SATELIT-Agent: An Adaptive Interface Based on Learning ... - CiteSeerX

In Anthony Jameson, Cécile Paris, and Carlo Tasso (Eds.), User Modeling: Proceedings of the Sixth International Conference, UM97. Vienna, New York: Springer Wien New York. © CISM, 1997. Available on−line from http://um.org.

SATELIT-Agent: An Adaptive Interface Based on Learning Interface Agents Technology Irina Akoulchina and Jean-Gabriel Ganascia LAFORIA-IBP-CNRS (LIP6), University Paris-VI, Paris, France

Abstract. This article presents an adaptive interface agent for SATELIT, a system that integrates Artificial Intelligence methods and hypermedia technology. SATELIT is implemented on the Internet, represented as a set of interactive World Wide Web (WWW) pages with their own functionalities. The SATELIT learning agent was developed for adaptive interface maintenance and pursues either of two main goals, depending on the user’s intentions. First, it is capable of distinguishing the profile of SATELIT experts, whose purpose is to construct a SATELIT application, and it offers a special interface to them. The second aim of SATELIT-Agent is a response to the general hypermedia problem of “getting lost in hypermedia space”. With the help of a learning interface agent, SATELIT will infer an analogue of the user’s search requirements and help him to achieve it, proposing good navigation routes. In this manner SATELIT-Agent functions as an active browser, interactively assisting and guiding the user.

1 Introduction With the appearance of hypertext and hypermedia tools offering new text reading possibilities, a new information era has begun. But at the same time, a common problem about navigating and “getting lost” in the global hyper-space has become very essential. Especially now, when hypermedia use enormous information networks like the Internet, this problem is drawing more and more attention. In the Internet there is a fundamental imbalance between the extraordinary breadth of represented information and the limited amount of knowledge that manages the navigation. It is evident now that the technology responsible for the processes of management is far less efficient than the technology responsible for the information accumulation (see, e.g., Floridi, 1995). The question arises again: How to reduce the risk of user disorientation so that a naive user will not lose the right direction in the navigation process and ignore important information. To this end, different researchers have proposed various methods of adaptive hypermedia interfaces that could construct themselves according to the user’s needs, knowledge, and preferred navigation strategies. At present, all research in the field of adaptive hypermedia interfaces construction is adopting user-centered system design, a paradigm advocated by Norman and Draper (1986). As part of this new approach, the first direct manipulation interfaces appeared. Instead of communicating through a “linguistic” intermediary such as command languages, menus or forms, the user directly initiates tasks and monitors events. He or she can, for example, activate hypertext links, move backwards,

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or search for needed information. For a long time, interface designers have been opposing these two interaction paradigms, direct manipulation and indirect management. But recently a new, complementary style of interaction has been proposed: The user engages in a cooperative process in which a human and one or more learning intelligent interface agents both initiate communication, monitor events and perform tasks. The objective of this new complementary interaction style is to allow a user direct manipulation and, at the same time, to offer him a new intelligent intermediary that assists him or her in indirect manipulation. With the apparition of the new learning agents conception, researchers have begun to place the emphasis of their work on learning from user activity and on developing systems based purely on user criticism. Having studied the particular user, his habits and preferences, this personal assistant, invisible to the user, interprets his directives and anticipates the next action. Thus, learning interface agents act as personalized assistants which “look over the user’s shoulder” and learn the user’s interests in order to act on his behalf. Agent programs differ from regular software in that an agent is (1) autonomous, in that it can sense the current state of its environment and act independently to make progress toward its goals; (2) adaptive, in that it is capable of learning and of adapting to situations, and (3) non-restricting, in that it does not insist and the user can always ignore the the suggestion and take a different action. For acquiring information about the user, there are four sources of information for interface agents (see Figure 1): observation and imitation of repetitive user actions; analysis of positive and negative user feedback; learning from explicit user instructions; and multi-agent collaboration. interface

User

Application 1 2

3 explicit instructions

feedback analysis

action observation

4 Agent i

multi-agent colaboration

Agent j

Figure 1. Four knowledge acquisition sources for learning interface agents.

The application domains for agents are strikingly varied. Agents have found employment in mail management (Maes, 1994), meeting scheduling (Mitchell et al., 1994), and information retrieval in the Internet jungle (see, e.g., Armstrong et al., 1995, or Edwards et al., 1996). They help users to learn software (Selker, 1994), handle the multimedia virtual conference (Riecken, 1994), etc. In our research group, we are developing a learning agent for an adaptive interface of the SATELIT system, which has been implemented on the Internet. With the help of this agent, SATELIT will be able to learn details about its users and consequently to distinguish among the users and among their goals. An analysis of all possible SATELIT user profiles is given by Akoulchina and Ganascia (1996). For two main user profiles—experts and novices—our SATELIT-Agent proposes different interfaces, in accordance with their intentions. For the expert, it first verifies the user’s competence level in order to permit this expert to construct some SATELIT application; later, it proposes spe-

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cial application development tools. The problem of expert profile distinction is very important in the Internet environment as it addresses a question of system security. For the novice, SATELITAgent plays the role of an intelligent assistant that learns a search goal from the user’s ordinary browsing actions. In this way, our SATELIT-Agent tries to solve the problem mentioned above by reducing the risk of user disorientation in the browsing process. Let us begin by considering some original aspects of our SATELIT system and explaining why it needs an adaptive interface. We will then demonstrate in detail our use of the learning interface agent techniques for two user profiles—expert and novice.

2 Original Aspects of the SATELIT System The SATELIT system belongs to the class of hypermedia systems that hold both textual information and knowledge about the domain. SATELIT allows the acquisition of a formalized knowledge base and the automatic indexing of Internet hypermedia documents according to the contents of this knowledge base in order to facilitate information access (see Faron and Kieu, 1995, and Faron et al., 1996, for details). In this way, our system is also capable searching for precise information, since the knowledge base structure provides an initial hypermedia network. The application domains of SATELIT are the sciences of analysis and observation, like botany and zoology. Those domains are characterized on the one hand by a great amount of information scattered in catalogues, and on the other hand by a taxonomic organization of multimedia data. Bringing this information together on an electronic medium is a crucial task. In these domains, knowledge objects are clustered according to their similarities to make up classes, called taxa, organized in hierarchical structures, called taxonomies. The taxum description is a synthesis of the common anatomy of its instances and characteristics of these instances. An appropriate knowledge representation formalism here is the Conceptual Graphs model suggested by Sowa (1984), which allows the elaboration of structured descriptions and the subsequent indexing of multimedia documents on the basis of these descriptions. The general structure of the SATELIT architecture is shown in Figure 2. Different types of information nodes appear in the network; they correspond, and are indexed to, different parts of the knowledge base: taxa nodes, concept nodes, taxonomy nodes and the terminology node.Taxa nodes are compound nodes, their different kinds of description being complementary: In addition to a structured description (a conceptual graph), hypertextual and graphic descriptions are associated with each taxum. A conceptual graph allows formal manipulation of a taxum description but does not account for all shades in a textual or graphic description. Concept nodes are also compound: Specialized texts and sketches clarify the meaning of concept types and references appearing in conceptual graphs describing taxa. The terminology node is composed of several hypermedia glossaries indexed to the concept types lattice. In order to handle the acquired knowledge, SATELIT offers information retrieval and object identification tools. Thus the originality of SATELIT consists in the integration of efficient hypermedia information access procedures that require reasoning on previously acquired formalized knowledge.

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Discrimination Knowledge Representation and Acquisition

Taxonomy

Taxum

Object Identification

Photo, Sketch

Conceptual Graph

Link Typing Information Retrieval

Terminology Hypertext Description

Concept Lattice

Concept Referents sketch, photo

Glossaries Hypertext Description

hypertext, sketch

Figure 2. The SATELIT architecture.

Using the Java language, we are developing SATELIT in the Internet as a set of interactive World Wide Web (WWW) pages and special windows with their own functionality. Some Web pages contain interactive terminology glossaries, other pages contain hypertext and hypermedia information about taxa. Each taxum has its own special window holding an interactive conceptual graph. Two other windows represent a taxonomy and a concept lattice. The procedure of information retrieval is maintained in a special SATELIT window; it will be discussed below. An example of some SATELIT windows and Web pages is shown in Figure 3. There are two main types of SATELIT user profiles, which are associated with different experience levels: an expert in one of the domains considered, whose goal is to construct an application with the help of SATELIT, and a novice who consults hypermedia using some information search strategy. For these different user profiles, SATELIT-Agent offers different interface tools. For an expert it is necessary, first, to confirm their level of expertise and, second, to offer them concrete tools, so that they may access the object taxonomy and add new taxa, acquire new knowledge about them, and construct new conceptual graphs representing taxa, hypertexts and sketches associated to an object, as well as “active” glossaries. For a user who consults existing hypermedia and searches for some specific information (with the help of formalized knowledge), SATELIT offers possibilities of indexing, composing search requests and consulting glossaries. But such flexibility always has the disadvantages discussed above: Users have great difficulty in formulating their search goals precisely, and they tend to get lost quickly. Our SATELIT-Agent pursues the aim of actively helping the user to find the necessary information and decreasing the risk of disorientation. Let us consider the details of SATELIT-Agent’s work for these two different user profiles.

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Figure 3. Several SATELIT windows and Web pages.

3 SATELIT-Agent for the Expert User Profile As was mentioned above, the SATELIT system organizes hypermedia information available on the Internet in the form of taxonomies and adds knowledge that manages information access. In the observation sciences, such as botany or archaeology, taxonomic data organization is es sential. For instance, in the domain of orchideology, which we have worked with, the taxonomy makes it possible to foresee more easily the possibilities of hybridations between different orchid sorts. But the classification problem in these domains is very complicated, as there are no general methods. The results derived from the same observation set may be interpreted differently according to the “classification school” that the expert belongs to. It is therefore necessary not only to confirm an expert user profile, but also to distinguish among different schools in order to propose to them different SATELIT application development tools. Furthermore, the expert distinction problem is crucial in the Internet environment. Only experts can access applications system construction. To permit a user to add a new taxum to the existing taxonomy or to acquire new associated knowledge, it is necessary to be assured that this user has competence in the domain under consideration. This task is closely linked with the problem of Internet security. Just asking for user passwords is not sufficient for expert recognition. As the number of Internet users is enormous, some of them, who are experts in the domain under consideration, may wish to complete a taxonomy by adding new information or to add new knowledge into existing taxa. For the distinction of the user’s expertise level, a competence confirmation by identification technique was developed.

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Discriminant features tree taxum i

distance User response

distance

i

feature

feature l

distance

feature

n

feature

taxum n

distance

feature

taxum j

taxum l

taxum p

taxum

m

feature taxum

q

x

identified taxum

Figure 4. The competence confirmation by identification process.

Object identification in SATELIT is based on a special knowledge acquisition method developed by Aimeur (1994). It is based on a discriminant features tree. This tree is constructed at the time of knowledge acquisition about a new taxum. It represents a hierarchy of the features (at least one feature in each tree level). The feature distinguishes the taxum from its brothers in the taxonomy. It has one of the following forms: 1. [concept i] → (composed) → [concept j], where the “composed” reference is “present”, “absent” or “unidentified”, or 2. [concept i] → (characterized) → [concept j], where the “characterized” reference may be any of several different characteristics, such as “value”, “form”, “number”, or “texture”. To go down this discriminant features tree, the agent asks the user questions about the presence of the concepts mentioned in each feature, or about the values of the concept’s characteristics. At the end, the tree leaf holds a taxum name with the desired discriminant features. The agent asks the user to give this name and compares the response string with the real value. The agent analyses the user’s response according to the fast algorithm for finding the nearest neighbor of a word in a dictionary that was proposed by Bunke (1993) and calculates the error scores—the values that reflect the distances between the user’s response and all taxa names (see Figure 4). If the user is wrong and the lowest error score exceeds a certain established limit, SATELITAgent does not confirm the expert status for this user and denies the user access to the application development SATELIT tools. If the minimal distance is the distance between the user response and the true response (distance x in Figure 4), the agent confirms the expert profile. The expert then has the right to: add a new taxum to an existing taxonomy; introduce new knowledge to existing conceptual graphs representing taxa; introduce new hypermedia information for taxa; and introduce new information for glossaries. In the other case, if the smallest distance is between the false user response and one of the taxa in the taxonomy (distances i, l or n in Figure 4), the agent concludes that from the same observation feature the user constructs a different classification from the proposed one. This means that the user is an expert but belongs to a different classification school. In this case, SATELIT-Agent repeats the expert confirmation by identification process again for another discriminant features tree, as SATELIT holds in its knowledge base several taxonomies (and, thus, several discriminant

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features trees) for the same domain field. If this iterative process is completed successfully once, the agent finds a taxonomy that agrees with the classification school of this expert and offers him or her the SATELIT application development tools mentioned above. Otherwise, the agent allows the expert to create a new SATELIT application—a new taxonomy in the domain field under consideration—and adds it to its taxonomies base.

4 SATELIT-Agent for the Consulting User Profile The user consulting a SATELIT application on the Internet environment searches with one or more of the following goals: to identify an object with certain known properties; to find precise SATELIT hypermedia information about a taxum; to find other information about a taxum available on the Internet; to consult the dictionary in the domain field under consideration; and to consult the catalogue of authors of the taxonomy classification. It is evident that such a search in the large and complex SATELIT knowledge bases is a very difficult process. The user always has difficulty describing his or her search goal directly and precisely. The system proposes some tools such as Retrieval, Concept Lattice, Identification, and Glossary, which help to formulate a request, but the user may easily get lost in the huge amount of concepts, references, characteristics and taxa names. Furthermore, studies (see, e.g., Fischer and Nieper-Lemke, 1989) have shown that browsing is a strategy that is preferred over analytic methods that require a formulation of wellstructured queries. This means that browsing is seen as a more natural and effective process when the user is uncertain about the target description. The user prefers to repeat the browsing process iteratively, skimming the results and evaluating and refining the search goal at each step. Our approach involves inferring the users’ search goals by learning their repetitive browsing actions. The aim of our SATELIT-Agent is to add an active component to the existing SATELIT and Internet browsing tools in order to anticipate the next browsing action. We distinguish two types of agents: those who use the previously acquired domain knowledge and those who do not. The agents without pre-acquired knowledge have been developed for active browsing on the Internet. These agents (see, e.g., Lieberman, 1995; Armstrong et al., 1995; Edwards et al., 1996) combine machine learning techniques with information filtering methods. They track users’ behaviour and attempt to anticipate their next Internet browsing actions by doing concurrent, autonomous exploration of links starting from the user’s current position. The agents of this type are distinguished by the level of “browsing autonomy”, the nature of the feedback from the user, and the machine learning techniques used. By contrast, the agents of the second type, who have explicit knowledge models of the particular domain, work in the environment of local systems with a limited number of users (see, e.g., Drummond, 1995). The principal idea behind SATELIT-Agent development is to combine the advantages of these two types of agents. Our agent is applied not to all Web pages but to a subset of pages that contain SATELIT hypermedia information. In addition, it is based on a precise model of SATELIT knowledge about the application field. In this way, it integrates the methods of analysis of the user’s behaviour with the techniques of knowledge based systems. Like the majority of the learning interface agents, SATELIT-Agent for the consulting user profile observes and imitates the user’s regular browsing actions (the source 1 in Figure 1) and constructs the adaptive user model after inferring the user’s search goal analogue. The next browsing actions predicted by SATELIT-Agent appear in the special window called “Agent sug-

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gests”. The agent displays these suggestions according to their weights, as it estimates their relevance to the user. But the agent’s suggestions are not obtrusive, as only the user is responsible for the choice of next browsing action. The agent gradually gains competence also by analyzing the user’s feedback, i.e, positive or negative responses to proposed agent recommendations. Such criticism dependency is the second source of knowledge acquisition in the schema of Figure 1. In addition, as a third source, it is possible for the user to instruct the SATELIT agent explicitly. Thus, the user can create a hypothetical situation and show the agent what should be done. The interface of the consulting user profile and SATELIT-Agent is presented in Figure 5. SATELIT-Agent has to work in real-time mode and to make its suggestions while the user is browsing the system. This means that it must be transparent and use only the time that is available between the user’s actions. In order to satisfy this aim and to reduce the time for the agent’s work, the SATELIT-Agent search mechanism is composed of three main parts: an inference engine, a pattern matcher and a confidence calculator. The general architecture of SATELIT-Agent is presented in Figure 6. The inference engine induces an analogue of the user’s search goal from observation of their browsing actions by constructing a browsing action pattern and inferring a type of matching procedure for this pattern. Constructed patterns are held in the Working Memory. The pattern description has the form “property = value” and contains information about the number of the executed action, the tool used, the action type and the action’s attribute type. The types of action and attribute depend on the concrete action performed by the user to the current tool. An attribute may be of one of three types: a taxum, a concept, or a triplet of the form “concept—relation— concept”. For instance, while the user constructs a request in the Retrieval tool in order to find the taxa which have a staminod in the shield form, the pattern attribute has a triplet of the form “staminod—form—in shield”. An attribute of the concept type is constructed, for example, when the user clicks the stamen link in the Web page dedicated to the orchis taxum (this action means that the user is searching for explication of the stamen concept in the Glossary). In this way, the Working Memory also keeps the traces of executed actions, so that the agent can reason about the entire chain of actions that led directly to the current one. Furthermore, owing to this action trace, the agent can detect patterns in the user’s search, even when the user is interrupted by spurious exploratory browsing actions.

-

SATELIT agent Search goal

Normal browsing actions

infering

SATELIT’s windows & WWW pages

Consultant user profile displaying User feedback

Search goal’s analogue "Agent suggests:" window

Next Attribut Attribut Refer action type name name Go HT Go HT Select Select Go Lattice

taxum taxum concept caract

orchis ophrys caudicule presence labelle form

concept caudicule

Figure 5. User-agent interface for the consulting user profile.

conf 0.92 0.92 0.78 0.71 0.70

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The general idea for reducing the duration of the agent’s work is to be highly selective about which pattern properties are proposed to the pattern matcher and which matching procedure is used. Among the possible matching procedures are the following: “Find taxum name”, “Find characteristics for a given concept”, “Find taxum son”, and “Find concept’s synonyms”. The pattern matcher carries out a proposed matching procedure, finds taxa and concepts related to the properties under consideration, and estimates their relativity measures. The relative attributes (taxa, concepts and triplets) are those connected by a path in the Canonical Graphs, in the Concept Types Lattice or in the Taxonomy, the three main structures that represent the SATELIT knowledge base. The relativity measure, also called importance (Imp(Ai)), expresses the closeness of a relative attribute Ai to a direct attribute A (chosen by the user in an action); it ranges between 0 and 1. The importance of the direct attribute is 1. The importance equals 0 for a relative attribute that is present in the Working Memory but not connected to the current action. The importance of the relative attributes increases with lower (more specific) levels of the Canonical Graph, the Lattice, or the Taxonomy. The default importance values change gradually depending on the user’s feedback. If the user does not accept an agent’s advice, the importance of the proposed attribute is decreased by 0.5; otherwise the importance is increased by 0.5 and becomes equal to 1.5. The weight W of an attribute Ai connected to an Action M is the sum of its old weights (calculated for other Actions {m1,...mk} to which the attribute Ai has been connected) and the product of the frequency measure of the attribute Ai and its current importance : k

W (Ai,,M) = frequency(Ai) / n • Imp (Ai) + Σ W(Ai, mj) , j =1

where frequency(Ai) is the number of appearances of the attribute Ai and n is the number of all attributes’ appearances in the Working Memory. The names of the identified related taxa, concepts and triplets and their calculated weights are added to the Working Memory; they represent statistics of the attributes considered in the browsing session. Finally, a special engine calculates a confidence level conf for a suggested action M using an attribute Ai. It takes into consideration the statistics of related properties and their influence on executed actions : N/Ai => M • WAi conf (Ai, M) = N where N is the number of actions executed during the given browsing session, N/Ai => M is the number of times that an attribute Ai has been the cause of the appearance of the action M, and WAi is the weight of Ai calculated before. These confidences are calculated for all actions related to a given situation. After obtaining the result, SATELIT-Agent posts a list of proposed actions with related attributes and orders them according to its confidence measure. The user may choose one of the proposed actions (in this case, he gives the agent new feedback for learning), or he may ignore these suggestions and execute other browsing actions. Figure 5 gives examples of an agent’s suggestion for a user who at that moment is in the Retrieval tool and is selecting the next action. At the beginning of a browsing session, when the Working Memory does not have much information, the agent cannot make strong suggestions about next actions. But with time it accumu-

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Inference Engine Matching Procedure .

browsing actions

Pattern

Tools . . Lattice

Hypertext Retrieval

SATELIT base for Matching Tools’ attribute base triplets taxa

Working Memory

feedback Ordered list of suggested actions

pattern descriptions taxa statistics concepts statistics triplets statistics

related actions

concepts

Taxonomy ..

Conceptual Graphs .

related taxa related concepts related triplets

Confidence Calculator

Figure 6. The SATELIT-Agent architecture (in the mode of consulting user profile interaction).

lates more knowledge and becomes better able to infer an analogue of the user’s search goal. The user’s actions implicitly carry information about his or her search goal, because they have been deliberately chosen to serve the user’s interests. However, such freedom of choice brings some important problems that are connected with ambiguousness and noisiness of learned information. As the user has many possible choices of actions, concepts and taxa at each browsing step, the agent may either propose many equally plausible explanations for a choice or not find any explanation. Noisy information arises when the user changes his or her search goal too quickly. One way to reduce the ambiguity is to present less information at each browsing step. Our agent takes the possibility of misleading actions into account by decreasing the importance of old information. SATELIT-Agent weights properties according to their frequency of appearance; therefore, the weights of the properties that are closely related to the inferred search goal analogue will be augmented continually, whereas the weights of properties of items that are visited randomly will not. Let us consider an example of a typical browsing process in the Orchideology domain, where the user tries to identify a plant and SATELIT-Agent attempts to infer an analogue of the search goal. The user begins by searching for information about a certain orchid with an unknown name. First, he sees a general Web page dedicated to the Orchids family and a Taxonomy tool in a special window. The user reads this page and understands that the labellum is the most informative characteristic of an orchid. He clicks on the labellum link and goes to the Glossary Web page that explains this notion and demonstrates some images of different labellum forms. Now it is clear to the user that his particular flower has a labellum in the sabot form and that it has a white color. At the moment when the user has performed his browsing action, the agent keeps its trace in the Working Memory, searches related attributes for the labellum concept, and calculates their weights. The first agent’s suggestions are ready: It proposes using the Retrieval tool and to compose one of the three following requests: to search taxa with a labellum in strip form, with a red labellum or a with very big labellum. The user does not accept these recommendations, because he is interested in a sabot form of labellum. Nevertheless, the user decides to apply the Retrieval tool in order to find taxa satisfying the condition “labellum—form—sabot”. SATELIT finds in response several relevant taxa: Cypripedioideae, Cypripedium, Phragmopedilinae, Phragmopedium, etc. (see Figure 7).

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Figure 7. A fragment of the user dialogue with the Retrieval tool.

The user is perplexed: Which of these taxa should he choose? The agent tries to help him and suggests selecting the Cypripedium taxum (as this genus is the first in the list of the relevant taxa that belong to a lower level in the Taxonomy and, consequently, have maximal weights). At this point, the user accepts the recommendation, clicks on it in the Retrieval tool, and visits Cypripedium’s Web page. The user sees the pictures of this genus of flowers and understands that this is not the desired plant. At the same time, the agent prepares new suggestions: to consult the Web page of Phragmopedium; to formulate the query “labellum—color—yellowish”; or to choose the “labellum—value—big” triplet in the Retrieval tool. The user accepts the first recommendation, visits the Phragmopedium page, and finds the flower he or she was trying to identify. The browsing process, which has been accelerated by the agent, is completed, and the user has received significant help.

5 Conclusion In this paper we have presented a model of an adaptive interface for the Internet-based SATELIT system that is based on a metaphor of a new complementary interaction style that involves intelligent agents. We have demonstrated that hypermedia authoring or browsing systems

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on the Internet seem to be especially in need of adaptive interfaces because of their complexity and their wide range of users. We analyzed two SATELIT user profiles—expert and consulting— and presented a generic architecture of the agent for each of them. SATELIT will be able to recognize and confirm the expert user profile; and for the consulting user profile it will propose appropriate interfaces according to the observable actions and feedback of the user. At the present time we are working on the problem of designing efficient procedures for matching and for calculating relatedness measures. Also, we have designed some studies for testing SATELIT-Agent in two modes of work. In particular, we are interested in finding out how often a user’s search goal can be inferred from normal browsing actions by the agent before the user himself achieved the goal. Another aim of our studies is to answer the question: How much can SATELIT-agent reduce the number of browsing actions required to achieve the user’s search goal?

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