SHOMAS: Intelligent guidance and suggestions in shopping centres

This paper introduces the SHOMAS Multiagent System that provides guidance on leisure facilities and suggestions for shopping in malls. The multiagent architecture incorporates reactive and deliberative agents that take decisions automatically. The developed deliberative agent provides suggestions in execution time, with the help of case-based planners. This agent is described together with its guidance and suggestion mechanism. SHOMAS has been tested successfully, and the results obtained are presented in this paper

A Guiding Agent: smart dynamic technology for solving distributed problems.

Mobile technology is everywhere nowadays in the developed world. This technology is mature enough to support intelligent applications and smart devices. Over the last few years we have developed a number of applications for PDAs and Mobile phones. This abstract outlines an information system that incorporate a recommender agent that helps the users of a shopping centre to identify offers, to find people or to define a plan which in the shopping centre for a day. The multiagent architecture incorporates a smart deliberative agent that take decisions with the help of casebased planners. The system that uses past experiences to recommend future actions has been tested successfully

A multiagent recommending system for shopping centres.

This paper presents a multiagent model that provides recommendations on leisure facilities and shopping on offer to the shopping mall users. The multiagent architecture incorporates deliberative agents that take decisions with the help of case-based planners. The system has been tested successfully, and the results obtained are presented in this paper

Intelligent Guidance and Suggestions Using Case-Based Planning

This paper presents a multiagent system that provides guidance on leisure facilities and suggestions for shopping in malls. This paper presents a deliberative agent which incorporates a case based planner that provides suggestions in execution time. This agent is described together with its guidance and suggestion mechanism. The multiagent system has been tested, and the results obtained are presented in this paper

Adaptabilni java agenti – alat za programiranje multi-agentskih sistema

The main goal of this thesis is the creation o f the tool agent-oriented programming tool AJA. AJA is the acronym for Adaptable Java Agents. AJA consists o f two programming languages: - A higher-level language used for the description of the main agent parts. This language is called HADL, which is the acronym for Higher Agent Definition Language. - A lower-level language used for the programming o f the agent parts defined in HADL. This language is called Java+. It is actually Java enriched with the constructs for accessing higher-level agent parts defined in HADL. A translator from AJA to Java is implemented in the practical part o f the thesis. AJA agents have the following features: - Agent communicates with other agents using a construct called negotiation. The messages sent can be encrypted or digitally signed in order to ensure the security of the system. - Agent possesses adaptable  parameters and neural nets that adapt themselves when the environment changes. - Agent has reflexes, which are the reactive component o f the agent architecture. - Agent can perform its actions parallel. Actions execution is synchronized. - Agent is accessible via Internet, because it acts as a simple HTTP server. People can use this way to communicate with an agent. - Agent has Java Swing based graphical user interface. Its owner uses this interface to communicate with the agent. - Because o f the fact that Java-i- language extends Java, it is possible to use all useful Java features in the implementation o f AJA agents (e.g. JDBC for the database access). The thesis also presents an original approach of integrating artificial intelligence techniques, such as neural nets, with a programming language. Having the artificial intelligence components as a part of the programming language runtime environment makes their use straightforward. A programmer uses the language constructs that are implemented using the artificial intelligence without the need for understanding their background and theory. The thesis contains eight chapters and three appendixes. In the first chapter, an overview of agents and multi-agent systems is given. The second chapter surveys existing agent-oriented programming languages and tools. The third chapter introduces AJA and describes the architecture of AJA agents. The syntax and semantics o f AJA languages HADL and Java+ is described in the fourth chapter. The fifth chapter presents adaptable AJA constructs in more details. To demonstrate and test the created tool, a case-study multi-agent system has been implemented in AJA. There are four personal digital assistant agents in the system. The sixth chapter describes the example agents and positively evaluates the tool. In the seventh chapter the related work and tools are analyzed and compared to AJA. The last chapter concludes the thesis. The first appendix describes the implementation details of the AJA to Java translator. The second appendix is a guide for the installation and usage of the implemented AJA to Java translator. Finally, the third appendix describes step by step how to translate, compile, run, and use the example agents. The thesis contains many references, which include almost all the most important and the most actual papers in the field. The reference list can be found at the end o f the thesis.Glavni doprinos doktorske teze je napravljeni alat za programiranje agenata AJA . AJA - Adaptabilni Java Agenti je jezički alat za programsku implementaciju agenata Sastoji se od dva programska jezika: - Jezik višeg nivoa kojim se opisuju glavne kom ponente agenta. Ovaj jezik se naziva HADL - Higher Agent Definition Language. - Jezik nižeg nivoa koji služi za implementaciju pojedinih komponenti agenta specificiranih HADL jezikom . Ovaj jezik se najava Java+, jer je on zapravo programski jezik Java obogaćen konstrukcijama pomoću kojih je moguće pristupati komponentama agenta, definisanim u jezik u HADL. AJA agent poseduje sledeće osobine: - Sigurna kom unikacija sa drugim A JA agentim a koristeći mehanizam pregovaranja, šifrovanje i digitalno potpisivanje poruka. - Mogućnost adaptiranja na promene u okruženju u kom se nalazi, koristeći neuralne mreže i adaptabilne parametre. - Reaktivnost zasnovana n a kom ponenti zvanoj refleks. - Paralelno izvršavanje akcija agenta u z njihovu internu sinhronizaciju. - D ostupnost agenta preko Interneta. Agent se ponaša kao jednostavan HTTP server. Na ovaj način se drugim osobama omogućuje da komuniciraju sa agentom . - G rafički korisnički interfejs zasnovan n a Java Swing tehnologiji - Pošto se u program iranju agenta koristi Java+, moguće je uposliti sve pogodnosti Jave, kao što su na primer pristup bazama podataka koristeći JDBC , rad sa multimedijalnim sadržajem , itd. U tezi je predstavljen i originalni pristup integrisanja tehnika veštačke inteligencije sa program skim jezikom . U građujući kom ponente veštačke inteligencije u izvršnu okolinu je z ik a čini n jihovo korišćenje veom a jednostavnim . Programer ne mora da bude ekspert iz veštačke inteligencije a da pri tome koristi konstrukcije jezika koje su implementirane pomoću veštačke inteligencije. AJA specifikacija agenta se sastoji od HADL i Java+ delova. U tezi je implementiran prevodioc kojim se A JA specifikacija prevodi u skup klasa programskog jezika Java. Implementiran je i jedan multi-agentski sistem kojim se praktično pokazuje korišćenje i mogućnosti napravljenog alata D oktorska teza sadrži i detaljan pregled oblasti o agentskpj m etodologiji. O n a kruniše višegodišnji rad kandidata i njegovog mentora u ovoj sve značajnijoj oblasti računarstva. Teza sadrži o sam glava i tri dodatka. U prvoj glavi se opisuje oblast agenata i m ulti-agentskih sistem a. Pregled postojećih agentskih program skih jezik a i alata se daje u drugoj glavi. O pis A JA agenata i njihove arhitekture je dat u trećoj glavi teze. Četvrta glava se bavi sintaksom i sem antikom oba A JA jezika: H A D L -a i Jave+. Adaptabilni elem enti A JA agenata se opisuju u petoj glavi. U šestoj glavi je opisan m ulti-agentski sistem koji j e ujed n o i prim er prim ene A JA alata. A JA se sa drugim postojećim agentskim alatim a upoređuje u sedm oj glavi. Osma glava sadrži zaključak. N a kraju se u tri dodatka detaljno opisuju im plem entacija prevodioca A JA -e u Javu, instalacija prevodioca i korišćenje napravljenog m ulti-agentskog sistema respektivno. U doktorskom radu su korišćene i navedene brojne reference kojim a su obuhvaćeni gotovo svi najznačajniji i najaktuelniji radovi iz oblasti multi-agentskih sistema. Lista referenci je navedena na kraju teze

Balancing privacy needs with location sharing in mobile computing

Mobile phones are increasingly becoming tools for social interaction. As more phones come equipped with location tracking capabilities, capable of collecting and distributing personal information (including location) of their users, user control of location information and privacy for that matter, has become an important research issue. This research first explores various techniques of user control of location in location-based systems, and proposes the re-conceptualisation of deception (defined here as the deliberate withholding of location information) from information systems security to the field of location privacy. Previous work in this area considers techniques such as anonymisation, encryption, cloaking and blurring, among others. Since mobile devices have become social tools, this thesis takes a different approach by empirically investigating first the likelihood of the use of the proposed technique (deception) in protecting location privacy. We present empirical results (based on an online study) that show that people are willing to deliberately withhold their location information to protect their location privacy. However, our study shows that people feel uneasy in engaging in this type of deception if they believe this will be detected by their intended recipients. The results also suggest that the technique is popular in situations where it is very difficult to detect that there has been a deliberate withholding of location information during a location disclosure. Our findings are then presented in the form of initial design guidelines for the design of deception to control location privacy. Based on these initial guidelines, we propose and build a deception-based privacy control model. Two different evaluation approaches are employed in investigating the suitability of the model. These include; a field-based study of the techniques employed in the model and a laboratory-based usability study of the Mobile Client application upon which the DPC model is based, using HCI (Human Computer Interaction) professionals. Finally, we present guidelines for the design of deception in location disclosure, and lessons learned from the two evaluation approaches. We also propose a unified privacy preference framework implemented on the application layer of the mobile platform as a future direction of this thesis

What Airbnb Reviews can Tell us? An Advanced Latent Aspect Rating Analysis Approach

There is no doubt that the rapid growth of Airbnb has changed the lodging industry and tourists’ behaviors dramatically since the advent of the sharing economy. Airbnb welcomes customers and engages them by creating and providing unique travel experiences to “live like a local” through the delivery of lodging services. With the special experiences that Airbnb customers pursue, more investigation is needed to systematically examine the Airbnb customer lodging experience. Online reviews offer a representative look at individual customers’ personal and unique lodging experiences. Moreover, the overall ratings given by customers are reflections of their experiences with a product or service. Since customers take overall ratings into account in their purchase decisions, a study that bridges the customer lodging experience and the overall rating is needed. In contrast to traditional research methods, mining customer reviews has become a useful method to study customers’ opinions about products and services. User-generated reviews are a form of evaluation generated by peers that users post on business or other (e.g., third-party) websites (Mudambi & Schuff, 2010). The main purpose of this study is to identify the weights of latent lodging experience aspects that customers consider in order to form their overall ratings based on the eight basic emotions. This study applied both aspect-based sentiment analysis and the latent aspect rating analysis (LARA) model to predict the aspect ratings and determine the latent aspect weights. Specifically, this study extracted the innovative lodging experience aspects that Airbnb customers care about most by mining a total of 248,693 customer reviews from 6,946 Airbnb accommodations. Then, the NRC Emotion Lexicon with eight emotions was employed to assess the sentiments associated with each lodging aspect. By applying latent rating regression, the predicted aspect ratings were generated. With the aspect ratings, , the aspect weights, and the predicted overall ratings were calculated. It was suggested that the overall rating be assessed based on the sentiment words of five lodging aspects: communication, experience, location, product/service, and value. It was found that, compared with the aspects of location, product/service, and value, customers expressed less joy and more surprise than they did over the aspects of communication and experience. The LRR results demonstrate that Airbnb customers care most about a listing location, followed by experience, value, communication, and product/service. The results also revealed that even listings with the same overall rating may have different predicted aspect ratings based on the different aspect weights. Finally, the LARA model demonstrated the different preferences between customers seeking expensive versus cheap accommodations. Understanding customer experience and its role in forming customer rating behavior is important. This study empirically confirms and expands the usefulness of LARA as the prediction model in deconstructing overall ratings into aspect ratings, and then further predicting aspect level weights. This study makes meaningful academic contributions to the evolving customer behavior and customer experience research. It also benefits the shared-lodging industry through its development of pragmatic methods to establish effective marketing strategies for improving customer perceptions and create personalized review filter systems

Personalised privacy in pervasive and ubiquitous systems

Our world is edging closer to the realisation of pervasive systems and their integration in our everyday life. While pervasive systems are capable of offering many benefits for everyone, the amount and quality of personal information that becomes available raise concerns about maintaining user privacy and create a real need to reform existing privacy practices and provide appropriate safeguards for the user of pervasive environments. This thesis presents the PERSOnalised Negotiation, Identity Selection and Management (PersoNISM) system; a comprehensive approach to privacy protection in pervasive environments using context aware dynamic personalisation and behaviour learning. The aim of the PersoNISM system is twofold: to provide the user with a comprehensive set of privacy protecting tools and to help them make the best use of these tools according to their privacy needs. The PersoNISM system allows users to: a) configure the terms and conditions of data disclosure through the process of privacy policy negotiation, which addresses the current “take it or leave it” approach; b) use multiple identities to interact with pervasive services to avoid the accumulation of vast amounts of personal information in a single user profile; and c) selectively disclose information based on the type of information, who requests it, under what context, for what purpose and how the information will be treated. The PersoNISM system learns user privacy preferences by monitoring the behaviour of the user and uses them to personalise and/or automate the decision making processes in order to unburden the user from manually controlling these complex mechanisms. The PersoNISM system has been designed, implemented, demonstrated and evaluated during three EU funded projects

User decision improvement and trust building in product recommender systems

As online stores are offering an almost unlimited shelf space, users must increasingly rely on product search and recommender systems to find their most preferred products and decide which item is the truly best one to buy. However, much research work has emphasized on developing and improving the underlying algorithms whereas many of the user issues such as preference elicitation and trust formation received little attention. In this thesis, we aim at designing and evaluating various decision technologies, with emphases on how to improve users' decision accuracy with intelligent preference elicitation and revision tools, and how to build their competence-inspired subjective constructs via trustworthy recommender interfaces. Specifically, two primary technologies are proposed: one is called example critiquing agents aimed to stimulate users to conduct tradeoff navigation and freely specify feedback criteria to example products; another termed as preference-based organization interfaces designed to take two roles: explaining to users why and how the recommendations are computed and displayed, and suggesting critique suggestions to guide users to understand existing tradeoff potentials and to make concrete decision navigations from the top candidate for better choices. To evaluate the two technologies' true performance and benefits to real-users, an evaluation framework was first established, that includes important assessment standards such as the objective/subjective accuracy-effort measures and trust-related subjective aspects (e.g., competence perceptions and behavioral intentions). Based on the evaluation framework, a series of nine experiments has been conducted and most of them were participated by real-users. Three user studies focused on the example critiquing (EC) agent, which first identified the significant impact of tradeoff process with the help of EC on users' decision accuracy improvement, and then in depth explored the advantage of multi-item strategy (for critiquing coverage) against single-item display, and higher user-control level reflected by EC in supporting users to freely compose critiquing criteria for both simple and complex tradeoffs. Another three experiments studied the preference-based organization technique. Regarding its explanation role, a carefully conducted user survey and a significant-scale quantitative evaluation both demonstrated that it can be likely to increase users' competence perception and return intention, and reduce their cognitive effort in information searching, relative to the traditional "why" explanation method in ranked list views. In addition, a retrospective simulation revealed its superior algorithm accuracy in predicting critiques and product choices that real-users intended to make, in comparison with other typical critiquing generation approaches. Motivated by the empirically findings in terms of the two technologies' respective strengths, a hybrid system has been developed with the purpose of combining them into a single application. The final three experiments evaluated its two design versions and particularly validated the hybrid system's universal effectiveness among people from different types of cultural backgrounds: oriental culture and western culture. In the end, a set of design guidelines is derived from all of the experimental results. They should be helpful for the development of a preference-based recommender system, making it capable of practically benefiting its users in improving decision accuracy, expending effort they are willing to invest, and even promoting trust in the system with resulting behavioral intentions to purchase chosen products and return to the system for repeated uses