به نام خدا
Title: Data mining agent conversations: A qualitative approach to multi-agent systems analysis
Authors: Emilio Serrano, Michael Rovatsos, Juan A Botia
Abstract: This paper presents a novel method for analyzing the behavior of multivalent systems on the basis of the semantically rich information provided by agent communication languages and interaction protocols specified at the knowledge level. More low-level communication mechanisms only allow for a quantitative analysis of the occurrence of message types, the frequency of message sequences, and the empirical distributions of parameter values. Quite differently, the semantics of languages and protocols in multi-agent systems can help to extract qualitative properties of observed conversations among agents. This can be achieved by interpreting the logical constraints associated with protocol execution paths or individual messages as the context of an observed interaction, and using them as features of learning samples. The contexts mined from such analyses, or context models, can then be used for various tasks, e.g. for predicting others future responses (useful when trying to make strategic communication decisions to achieve a particular outcome), to support ontological alignment (by comparing the properties of logical constraints attached to messages across participating agents), or to assess the trustworthiness of agents (by verifying the logical coherence of their behavior). This paper details a formal approach that describes our notion of context models in multi-agent conversations, an implementation of this approach in a practical tool for mining qualitative context models, and experimental results to illustrate its use and utility.
Publish Year: 2013
Published in: Information Sciences - Science Direct
موضوع: داده کاوی (Data Mining) – عاملهای هوشمند (Intelligent Agents)
ایران سای – مرجع مقالات علمی فنی مهندسی
حامی دانش بومی ایرانیان
به نام خدا
Title: Handwritten Text Segmentation using Average Longest Path Algorithm
Authors: Dhaval Salvi, Jun Zhou, Jarrell Waggoner, and Song Wang
Abstract: Offline handwritten text recognition is a very challenging problem. Aside from the large variation of different hand-writing styles, neighboring characters within a word are usually connected, and we may need to segment a word into individual characters for accurate character recognition. Many existing methods achieve text segmentation by evaluating the local stroke geometry and imposing constraint on the size of each resulting character, such as the character width, height and aspect ratio. These constraints are well suited for printed texts, but may not hold for handwritten texts. Other methods apply holistic approach by using setof lexicons to guide and correct the segmentation and recognition. This approach may fail when the lexicon domain is insufficient. In this paper, we present a new global non-holistic method for handwritten text segmentation, which does not make any limiting assumptions on the characterize and the number of characters in a word. Specifically, the proposed method finds the text segmentation with the maximum average likeliness for the resulting characters. For this purpose, we use a graph model that describes the possible locations for segmenting neighboring characters, and we then develop an average longest path algorithm to identify the globally optimal segmentation. We conduct experiments on real images of handwritten texts taken from the IAM handwriting database and compare the performance of the proposed method against an existing text segmentation algorithm that uses dynamic programming.
Publish Year: 2013
Published in: WACV – IEEE
موضوع: تشخصی دست خط (Handwritten Text Recognition)
ایران سای – مرحع علمی فنی مهندسی
حامی دانش بومی ایرانیان
به نام خدا
Title: Influence of Sci-Fi Films on Artificial Intelligence and Vice-Versa
Authors: D Lorenk, M Tarhaniov and P Sincak
Abstract: Sci-fi technological movie domain is an important part of human culture. The paper focus on comparison study of selected robotics sci-fi movie domain from a technological point of view. It is necessary to accomplish technological analysis of studied sci-fi movies and able to distinguish about possible current technology and future direction of the artificial intelligence in the domain of Robot intelligence. The review of existing movies which are in fact influencing thinking of humans is essential since it can influence a future research direction in AI. This information is interesting for inspiration of students and research associates in theory and applications. In conclusion, we envision potential problems of social networks and impact of Internet of things facts which is becoming a reality with IPv6 protocol. The goal of the paper is also to underline the importance of such cultural phenomena as sci-fi movies for the future of humanity.
Publish Year: 2013
Published in: SAMI – IEEE
موضوع: هوش مصنوعی (Artificial Intelligence)
ایران سای – مرجع علمی فنی مهندسی
حامی دانش بومی ایرانیان
به نام خدا
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ایران سای – مرجع علمی فنی مهندسی
حامی دانش بومی ایرانیان
به نام خدا
Title: Tell Me More? The Effects of Mental Model Soundness on Personalizing an Intelligent Agent
Authors: Todd Kulesza, Simone Stumpf, Margaret Burnett, Irwin Kwan
Abstract: What does a user need to know to productively work with an intelligent agent? Intelligent agents and recommender systems are gaining widespread use, potentially creating a need for end users to understand how these systems operate in order to fix their agents personalized behavior. This paper explores the effects of mental model soundness on such personalization by providing structural knowledge of a music recommender system in an empirical study. Our findings show that participants were able to quickly build sound mental models of the recommender system’s reasoning, and that participants who most improved their mental models during the study were significantly more likely to make the recommender operate to their satisfaction. These results suggest that by helping end users understand a system’s reasoning, intelligent agents may elicit more and better feedback, thus more closely aligning their output with each user’s intentions.
Publish Year: 2012
Published in: CHI – ACM
موضوع: عاملهای هوشمند (Intelligent Agents) ، هوش مصنوعی (Artificial Intelligence)
لینک مشاهده مقاله در سایت ناشر
ایران سای – مرجع علمی فنی مهندسی
حامی دانش بومی ایرانیان
