13,811 research outputs found
Using Monte Carlo Search With Data Aggregation to Improve Robot Soccer Policies
RoboCup soccer competitions are considered among the most challenging
multi-robot adversarial environments, due to their high dynamism and the
partial observability of the environment. In this paper we introduce a method
based on a combination of Monte Carlo search and data aggregation (MCSDA) to
adapt discrete-action soccer policies for a defender robot to the strategy of
the opponent team. By exploiting a simple representation of the domain, a
supervised learning algorithm is trained over an initial collection of data
consisting of several simulations of human expert policies. Monte Carlo policy
rollouts are then generated and aggregated to previous data to improve the
learned policy over multiple epochs and games. The proposed approach has been
extensively tested both on a soccer-dedicated simulator and on real robots.
Using this method, our learning robot soccer team achieves an improvement in
ball interceptions, as well as a reduction in the number of opponents' goals.
Together with a better performance, an overall more efficient positioning of
the whole team within the field is achieved
Machine Learning Approach for an Advanced Agent-based Intelligent Tutoring System
Machine Learning Approach for an Advanced Agent-based Intelligent Tutoring System
Roya Aminikia
Learning Management Systems (LMSs) are digital frameworks that provide curriculum, training
materials, and corresponding assessments to guarantee an effective learning process. Although
these systems are capable of distributing the learning content, they do not support dynamic learning
processes and do not have the capability to communicate with human learners who are required to
interact in a dynamic environment during the learning process. To create this process and support
the interaction feature, LMSs are equipped with Intelligent Tutoring Systems (ITSs). The main
objective of an ITS is to facilitate students’ movement towards their learning goals through virtual
tutoring. When equipped with ITSs, LMSs operate as dynamic systems to provide students with
access to a tutor who is available anytime during the learning session. The crucial issues we address
in this thesis are how to set up a dynamic LMS, and how to design the logical structure behind an
ITS. Artificial intelligence, multi-agent technology and machine learning provide powerful theories
and foundations that we leverage to tackle these issues.
We designed and implemented the new concept of Pedagogical Agent (PA) as the main part of
our ITS. This agent uses an evaluation procedure to compare each particular student, in terms of
performance, with their peers to develop a worthwhile guidance. The agent captures global knowledge
of students’ feature measurements during students’ guiding process. Therefore, the PA retains
an updated status, called image, of each specific student at any moment. The agent uses this image
for the purpose of diagnosing students’ skills to implement a specific correct instruction. To develop
the infrastructure of the agent decision making algorithm, we laid out a protocol (decision tree) to
select the best individual direction. The significant capability of the agent is the ability to update
its functionality by looking at a student’s image at run time. We also applied two supervised machine
learning methods to improve the decision making protocol performance in order to maximize
the effect of the collaborating mechanism between students and the ITS. Through these methods,
we made the necessary modifications to the decision making structure to promote students’ performance
by offering prompts during the learning sessions. The conducted experiments showed that
the proposed system is able to efficiently classify students into learners with high versus low performance.
Deployment of such a model enabled the PA to use different decision trees while interacting
with students of different learning skills. The performance of the system has been shown by ROC
curves and details regarding combination of different attributes used in the two machine learning
algorithms are discussed, along with the correlation of key attributes that contribute to the accuracy
and performance of the decision maker components
Intelligent agent supported personalization for virtual learning environments
Virtual learning environments (VLEs) are computer-based online learning environments, which provide opportunities for online learners to learn at the time and location of their choosing, whilst allowing interactions and encounters with other online learners, as well as affording access to a wide range of resources. They have the capability of reaching learners in remote areas around the country or across country boundaries at very low cost. Personalized VLEs are those VLEs that provide a set of personalization functionalities, such as personalizing learning plans, learning materials, tests, and are capable of initializing the interaction with learners by providing advice, necessary instant messages, etc., to online learners. One of the major challenges involved in developing personalized VLEs is to achieve effective personalization functionalities, such as personalized content management, learner model, learner plan and adaptive instant interaction. Autonomous intelligent agents provide an important technology for accomplishing personalization in VLEs. A number of agents work collaboratively to enable personalization by recognizing an individual's eLeaming pace and reacting correspondingly. In this research, a personalization model has been developed that demonstrates dynamic eLearning processes; secondly, this study proposes an architecture for PVLE by using intelligent decision-making agents' autonomous, pre-active and proactive behaviors. A prototype system has been developed to demonstrate the implementation of this architecture. Furthemore, a field experiment has been conducted to investigate the performance of the prototype by comparing PVLE eLearning effectiveness with a non-personalized VLE. Data regarding participants' final exam scores were collected and analyzed. The results indicate that intelligent agent technology can be employed to achieve personalization in VLEs, and as a consequence to improve eLeaming effectiveness dramatically
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