DCAART 2017 Abstracts


Short Papers
Paper Nr: 3
Title:

Agent-Assisted Collaborative Learning - Using Agent Teamwork as a Collaborative Method to Facilitate e-Learning

Authors:

Mario Mallia-Milanes and Matthew Montebello

Abstract: e-Learning was a major shift in the learning medium to reach out to vast amounts of people and enable the possibility for them to catch up on lost time or acquire new skills from the comfort of their home and at the time most suitable to them. However numerous issues have been attributed to e-learning over the years amongst which is the low retention rate that sheds a shadow on its validity and effectiveness. In this paper we discuss how we propose to employ artificially intelligent agents that collaborate together and with human counterparts to optimise the medium and extract academic benefits.

Paper Nr: 4
Title:

Assured Reinforcement Learning for Safety-critical Applications

Authors:

George Mason, Radu Calinescu, Daniel Kudenko and Alec Banks

Abstract: Reinforcement learning (RL) is a machine learning technique where an autonomous agent uses the rewards received from its interactions with an initially unknown Markov decision process (MDP) to converge to an optimal policy, i.e., the actions to take in the MDP states in order to maximise the obtained rewards. Although successfully used in applications ranging from gaming to robotics, standard RL is not applicable to problems where the policies learned by the agent must satisfy strict constraints associated with the safety, reliability, performance and other critical aspects of the problem. Our project addresses this significant limitation of standard RL by integrating it with probabilistic model checking, and thus extending the applicability of the technique to mission-critical and safety-critical systems.

Paper Nr: 5
Title:

Extracting Implicit Aspects based on Latent Dirichlet Allocation

Authors:

Ekin Ekinci and Sevinç ─░lhan Omurca

Abstract: Sentiment analysis arises as one of the important research field. With the growing popularity of opinion-rich resources such as ecommerce and social media websites, blogs, dictionaries and news portals people and companies start to understand the opinions of others by using this mediums. The majority of the studies on sentiment analysis focus on whether or not the meaning of the text is positive or negative. Nowadays, aspect based sentiment analysis has become a prominent field of study for in-depth analysis of customer reviews. In sentiment analysis aspects are categorized as two types: explicit aspects and implicit aspects. In this study we aim to extract implicit aspects with topic models.