Treballs de recerca de màsterhttp://hdl.handle.net/10230/361012024-03-28T10:41:18Z2024-03-28T10:41:18ZEmpirical analysis of exploration strategies in QMIXColom, Arnauhttp://hdl.handle.net/10230/492612021-12-21T08:45:21Z2021-09-01T00:00:00ZEmpirical analysis of exploration strategies in QMIX
Colom, Arnau
In real world scenarios, to solve a gran majority of problems, there is the necessity for
different agents to cooperate under the condition of local observations. Fortunately,
in the recent years, significant advances in Multi-Agent Reinforcement Learning
have been done regarding this matter. To tackle this kind of problems, a lot of
approaches are based on the on Centralized Training with Decentralized Execution,
which allow the agents to be trained in a simulated environment where they can
have access to the global information to later solve the problem relying only on local
observations. Some popular methods are Value-Decomposition Networks (VDN) and
QMIX. They undertake the problem by computing the joint action-value function
Qtot as a combination of the individual action-value functions Qa, that only condition
on individual action-observation histories.
Specifically, this work focuses on QMIX, which has been gaining a lot of popularity
in the last year due to its capacity to compute a richer representation of the joint
action-value function than VDN by combining the individual Q-values in a non-linear
approach. However, despite the fact that there have been a lot of improvements on
QMIX, there have been small advances on how different exploration techniques could
boost the learning in this context. In this work, by performing an experimental
evaluation, its shown how some exploration methods outperform the greedy
approach used on the original implementation of QMIX on different cases.
Treball fi de màster de: Master in Intelligent Interactive Systems; Tutor: Vicenç Gómez
2021-09-01T00:00:00ZUnderstanding Alzheimer’s disease progression through phenotypes discovery using manifold learning techniquesPattarone, Natalia Karinahttp://hdl.handle.net/10230/492582021-12-21T08:46:55Z2021-07-01T00:00:00ZUnderstanding Alzheimer’s disease progression through phenotypes discovery using manifold learning techniques
Pattarone, Natalia Karina
Alzheimer’s disease (AD) is clinically highly heterogeneous, varying in terms of
rates of progression, test and cognitive symptoms among patients, as well as from
a neuroimaging perspective. In the datasets provided by The Alzheimer’s Disease
Neuroimaging Initiative (ADNI), researchers collect, validate and utilize data, including
MRI and PET images, genetics, cognitive tests, CSF and blood biomarkers
as predictors of the disease. Data coming from these datasets allow discovering
phenotypes that could help to better understand the disease and provide targeted
treatment.
The objective of this thesis is to identify data-driven phenotypes using manifold
learning and unsupervised clustering on multimodal longitudinal imaging and nonimaging
data. First, we apply a novel approach for dimensionality reduction called
PHATE that captures both local and global nonlinear structure using an informationgeometric
distance between datapoints that would facilitate the discovery of possible
AD phenotypes. Over PHATE output space, we performed a multiple-kernel unsupervised
clustering to obtain profiles and describe AD phenotypes where features are
weighted to construct kernels. Our results show that our approach can reveal AD
progression trajectories in a lower dimensionality space, improving the results of the
profiling where we obtained 4 possible profile subgroups using MRI cross-sectional
baseline data and 8 possible profile subgroups when using longitudinal data. Furthermore,
longitudinal data established clearer separation among profiles and higher
significance for cognitive tests and general volumetric cerebral values than baseline
data. Identifying these profiles could be useful for more personalized treatment of
such a heterogeneous disease as AD.
Treball fi de màster de: Master in Intelligent Interactive Systems; Tutor: Gemma Piella
2021-07-01T00:00:00ZExploring neural paraphrasing to improve fluency of rule-based generationDu, Shixiaohttp://hdl.handle.net/10230/492312021-12-22T08:00:04Z2021-09-01T00:00:00ZExploring neural paraphrasing to improve fluency of rule-based generation
Du, Shixiao
Data-to-text generation is a greatly significant task in the field of natural language
processing. FORGe, a typical rule-based generator, has excellent performance in
the task of mapping from RDF triples to text. As this generator is strongly reliant
on rules, despite the fact that the generated text is with highly semantic accuracy,
and faithful to the input RDF triples, the generated sentence could be somewhat
rigid in terms of fluency.
This thesis would like to explore a possible way to improve the fluency of output
text generated by FORGe. A neural paraphrase method is suggested to act as a
post-processing method to achieve our goal. This method can control the tradeoff
between two models, the fluency and semantic similarity model, and the lexical
and/or syntactic diversity model by setting a parameter . In this way, it can not
only make the output semantically consistent with the input, but also diversify the
lexical and syntactic items of the sentence. In order to verify our idea, we designed
and conducted related experiments.
Furthermore, the performance of deep-learning based generator OSU Neural NLG,
which also performs well in English D2T tasks, is considered as a baseline. Since
we need to evaluate all the generated text, an automatic evaluation method is used
in order to ensure an uniform evaluation criterion on these outputs in terms of
semantic accuracy. Based on the result of this automatic evaluation method, we
also conducted manual verification to make the evaluation result more reliable and
have reference value. Through our experimental results, we believe that applying
this neural paraphrase method as a post-processing stage is promising in improving
the fluency of the output text generated by FORGe.
Tutors: Leo Wanner i Simon Mille; Treball fi de màster de: Master in Intelligent Interactive Systems
2021-09-01T00:00:00ZChatbots as educational assistants: teaching about the digital footprintMancheno Gutiérrez, María Fernandahttp://hdl.handle.net/10230/492262021-12-16T02:32:04Z2021-07-01T00:00:00ZChatbots as educational assistants: teaching about the digital footprint
Mancheno Gutiérrez, María Fernanda
The digital footprint is a trace of everything a user does online. Everyone has one, but not everyone is aware of how to remain safe when being online. Every action a user takes when navigating through the Web or social media matters. Unlike other methods of communication, the Internet holds a record of each user’s activity, the key part being that this record will be held permanently. Therefore, each user should have the responsibility of making sure his/her digital footprint is protected.
As the world becomes more technologically advanced, so should teaching methods. Young adults are fast learners and sometimes learning techniques do not adequately fit in with the teaching methods still being used in classrooms. Students want immediate and concise help, which is not something they can always find in other humans. Several studies show how chatbots have proven to be efficient teaching methods as they not only consider the technological aspect, but also emotional aspects. They also have one major advantage over humans, chatbots have infinite patience and they do not ever get tired.
Therefore, this Master thesis answers the following question: to what extent can a chatbot prove to be efficient enough to teach young adults about their social media digital footprint? This Master thesis seeks, through the creation of an intelligent chatbot that takes in young adults’ responses, to provide knowledge about the digital footprint and the risks of being a social media user.
The process behind this work includes three main stages: developing the chatbot, testing the chatbot, and evaluating the chatbot. The chatbot uses NLP (Natural Language Processing) techniques and Chatterbot (Python package) to become intelligent enough to recognize users’ answers. Six Pompeu Fabra University bachelor students participate in this chatbot interaction to provide feedback and a proper evaluation about their experience and knowledge gained. Finally, based on this feedback, the Master thesis shows that this teaching method is successful in teaching young users about their digital footprint.
Tutors: Davinia Hernández-Leo i Emily Theophilou; Treball fi de màster de: Master in Intelligent Interactive Systems
2021-07-01T00:00:00Z