In mixtures-of-experts (ME) model, where a number of submodels (experts) are combined, there have been two longstanding problems: (i) how many experts should be chosen, given the size of the training data? (ii) given the total number of parameters, is it better to use a few very complex experts, or is it better to combine many simple experts? In this paper, we try to provide some insights to these problems through a theoretic study on a ME structure where $m$ experts are mixed, with each expert being related to a polynomial regression model of order $k$. We study the convergence rate of the maximum likelihood estimator (MLE), in terms of how fast the Kullback-Leibler divergence of the estimated density converges to the true density, when the sample size $n$ increases. The convergence rate is found to be dependent on both $m$ and $k$, and certain choices of $m$ and $k$ are found to produce optimal convergence rates. Therefore, these results shed light on the two aforementioned important problems: on how to choose $m$, and on how $m$ and $k$ should be compromised, for achieving good convergence rates.

Web Effort Estimation is a process of predicting the efforts and cost in terms of money, schedule and staff for any software project system. Many estimation models have been proposed over the last three decades and it is believed that it is a must for the purpose of: Budgeting, risk analysis, project planning and control, and project improvement investment analysis. In this paper, we investigate the use of Fuzzy ID3 decision tree for software cost estimation; it is designed by integrating the principles of ID3 decision tree and the fuzzy set-theoretic concepts, enabling the model to handle uncertain and imprecise data when describing the software projects, which can improve greatly the accuracy of obtained estimates. MMRE and Pred are used as measures of prediction accuracy for this study. A series of experiments is reported using two different software projects datasets namely, Tukutuku and COCOMO'81 datasets. The results are compared with those produced by the crisp version of the ID3 decision tree.

Recommender systems now play an important role in online information discovery, complementing traditional approaches such as search and navigation, with a more proactive approach to discovery that is informed by the users interests and preferences. To date recommender systems have been deployed within a variety of e-commerce domains, covering a range of products such as books, music, movies, and have proven to be a successful way to convert browsers into buyers. Recommendation technologies have a potentially much greater role to play in information discovery however and in this article we consider recent research that takes a fresh look at web search as a fertile platform for recommender systems research as users demand a new generation of search engines that are less susceptible to manipulation and more responsive to searcher needs and preferences.

Context-aware recommender systems (CARS) generate more relevant recommendations by adapting them to the specific contextual situation of the user. This article explores how contextual information can be used to create more intelligent and useful recommender systems. It provides an overview of the multifaceted notion of context, discusses several approaches for incorporating contextual information in recommendation process, and illustrates the usage of such approaches in several application areas where different types of contexts are exploited. The article concludes by discussing the challenges and future research directions for context-aware recommender systems.

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, presented the 2011 Spring Symposium Series Monday through Wednesday, March 21–23, 2011 at Stanford University. The titles of the eight symposia were AI and Health Communication, Artificial Intelligence and Sustainable Design, AI for Business Agility, Computational Physiology, Help Me Help You: Bridging the Gaps in Human-Agent Collaboration, Logical Formalizations of Commonsense Reasoning, Multirobot Systems and Physical Data Structures, and Modeling Complex Adaptive Systems As If They Were Voting Processes.

The 19th robotics program at the annual AAAI conference was held in Atlanta, Georgia in July 2010. In this article we give a summary of three components of the exhibition: small scale manipulation challenge: robotic chess; the learning by demonstration challenge, and the education track. We also describe the participating teams, highlight the research questions they tackled and briefly describe the systems they demonstrated.

In recommender systems, explanations serve as an additional type of information that can help users to better understand the system's output and promote objectives such as trust, confidence in decision making or utility. This article proposes a taxonomy to categorize and review the research in the area of explanations. It provides a unified view on the different recommendation paradigms, allowing similarities and differences to be clearly identified.

Requirements engineering in large-scaled industrial, government, and international projects can be a highly complex process involving thousands, or even hundreds of thousands of potentially distributed stakeholders. As a result, many human intensive tasks in requirements elicitation, analysis, and management processes can be augmented and supported through the use of recommender system and machine learning techniques. In this article we describe several areas in which recommendation technologies have been applied to the requirements engineering domain, namely stakeholder identification, domain analysis, requirements elicitation, and decision support across several requirements analysis and prioritization tasks. We also highlight ongoing challenges and opportunities for applying recommender systems in the requirements engineering domain.

State of the art recommender systems support users in the selection of items from a predefined assortment (for example, movies, books, and songs). In contrast to an explicit definition of each individual item, configurable products such as computers, financial service portfolios, and cars are repre sented in the form of a configuration knowledge base that describes the properties of allowed instances. Although the knowledge representation used is different compared to non-confi gurable products, the decision support requirements remain the same: users have to be supported in finding a solution that fits their wishes and needs. In this article we show how recommendation technologies can be applied for supporting the configuration of products.

Recommender systems have been part of the Internet for almost two decades. Today recommender systems are found in a multitude of online services. However, when we evaluate the current generation of recommender systems from the point of view of the "recommendee," we find that most recommender systems serve the goals of the business instead of their users' interests. We foresee a third wave of recommender systems that act directly on behalf of their users across a range of domains instead of acting as a sales assistant.