect to both price and quality. The performancemodel of content providers is founded on research regarding trust and reputation ine-business [8, 33, 38, 39, 42].

7.3.1 The agent-based architecture of electronic market fortelecommunication services

A description of the Telco 2.0 service e-market architecture follows along with ademonstration of how it operates. The proof-of-concept prototype is implemented asa JADE (Java Agent DEvelopment Framework) multi-agent system [2]. In the pro-totype agents communicate by exchanging ACL (Agent Communication Language)messages. Coordination between agents is achieved by applying FIPA (Foundationof Intelligent Physical Agents) interaction protocols. Two types of pre-defined FIPAconversation protocols (FIPA Request and FIPA Contract-Net) are used.

Figure 7.1 illustrates the Telco 2.0 service e-market architecture. There are threestakeholders in service e-market: content providers, users and telcos. These stake-holders are in our proposal of service e-market represented with three types ofagents: Content Provider Agents, User Agents and Telco Agents, respectively.

In the Figure 7.1 also four different interactions can be identified:


1. SPPCA auction interaction: between Content Provider Agent and Telco Agent,used for advertising content at the broker;

2. FIPA Contract-Net interaction: between User Agent and Telco Agent, used fordiscovery of eligible content;

3. FIPA Contract-Net interaction: between User Agent and Content Provider Agent,used for negotiation about content purchase, and;

4. FIPA Request interaction: between Content Provider Agent and Telco Agent,used for requesting content delivery (in form of telecommunication service) tothe user.

A more detailed description of agents and interactions follow.

Fig. 7.1: A Telco 2.0 service e-market


7.3.1.1 The Content Provider Agent

In the service e-market agents trade with various types of content C:

C = {c1,c2, . . . ,c|C|},c⊂C,ci ⊂C : |ci|= 1

which is provided by different content providers CP:

CP = {cp1,cp2, . . . ,cp|CP|},cp⊂CP,cpi ⊂CP : |cpi|= 1

Content providers are represented in the e-market by Content Provider AgentsACP:

ACP = {acp1 ,acp2 , . . . ,acp|CP|},aCP ⊂ ACP,acpi ⊂ ACP : |acpi |= 1

An acpi represents a content provider which offers a certain content ci. Initially,acpi advertise its content (advertised ci is denoted as cadv) at the broker (i.e., theTelco Agent). An acpi accomplishes that by participation in the SPPCA auction(interaction 1 in Figure 7.1), which enables cpi to dynamically and autonomouslyadvertise semantic descriptions of its content.

After successfully advertising its cadv, an acpi waits to be contacted by an user(i.e., an User Agent) which is interested in the content it is providing. If user pur-chases the content from acpi , the acpi requests from user’s telco content delivery (inform of telecommunication service) to that user (interaction 4 in Figure 7.1).

7.3.1.2 The User Agent

Users of telecommunication services U :

U = {u1,u2, . . . ,u|U |},u⊂U,ui ⊂U : |ui|= 1

are represented in the Telco 2.0 service e-market by agents AU :

AU = {au1 ,au2 , . . . ,au|U |},aU ⊂ AU ,aui ⊂ AU : |aui |= 1

An aui acts on behalf of its owner (i.e., user) in the discovery process of suitablecontent and subsequently negotiates the utilization of that content. An aui wishesto get an ordered list of ranked advertised content which is most appropriate withrespect to its needs (requested ci is denoted as creq). It uses the FIPA Contract-Netinteraction protocol (interaction 2 in Figure 7.1) to contact the broker (i.e., the TelcoAgent). After an aui receives recommendations from the broker, it tries to contact adesired number of proposed aCP and find the one which offers the best conditions(e.g., the lowest price) for the requested content (interaction 3 in Figure 7.1).

After the selected content is delivered to the user (in form of telecommunicationservice), the aui sends a feedback message to the broker with information about


its level of satisfaction regarding the proposed aCP (completion of interaction 2 inFigure 7.1).

7.3.1.3 The Telco Agent

The telco t is represented in the e-market by the Telco Agent at . An at is the onlyTelco Agent “visible” from outside of telco system and represents a broker betweenthe remaining two types of agents, i.e., AU and ACP. An at enables ACP to advertisetheir content descriptions (interaction 1 in Figure 7.1) and recommends ranked setsof eligible content to AU in response to their requests (interaction 2 in Figure 7.1).It is assumed that at is trusted party which fairly intermediates between contentrequesters (i.e., users) and content providers.

It is important to highlight the fact that the at serves as manager agent whichcoordinates telco’s brokering services and represents the telco in communicationwith all non-telco agents (i.e., the AU and ACP). The telco brokering services are inpresented proof-of-concept implementation facilitated by three other Telco Agentswhich are not “visible” from outside the telco system: the SPPCA Auction Agent(aSAA), the Matching Agent (aMA) and the Discovery Agent (aDA).

7.3.2 Content discovery in telecommunication electronic markets

Figure 7.2 shows a more detailed architecture of a broker in the service e-market.Note that the at serves as an interface agent between AU/ACP and the telco. TheSPPCA Auction Agent (aSAA), the Matching Agent (aMA) and the Discovery Agent(aDA) enable the broker functionalities. These agents are allowed to make queriesto the telco’s databases. The aSAA is in charge of conducting the SPPCA auction.Interaction 1.1 is used for registering/deregistering CP in the auction and placingnew bids, while the aSAA uses interaction 1.2 to announce a new auction round.The aMA facilitates semantic matchmaking which corresponds to the first level offiltration ( f1) in the content discovery process. It receives semantic descriptions ofrequested content through interaction 2.1 and forwards a list of semantically suit-able content c f1 through interaction 2.2 to the aDA which carries out second-levelfiltration ( f2) and recommends a ranked set of eligible advertised content −→c f2 (in-teraction 2.3). Sometime later, after the selected content is delivered to the user (inform of telecommunication service), the aDA receives feedback information fromthe aureq (through the at ) regarding the performance of the −−→cp f2 (cp which offer −→c f2 )(interaction 2.4).

There are two databases at the broker (i.e., telco): the Content Database andthe Provider Database. The Content Database contains information about all the cwhose bids are currently active and which therefore participates in SPPCA auctionrunning at this broker. The Provider Database contains information regarding all thecp whose c is advertised at this broker.


Fig. 7.2: The detailed architecture of a broker in the service e-market

Figure 7.3 in more details describes the communication between the three partiesinvolved in the discovery process: ui (i.e., aui ) as content requester, telco (i.e., at ) asbroker and cpi (i.e., acpi ) as content provider. The presented interactions facilitate adiscovery process. The specific parameters in the exchanged messages are describedin the following subsections to help clearly present the advertising concept, match-making mechanisms and performance evaluation techniques used for designing ourcontent discovery model in the Telco 2.0 service e-market.

Fig. 7.3: Communication between the User Agent, Telco Agent and ContentProvider Agent enabling discovery


Figure 7.4 presents interactions between aui and at which enable content discov-ery in the proposed service e-market, while Figure 7.5 explains how the SPPCAauction, which is part of the discovery process, operates.

Fig. 7.4: The User Agent discovers the most eligible content advertised at the TelcoAgent

The aui , by sending CFP (Call for Proposal) to at , requests two-level filteringof advertised content descriptions to discover which is the most suitable for itsneeds. Along with the description of requested content creq, the CFP includes theset of matching parameters (to be explained later) that personalize the discoveryprocess according to the user preferences. First-level filtering ( f1 : C→C) is basedon semantic matchmaking between descriptions of content requested by ui (i.e., aui )and those advertised by cp (i.e., aCP). Content which pass the first level of filter-ing (c f1 ⊂ C) is then considered in the second filtering step. Second-level filtering( f2 : C→ C) combines information regarding the actual performance of cp f1 (cpwhich offer c f1 ) and prices bid in SPPCA auction by corresponding aCPf1

(aCP thatrepresent cp which offer c f1 ). The performance of cp f1 (with respect to both priceand reputation) is calculated from the previous AU feedback ratings. Following fil-tration, a final ranked set of eligible content (−→c f2 ⊂ c f1 ) is chosen. This set is thenrecommended to the AU in response to their requests.

The SPPCA auction is divided into rounds of a fixed time duration. To announcethe beginning of a new auction round, the at broadcasts a CFB (Call for Bid) mes-sage to all the aCP which have registered their cadv for participation in the SPPCAauction. Every CFB message contains a status report. In such a report, the at sendsto the acpi information regarding events related to its advertisement which occurredduring the previous auction round. The most important information is that regard-


Fig. 7.5: The SPPCA auction

ing how much of the acpi budget8 was spent (the cost of certain advertisement inone auction round is equal to this advertisement’s bid price bidcadv multiplied by thenumber of recommendations of corresponding cadv to various aU ). In response to aCFB message, an acpi sends a BID message. In doing so, the acpi assures that itscadv will be considered in the discovery processes which will occur during the nextauction round. In addition to referencing the corresponding content description cadv,a BID message also contains information specifying the value of the bid bidcadv andinformation regarding the acpi budget.

7.3.2.1 Semantic matchmaking of content descriptions

In the MAS implementing the proposed service e-market, the Semantic Web tech-nology [1, 21] is used to describe content. By applying the Semantic Web concepts[3, 17, 9], content can be described by OWL9 (Web Ontology Language), a seman-tic mark-up language based on DL. OWL provides a reasonable level of flexibility

8 The notion of budget is very important in the SPPCA scenario because it enables contentproviders to specify their spend limits for the current auction round. Note that one content providercan have multiple content advertisements simultaneously participating in the same SPPCA auction.If such is the case, all advertisements of the same content provider potentially have different bidvalues since a content provider can advertise only one content advertisement per BID message, andyet all the advertisements share the same budget. Thus, when multiple BID messages for the sameauction round from a single content provider are received, budget values are cumulatively addedto a budget balance unique for all advertisements originating from the same content provider. Thisway, content providers do not need to use complex optimization techniques to optimally distributetheir budget among their multiple content advertisements. The advertisements of every contentprovider are monitored during the auction round and potentially all advertisements of a certaincontent provider become inactive until the end of that round if this content provider’s budget isspent before the auction round is over. Consequently, this content provider’s advertisements arenot considered in any of the subsequent content discovery processes during that round.9 http://www.w3.org/TR/owl-features/


and extensiveness while keeping a balance between expressiveness and decidabil-ity. OWL ontology describing content is shown in Figure 7.6: Content is definedby its Category, its InformationType and its Theme. The Category can be one ofthe following: News, Music, Movies. Furthermore, the InformationType is definedas Data, Audio or Video, where Audio is Voice or HighFidelityAudio (also referredas CDAudio) and Video is HighDefinitionVideo or InteractionVideo. The Theme ishierarchically organized structure, here represented through hierarchy of continentsand countries. The OWL-S10 (Web Ontology Language for Services) is an OWL-based technology originally designed for describing the semantics of services in anunambiguous, computer interpretable mark-up language, but can also been used fordescribing the semantics of products such as content. The three main parts of anOWL-S ontology are: a service profile for advertising and discovering service (theservice profile is defined by four parameters: input, output, precondition and effect);a service model, which gives a detailed description of a service’s operation; and aservice grounding, which provides details on how to interoperate with a service viamessages. In our proposal of autonomous content discovery in Telco 2.0 servicee-market we use only service profile for description of content: thus, hereafter, theOWL-S service profile will be referred as OWL-S content profile. The OWL-S con-tent profile is defined by two parameters: input and output, which are described bythe ontology in Figure 7.6. The input is described by hasCategory and hasThemeproperties, and output is described by hasInformationType property.

The aMA uses OWLS-MX [19], a hybrid semantic matching tool which com-bines logic-based reasoning with approximate matching based on syntactic infor-mation retrieval similarity computations. As the notion of match rankings is impor-tant, OWLS-MX enables computation of the degree of similarity between comparedcontent descriptions, i.e., the comparison is assigned a content correspondence fac-tor (M), which we use as one of the parameters for calculation of a ranked finalset of eligible content −→c f2 in. Such a similarity ranking is highly relevant since it isunlikely that there will always be a content available which offers the exact featuresrequested. Namely, the OWLS-MX matchmaker takes as input the OWL-S contentprofile of aui desired content creq (the creq parameter in Figures 7.3 and 7.4), andreturns a set of relevant content which match the query: c f1 . Each relevant con-tent is annotated with its individual content correspondence factor Mcreq,cadv . Thereare six possible levels of matching. The first level is a perfect match (also calledan EXACT match) which is assigned factor M = 5. Furthermore, we have fourpossible inexact match levels which are as follows: a PLUG-IN match (M = 4), aSUBSUMES match (M = 3), a SUBSUMES-BY match (M = 2) and a NEAREST-NEIGHBOUR match (M = 1). If two content descriptions do not match accordingto any of the above mentioned criteria, they are assigned a matching level of FAIL(M = 0). The EXACT, PLUG-IN and SUBSUMES criteria are logic-based only,whereas the SUBSUMES-BY and NEAREST-NEIGHBOUR are hybrid due to theadditional computation of syntactic similarity values required. A aui specifies itsdesired matching degree threshold, i.e., the Mmin parameter (one of the matching

10 http://www.daml.org/services/owl-s


Fig. 7.6: Ontology describing content


Fig. 7.7: The semantic matchmaking between required and advertised content de-scriptions

parameters in CFP message from Figures 7.3 and 7.4), defining how relaxed thesemantic matching is.

A illustration of the hybrid content matching with OWLS-MX by means of sim-ple example follows. Figure 7.7 shows four OWL-S content profiles: the requiredcontent description (creq), and three different advertised content descriptions (cadv1 ,cadv2 and cadvn ). When OWLS-MX semantic matchmaking rules are applied, bear-ing in mind that OWL-S content profile is defined by input and output parameters(which are described by ontology in Figure 7.6), the result is EXACT match be-tween creq and cadv1 (i.e., Mcreq,cadv1

= 5), PLUG-IN match between creq and cadv2(i.e., Mcreq,cadv2

= 4) and NEAREST-NEIGHBOUR match between creq and cadvn

(i.e., Mcreq,cadvn= 1).

7.3.2.2 The performance model of content providers

A performance model tracks the past performance of CP in the service e-market.This information can then be used to estimate its performance with respect to futurerequests [22]. Our approach monitors two aspects of a cpi performance – the repu-tation of the cpi and the cost of utilizing the c that cpi is offering. The reputation ofa cpi reveals its former cooperative behavior and thus reduces the risk of financialloss for U [25]. Additionally, the reputation of the cpi is a measure for quality of thec provided by that cpi. On the other hand, information regarding the cost of utiliz-


ing the offered c enables U to find the best-buy option and helps prevent them fromspending their money where it is not necessary.

An aui gives an at feedback regarding all cpi from −−→cp f2 , both from reputationviewpoint called the quality rating (Q ∈ [0.0,1.0]) and the cost viewpoint called theprice rating (P ∈ [0.0,1.0]) (the FEEDBACK (−−→cp f2 ) parameter in Figures 7.3 and7.4). A rating of 0.0 is the worst (i.e., the cpi could not provide the content at alland/or utilizing the content is very expensive) while a rating of 1.0 is the best (i.e.,the cpi provides a content that perfectly corresponds to the ui needs and/or utilizingthe content is very cheap).

EWMA-based (Exponentially Weighted Moving Average) learning is used forcalculating the overall ratings of cpi

11. It is computationally simple since the newoverall rating can be calculated from the previous overall rating and the currentfeedback rating (i.e., there is no need to store old ratings which is desirable due toscalability issues). EWMA is defined as follows:

xt = ξ xt +(1−ξ )xt−1 for t = 1,2, . . .

where xt is the new forecast value of x; xt is the current observation value (inour case, the new feedback rating); xt−1 is the previous forecast value; 0 ≤ ξ ≤ 1is a factor that determines the depth of memory of the EWMA. As the value of ξ

increases, more weight is given to the most recent values. Every broker (i.e., telco)sets this factor value according to its preferences.

7.3.2.3 Calculating a recommended ranked set of eligible content

After an at receives a discovery request message (the CFP (creq, matching parame-ters) message in Figures 7.3 and 7.4) from an aui , the broker (i.e., telco) calculates aranked set of the best-suitable content−→c f2 . An ordered set−−→cp f2 is then recommendedto the aui in response to its request (the PROPOSE (−−→cp f2 ) message in Figures 7.3and 7.4). The matching parameters in CFP message are defined as: {α,β ,γ,Mmin}.Since the performance model monitors two aspects of the cpadv (i.e., its quality andprice), the aui defines two weight factors which determine the significance of eachof the two aspects in the process of calculating the final proposal (β represents aweight factor describing the importance of content quality at cpadv while γ repre-sents a weight factor describing the importance of content prices at cpadv). Further-more, an aui can specify whether information regarding the semantic similarity ofcreq and cadv is more important to it or information regarding a cpadv performance.

11 EWMA-based learning cannot calculate the overall ratings when a content provider is partici-pating for the first time and does not have a history of customer feedback (i.e., there is no entry forthe content provider in the Provider Database). Therefore, when a content provider sends a BIDmessage for the first time, the broker not only puts the information about new content advertise-ment into the Content Database, but also creates a new entry in the Provider Database where theinitial quality and price ratings of this new content provider are set to the average values of qualityand price ratings of all content providers whose entries already exist in the Provider Database. Insuch a manner we counter the problem of cold start inherent to EWMA-based learning method.


Thus, the aui also defines parameter α which is a weight factor representing theimportance of the semantic similarity between creq and cadv. The Mmin parameter isalready explained: with it a aui specifies its desired matching degree threshold, i.e.,defining how relaxed the semantic matching is.

The final rating Rcadv of a specific cadv at the end of discovery process is givenby:

Rcadv =α · Mcreq ,cadv

5 +β ·Qcpadv + γ ·Pcpadv

α +β + γ·bidcadv

A higher rating means that this particular cadv is more eligible for the user’s needs(i.e., creq); α , β and γ are weight factors which enable the aui to profile its requestaccording to its owner ui needs regarding the semantic similarity, quality and priceof a cadv, respectively; Mcreq,cadv represents the content correspondence factor M,but only cadv with M higher than threshold Mmin are considered; Qcpadv and Pcpadvrepresent the quality and price ratings of a particular cpadv, respectively; bidcadv isthe bid value for advertising a cadv in the SPPCA auction.

An illustration of the content discovery process by means of simple exampleshown in Figure 7.8 follows. The input for the discovery process is the CFP mes-sage sent by the aui where the following matching parameters, along with the creq,are defined: α = 5, β = 2, γ = 8 and Mmin = 1. The required content description(creq) and three different advertised content descriptions (cadv1 , cadv2 and cadvn ) arethe same as shown in Figure 7.7. The Qcpadv , Pcpadv and bidcadv for all the cadv arerandomly defined as shown in Figure 7.8. Thus, cadv1 is advertised by cpi with ahigh quality rating, but expensive, opposite of cadv2 which is advertised by cpi witha lower quality rating, but very cheap. The cadvn is advertised by cpi with both qual-ity and price rating somewhere between ratings for cpis that advertised cadv1 andcadv2 . Additionally, the acpadvn

made the highest bid in the SPPCA auction, whilethe acpadv2

made the smallest bid. The Rcadv calculation shows that the best final rat-ing does not achieve the cadv1 whose description is semantically exact in relation tothe required content description (creq), but cadv2 whose description is semanticallysimilar to the required content description (creq). This is the consequence of the facthow the aui has set the matching parameters (i.e., {α,β ,γ,Mmin}): it was lookingfor a cheap cadv and it was not very concerned with the cadv quality, while semanticmatchmaking was rather relaxed.

7.3.3 Conclusion

An agent-based approach for modeling and analysis of telecommunication e-marketsbased on Telco 2.0 paradigm is proposed. In particular, B2C e-market for con-tent trading by creating a novel auction, SPPCA auction, which merges togetherprovider’s content advertising and user’s content discovery is presented. The SP-PCA auction is modeled to reward low cost and high quality of content providers


Fig. 7.8: An example of the discovery process

(i.e., content providers with better performance rating can put smaller bids and staycompetitive). By contrast, the SPPCA auction punishes high cost and low quality ofcontent providers (i.e., content providers with lower performance rating must placehigher bids to stay competitive). The autonomous semantic-based content discov-ery based on the SPPCA is a better solution within the telecom sector comparedto the keyword-based discovery based on the classic PPC auction which has a sev-eral shortcomings. First of all, there is a scalability problem. Namely, there are ahuge number of syntactically valid combinations which result in a vast number ofconcurrent PPC auctions (a separate PPC auction runs for each particular charactersequence and, thus, for every possible character sequence there is a separate auc-tion). Another problem is that separate auctions are held for synonyms. From thecontent providers’ point of view, it can be very complex and expensive for them to


bid in auctions for all synonyms. From the content requesters’ (i.e., users’) pointof view, it is very complicated to search all synonymous words when they requirea particular content. The last disadvantage of the classic PPC auction model weconsider here is competitor click fraud. This occurs when one company clicks on acompetitor’s advertisement to spend their budget with the long term aim of makingPPC advertising too expensive for them and therefore removing them as a com-petitor from the search engine’s results. The auction model proposed here, SPPCAauction, solves the shortcomings described above. The first problem of a vast num-ber of concurrent auctions is solved by having one broker (i.e., telco) running onlyone SPPCA auction and connecting content provider agent’s bids with their OWL-S descriptions and not a specific keyword. The second problem of running separateauctions for synonyms is solved by introducing the Semantic Web technology whichuses OWL-S descriptions to characterise advertised services. The third problem ofcompetitor click fraud cannot occur in the SPPCA auction model since a requestercannot predict which advertised content will be recommended as response to a re-quest. Namely, the answer to each new discovery request is calculated dynamicallyand depends on fast-changing variables which are unknown to all entities outsidethe broker (i.e., telco). Hence, a user cannot purposely cause the broker to chargethe targeted content provider by making a discovery request without the intent ofutilizing any content.

Acknowledgment

This work was partially supported by the Hasler Foundation project n. 11115-KGand by the Swiss State Secretariat for Education and Research project n. C08.0114,as well as projects 036-0362027-1639 “Content Delivery and Mobility of Users andServices in New Generation Networks”, supported by the Ministry of Science, Edu-cation and Sports of the Republic of Croatia, and “Agent-based Service & TelecomOperations Management”, supported by Ericsson Nikola Tesla, Croatia.

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