Applied Artificial Intelligence 

About: Applied Artificial Intelligence is an academic journal published by Taylor & Francis. The journal publishes majorly in the area(s): Computer science & Artificial neural network. It has an ISSN identifier of 0883-9514. Over the lifetime, 1588 publications have been published receiving 33401 citations. The journal is also known as: AAI.

An analysis of four missing data treatment methods for supervised learning

Abstract: One relevant problem in data quality is missing data. Despite the frequent occurrence and the relevance of the missing data problem, many machine learning algorithms handle missing data in a rather naive way. However, missing data treatment should be carefully treated, otherwise bias might be introduced into the knowledge induced. In this work, we analyze the use of the k-nearest neighbor as an imputation method. Imputation is a term that denotes a procedure that replaces the missing values in a data set with some plausible values. One advantage of this approach is that the missing data treatment is independent of the learning algorithm used. This allows the user to select the most suitable imputation method for each situation. Our analysis indicates that missing data imputation based on the k-nearest neighbor algorithm can outperform the internal methods used by C4.5 and CN2 to treat missing data, and can also outperform the mean or mode imputation method, which is a method broadly used to treat missing ...

The open agent architecture: A framework for building distributed software systems

Abstract: The Open Agent Architecture (OAA), developed and used for several years at SRI International, makes it possible for software services to be provided through the cooperative efforts of distributed collections of autonomous agents. Communication and cooperation between agents are brokered by one or more facilitators, which are responsible for matching requests, from users and agents, with descriptions of the capabilities of other agents. Thus it is not generally required that a user or agent know the identities, locations, or number of other agents involved in satisfying a request. OAA is structured so as to minimize the effort involved in creating new agents and "wrapping" legacy applications, written in various languages and operating on various platforms; to encourage the reuse of existing agents; and to allow for dynamism and flexibility in the makeup ofagent communities. Distinguishing features of OAA as compared with related work include extreme flexibility in using facilitator-based delegation of com...

Trust management through reputation mechanisms

Abstract: The members of electronic communities are often unrelated to each other; they may have never met and have no information on each other's reputation This kind of information is vital in electronic commerce interactions, where the potential counterpart's reputation can be a significant factor in the negotiation strategy Two complementary reputation mechanisms are investigated which rely on collaborative rating and personalized evaluation of the various ratings assigned to each user While these reputation mechanisms are developed in the context of electronic commerce, it is believed that they may have applicability in other types of electronic communities such as chatrooms, newsgroups, mailing lists, etc

Probabilistic assessment of user's emotions in educational games

Abstract: We present a probabilistic model to monitor a user's emotions and engagement during the interaction with educational games. We illustrate how our probabilistic model assesses affect by integrating evidence on both possible causes of the user's emotional arousal (i.e., the state of the interaction) and its effects (i.e., bodily expressions that are known to be influenced by emotional reactions). The probabilistic model relies on a Dynamic Decision Network to leverage any indirect evidence on the user's emotional state, in order to estimate this state and any other related variable in the model. This is crucial in a modeling task in which the available evidence usually varies with the user and with each particular interaction. The probabilistic model we present is to be used by decision theoretic pedagogical agents to generate interventions aimed at achieving the best tradeoff between a user's learning and engagement during the interaction with educational games.

Animated agents for procedural training in virtual reality: Perception, cognition, and motor control

Abstract: This paper describes Steve , an animated agent that helps students learn to perform physical , procedural tasks . The student and Steve cohabit a three - dimensional , simulated mock - up of the student's work environment . Steve can demonstrate how to perform tasks and can also monitor students while they practice tasks , providing assistance when needed . This paper describes Steve's architecture in detail , including perception , cognition , and motor control . The perception module monitors the state of the virtual world , maintains a coherent representation of it , and provides this information to the cognition and motor control modules . The cognition module interprets its perceptual input , chooses appropriate goals , constructs and executes plans to achieve those goals , and sends out motor commands . The motor control module implements these motor commands , controlling Steve's voice , locomotion , gaze , and gestures , allowing Steve to manipulate objects in the virtual world .