Lashkari, Y., Metral, M., & Maes, P. Collaborative interface agents. In Proc. AAAI  Home/Search Document Details and Download
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Unifying Agent Systems - d'Inverno, Luck (2003) (Correct)
....not only been in artificial intelligence but in other areas of computer science such as information retrieval and software engineering. Indeed, there is now a plethora of different labels for agents including autonomous agents [32] software agents [22] intelligent agents [59] interface agents [37], virtual agents [1] information agents [35] mobile agents [57] and so on. The diverse range of applications for which agents are being touted include operating systems interfaces [21] processing satellite imaging data [54] electricity distribution management [31] air traffic control [34] ....
Y. Lashkari, M. Metral, and P. Maes. Collaborative interface agents. In Proceedings of the Twelfth National Conference on Artificial Intelligence, pages 444--449, 1994.
Search Engine-Crawler Symbiosis - Pant, Bradshaw, Menczer (2003) (Correct)
....on link based criteria [15, 19] Others exploit lexical and conceptual knowledge such as those provided by a topic hierarchy [13] Still others emphasize contextual knowledge [1, 28] for the topic including those received via relevance feedback. Our work also relates to collaborative filtering [26, 5, 25] since the queries submitted by the users, help in preparing the collection for similar queries by other users in the future. The use of queries to collaborate between users has been studied in the past [21] 5.2 Future Work One obvious extension to the work is to integrate the crawler and the ....
Y Lashkari, M Metral, and P Maes. Collaborative interface agents. Media lab technical report, MIT, 1994.
A Medical Digital Library to Support Scenario and.. - Chu, Johnson, Kangarloo (2000) (Correct)
....supports an atomic query mode where each information access is taken as a new request, independent of previous requests, and returns at lists of ranked resources. The intelligent agent community has introduced methods for assisting users with maintaining focus in large information spaces [7], 8] 9] These techniques revolve around agents that model users and tailor the users perception of the information space. The tailoring attempts to provide contextual focus and relevancy rankings of information. The agents maintain a persistent (and evolving) model of the user to base ....
Y. Lashkari, M. Mentral, and P. Maes, \Collaborative interface agents," in Twelfth National Conference on Arti cial Intelligence, 1994.
On How Agents Make Friends: Mechanisms for Trust Acquisition - Esfandiari, Chandrasekharan (2001) (1 citation) (Correct)
....of the buyer or the seller before initiating a commercial transaction [28] From an AI perspective, an evaluation of trust is needed to facilitate task allocation, and therefore cooperation, between agents in an open, multiagent, system setting. In the context of collaborative interface agents [21], it is important for agents to be able to identify their true peers, i.e. the ones that can give them relevant advice, and only relevant advice, and information to increase productivity. Different authors have given various definitions for the term Trust, as well as properties that trust must ....
....with this type of reasoning all too well. One could also imagine reinforcement learning techniques: John passes the ball, Mary scores, so John receives positive feedback. For a more complex use of Bayesian networks to model agent relationships, see [2] 3. 2 Trust Acquisition by Interaction In [21], a simple set of protocols are proposed to allow learning interface agents to collaborate in order to learn from each other. It was important for such agents to determine which fellow agents to trust, since the users they were derived from possibly had very dissimilar behavior. The main protocols ....
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Lashkari, Y., Metral, M. and Maes, P.: Collaborative Interface Agents. In Proceedings of the Twelfth National Conference on Artificial Intelligence, AAAI-Press, 1994.
Applying Talking Head Technology To A Web Based Weather Service - Dam, de Souza (2002) (Correct)
....and response toward a given weather condition. This information will be used in future dialogues with that user by the VWW when expounding upon the same weather conditions. Our learning method is similar to the single user s agent learning method for the electronic mail domain employed by Maes [14]. But, instead of learning the user s reaction towards incoming messages, the system learns the emotions and favourite activities of the users. Users Tell me today s weather Dialog Manager Personalization Module Modification Module The Virtual Weather Woman BRAIN TTS Module Typing ....
Y. Lashkari, M. Metral, and P. Maes. Collaborative Interface Agents. In Proceedings of the Twelfth National Conference on Artificial Intelligence, volume 1, Seattle, WA, 1994. AAAI Press.
Task Automation by Interpreting User Intent - Dixon (Correct)
....automation tasks, but there are potential applications to segmenting speech for better phoneme alignment in the speech recognition domain. We also expect that incorporating multiple demonstrations to improve system performance will prove useful in many areas, such as Intelligent Assistant Agents [15]. These agents assimilate multiple examples of user behavior and attempt to help the user automate daily computer based tasks. Devising an algorithm that determines the underlying structure of the task in a reasonable amount of time with a small set of examples would be extremely useful, ....
Y. Lashkari, M. Metral, and P. Maes. Collaborative interface agents. In Proceedings of AAAI'94 Conference, 1994.
A Multiagent System for Users to Share Their Web - Experience On Search (Correct)
....lime bul cannol ezplore new and possibly belief search space for ils user s search query. And when user execules informalion search upon lhe Web, in mosl cases he she maybe Wahl lo gel some helps beyond his her Web experience. A possible solulion for lhis silualion is lo develop a mulliagenl syslem[6, 14] among a group users in which lhe agehis communicale lo each olher and cooperate on lhe users informalion search lask. The basic idea underlying lhe proposed mulliagenl archileclure is lhal each user in lhe group has his her own personalized agenl lo learn and manage his her informalion ....
....user s information interests and share the learned infor mation among all the group users information search activities. By sharing other users Web experience, the personalized agent will serve its user much well on exploring some possibly good search space that the user has never experienced[6]. 4.1 Multiagent system structure We take the design policy in our previous systems [19, 20] that use Web browser as the only user agent interface. As shown in Figure 2, every user runs one of their own copy of the personalized agent and the agent is personalized to and responsible for its ....
[Article contains additional citation context not shown here]
Y. Lashkari, M. Metral and P. Maes, Collaborative Interface Agents, Proceedings of AAAI'9d Conference, 1994.
Design Visual Thinking tools for Mixed Initiative Systems - Pu, Lalanne (2002) (Correct)
....is that all of our visualization methods come with inference en gines so that solutions can be automatically generated. In addition, visual cues help humans think and solve problems, not just clarify ideas. Intelligent agents We can find many similar examples in the area of intelligent agents [ 12,14,22] which treat problem solving. There are mainly two classes of agents: those self learning agents that watch over the shoulder of a user and become mined to perform the tasks for the user, and those autonomous ones that from the beginning solve problems for users. Our work differs from intelligent ....
Max Metml Yezdi Lashkari and Pattie Maes. (1994). Collaborative interface agents, In Proceedings of National Conference on Artificial Intelligence.
Dynamic Information Filtering - Baudisch (2001) (1 citation) (Correct)
.... to several application areas including scientific publications [Luh58] technical memos and reports [FD92, FC97] Usenet news [FS91, Bac91, SK92, Ste92b, Bac92, JH92, SM93, Mae94, KHL 94, RIS 94, MS94, Lan95, YG95, Moc96, MRK97, MRK 97] electronic mail [Mye80, MGT 87, Pol88, GNOT92, Ter91, Ter93, LMM94] books [Ric79b, MR98] application program know how [LN98] the finding of experts [SW93, KSS96] Web pages [RM96, HT96, Bal97, THA 97, PB97, RP97, Bie98] classified ads [GGKS95] movies [Kay95, HRF95, AKK97, AKK98] music [Sha94, SM95, Loe92] and TV [EHWS96, DtHh98, Bau96] In Chapter 2, we ....
Y. Lashkari, M. Metral, and P. Maes. Collaborative interface agents. Proceedings of the Twelfth National Conference on Artificial Intelligence, pages 444-449, 1994.
MANTHA: Agent-based Management of Hypermedia Documents - Roberto, Mea, Di Gaspero.. (Correct)
.... maintains an internal state, controls actions and operates without any external intervention [Cas95]# ffl social ability: interacts on a peer to peer basis with other agents as well as with humans by means of an Agent Communication Language, ACL [GK94] or proper interfaces respectively [LMM94]# ffl reactivity: can react and respond to changes# ffl pro activeness: can take the initiative, making choices and performing actions on a goaldirected basis. 2.2 Agent Languages Akey pointisthewayagents interact with each other, i.e. via a shared high level communication language. Agents ....
....2. 3 Agent Architectures An interesting topic is how the agents can be arranged into social architectures [GK94] In an information sharing model, for example, agents advertise about their capabilities and needs, so that other agents can use this information for their own tasks ( EW94, LMM94] We take an alternative approach, organizing agents in groups, called federated systems [GK94, WWC92] Figure 2 illustrates an example federation: In such a system, agents communicate by means of Agent Resource Broker Agent A Agent B Agent C Agent D Agent E Federation entry point ....
Y. Lashkari, M. Metral, and P. Maes. Collaborativeinterface agents. In Proceedings of the Twelfth National ConferenceonArtificial Intelligence, Seattle, WA,Vol. 1, pp.444-- AAAI Press, Menlo Park, CA, USA, 1994.
A Survey on Personalised Information Filtering Systems for the.. - Aas (1997) (Correct)
....from friends. This information is processed, and a decision is made on which movie to see. The recommendations of some friends are trusted more than recommendations from others. This trust is weighted accordingly in the decision making process. Examples of social filtering systems are WebHunter [16] and Tapestry [11] WebHunter WebHunter (also known as WebHound) is one of the social filtering systems that has been developed. In this system, the user submits a list of pages together with the ratings of these pages. The agent finds other users with similar ratings and suggests unread pages ....
Y. Lashkari, M. Metral and P. Maes, Collaborative Interface Agents, In Proc. of the 12th Nat. Conf. Artificial Intelligence, 1995.
Understanding Email Use: Predicting Action on a.. - Human-Computer.. (2005) (Correct)
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Lashkari, Y., Metral, M., & Maes, P. Collaborative interface agents. In Proc. AAAI
Adaptive Help for e-mail Users - Katerina Kabassi University (Correct)
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Lashkari, Y., Metral, M., & Maes, P. (1994). Collaborative Interface Agents. Proceedings of the 12th National Conference on Artificial Intelligence, 444-449.
Infrastructure for Tracking Users in Open Collaborative.. - Gal Kaminka And (Correct)
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Y. Lashkari, M. Metral, and Patti Maes. Collaborative interface agents. In Proceedings of the Twelfth National Conference on Artificial Intelligence (AAAI-94), 1994.
Adaptive Filtering of Advertisements on Web Pages - Babak Esfandiari Dpt (2005) (Correct)
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Y. Lashkari, M. Metral, and P. Maes. Collaborative interface agents. In Proc. of AAAI-94, pages 444--449, 1994.
Web Mining - Fürnkranz (2004) (1 citation) (Correct)
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Y. Lashkari, M. Metral, and P. Maes. Collaborative interface agents. In Proceedings of the 12th National Conference on Artificial Intelligence (AAAI-94), pages 444-- 450, Seattle, WA, 1994. AAAI Press.
First Implementation of VHML on the Java Text-to-Speech Synthesiser - De Souza (Correct)
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Y. Lashkari, M. Metral, and P. Maes. Collaborative Interface Agents. In Proceedings of the Twelfth National Conference on Artificial Intelligence, volume 1, Seattle, WA, 1994. AAAI Press.
Augmenting the Knowledge Bandwidth and Connecting.. - Novak.. (2002) (1 citation) (Correct)
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Lashkari, Y. et al.; Collaborative Interface Agents, Proceedings of the Twelfth National Conference on Artificial Intelligence, Vol 1, AAAI PressSeattle, WA, 1994
Internet Agents for Effective Collaboration - Vidya Renganarayanan Amar (2001) (Correct)
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Lashkari, Y., Metral, M., and Maes, P., Collaborative Interface Agents, Huhns, Michael N., and Singh, Muninder P., editors, Readings in Agents. (1997).
Compact and Tractable Descriptors for Information Discovery - Wondergem (Correct)
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Y. Lashkari, M. Metral, and P. Maes. Collaborative Interface Agents. In Huhns M.N. and M. Singh, editors, Readings in Agents, pages 111-116, San Francisco, California, 1997. Morgan Kaufmann Publishers.
Automating Negotiation for M-Services - Paurobally, Turner, Jennings (2003) (1 citation) (Correct)
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Y. Lashkari, M. Metral, and P. Maes, "Collaborative interface agents," presented at the 12th Nat. Conf. Artificial Intelligence, Seattle, WA, 1994.
Adapting to Evolving Needs: Evaluating a Behaviour-Based.. - White, Jose, Ruthven (2003) (Correct)
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Lashkari, T., Metral, M. and Maes, P. (1994). Collaborative Interface Agents. Proc. of the American Association for Artificial Intelligence.
Instant Personalization via Clustering TV Viewing Patterns - Kaushal Kurapati Srinivas (2002) (Correct)
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Lashkari, Y., Metral, M., and Maes, P. Collaborative Interface Agents. In Proceedings of the National Conference on Artificial Intelligence. AAAI, Seattle, August 1994.
Searching Social Networks - Bin Yu And (2002) (11 citations) (Correct)
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Yezdi Lashkari, Max Metral, and Pattie Maes. Collaborative interface agents. In Proceedings of the Twelfth National Conference on Artificial Intelligence (AAAI), 1994.
Organizing R&D experiences using agents - TACLA, ENEMBRECK (Correct)
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Y. Lashkari et al., Collaborative Interface Agents, In Proceedings of the Twelfth National Conference on Artificial Intelligence, vol. 1, AAAI Press, Seattle, WA, 1994.
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