7,329 research outputs found
UMSL Bulletin 2023-2024
The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp
Affinity-Based Reinforcement Learning : A New Paradigm for Agent Interpretability
The steady increase in complexity of reinforcement learning (RL) algorithms is accompanied by a corresponding increase in opacity that obfuscates insights into their devised strategies. Methods in explainable artificial intelligence seek to mitigate this opacity by either creating transparent algorithms or extracting explanations post hoc. A third category exists that allows the developer to affect what agents learn: constrained RL has been used in safety-critical applications and prohibits agents from visiting certain states; preference-based RL agents have been used in robotics applications and learn state-action preferences instead of traditional reward functions. We propose a new affinity-based RL paradigm in which agents learn strategies that are partially decoupled from reward functions. Unlike entropy regularisation, we regularise the objective function with a distinct action distribution that represents a desired behaviour; we encourage the agent to act according to a prior while learning to maximise rewards. The result is an inherently interpretable agent that solves problems with an intrinsic affinity for certain actions. We demonstrate the utility of our method in a financial application: we learn continuous time-variant compositions of prototypical policies, each interpretable by its action affinities, that are globally interpretable according to customers’ financial personalities.
Our method combines advantages from both constrained RL and preferencebased RL: it retains the reward function but generalises the policy to match a defined behaviour, thus avoiding problems such as reward shaping and hacking. Unlike Boolean task composition, our method is a fuzzy superposition of different prototypical strategies to arrive at a more complex, yet interpretable, strategy.publishedVersio
DCNFIS: Deep Convolutional Neuro-Fuzzy Inference System
A key challenge in eXplainable Artificial Intelligence is the well-known
tradeoff between the transparency of an algorithm (i.e., how easily a human can
directly understand the algorithm, as opposed to receiving a post-hoc
explanation), and its accuracy. We report on the design of a new deep network
that achieves improved transparency without sacrificing accuracy. We design a
deep convolutional neuro-fuzzy inference system (DCNFIS) by hybridizing fuzzy
logic and deep learning models and show that DCNFIS performs as accurately as
three existing convolutional neural networks on four well-known datasets. We
furthermore that DCNFIS outperforms state-of-the-art deep fuzzy systems. We
then exploit the transparency of fuzzy logic by deriving explanations, in the
form of saliency maps, from the fuzzy rules encoded in DCNFIS. We investigate
the properties of these explanations in greater depth using the Fashion-MNIST
dataset
China’s Augmented National Innovation System (ANIS) and the Future: A Nonlinear Complex Systems Model with Application to Semiconductors and AI
I present a nonlinear complex dynamic systems model of innovation for China within which both efficiency and equity can be addressed. For the fourth industrial revolution(IR4), digital technologies based on semiconductor material foundation and AI are analyzed for China within such a system which can be called an Augmented National innovation system or ANIS. There are at least two dimensions along which China’s NIS can be augmented. One is to include the AI and semiconductor base for high technology for IR4, and the other is to move towards a more egalitarian innovation system in accordance with the goal of creating a harmonious moderately prosperous economy and society. The Chinese ANIS that is being built for the 21st century has important regional and geoeconomic implications for the future
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Beyond Worst-Case Analysis for Sequential Decision Making
Traditionally, algorithms have been evaluated through worst-case analysis, where the input is presumed to take its worst possible configuration. However, in many real-world settings, the data is not adversarially constructed and, on the contrary, exhibits some recognizable patterns. This often leads worst-case guarantees to be poor indicators of algorithms' performance. To overcome this limitation, a growing body of work on Beyond Worst-Case analysis has recently emerged.
In this thesis, we are concerned with sequential decision-making problems, where an agent must take successive decisions over multiple time steps without knowing in advance the forthcoming input. Examples of such settings include ride-sharing, online retail or job scheduling. Motivated by the unprecedented surge of data in these domains, which may help to overcome worst-case barriers by allowing to predict at least partially the future, we explore three distinct frameworks for Beyond Worst-Case analysis of sequential decision-making: (i) semi-random models, (ii) parametric models, and (iii) algorithms with predictions. While they all pursue the same objective — using previously collected data to provide stronger theoretical guarantees —, these frameworks mainly differ in the way the data is utilized. We examine each of them separately and present novel results for five different online optimization problems: minimum cost matching, assortment optimization (with and without inventory constraints), pricing and scheduling
LIPIcs, Volume 274, ESA 2023, Complete Volume
LIPIcs, Volume 274, ESA 2023, Complete Volum
A geo-informatics approach to sustainability assessments of floatovoltaic technology in South African agricultural applications
South African project engineers recently pioneered the first agricultural floating solar photovoltaic tech nology systems in the Western Cape wine region. This effort prepared our country for an imminent large scale diffusion of this exciting new climate solver technology. However, hydro-embedded photovoltaic sys tems interact with environmentally sensitive underlying aquatic ecosystems, causing multiple project as sessment uncertainties (energy, land, air, water) compared to ground-mounted photovoltaics. The dissimi lar behaviour of floatovoltaic technologies delivers a broader and more diversified range of technical advan tages, environmental offset benefits, and economic co-benefits, causing analytical modelling imperfections
and tooling mismatches in conventional analytical project assessment techniques. As a universal interna tional real-world problem of significance, the literature review identified critical knowledge and methodology
gaps as the primary causes of modelling deficiencies and assessment uncertainties. By following a design thinking methodology, the thesis views the sustainability assessment and modelling problem through a geo graphical information systems lens, thus seeing an academic research opportunity to fill critical knowledge
gaps through new theory formulation and geographical knowledge creation. To this end, this philosophi cal investigation proposes a novel object-oriented systems-thinking and climate modelling methodology to
study the real-world geospatial behaviour of functioning floatovoltaic systems from a dynamical system thinking perspective. As an empirical feedback-driven object-process methodology, it inspired the thesis to
create new knowledge by postulating a new multi-disciplinary sustainability theory to holistically characterise
agricultural floatovoltaic projects through ecosystems-based quantitative sustainability profiling criteria. The
study breaks new ground at the frontiers of energy geo-informatics by conceptualising a holistic theoretical
framework designed for the theoretical characterisation of floatovoltaic technology ecosystem operations
in terms of the technical energy, environmental and economic (3E) domain responses. It campaigns for a
fully coupled model in ensemble analysis that advances the state-of-the-art by appropriating the 3E theo retical framework as underpinning computer program logic blueprint to synthesise the posited theory in a
digital twin simulation. Driven by real-world geo-sensor data, this geospatial digital twin can mimic the geo dynamical behaviour of floatovoltaics through discrete-time computer simulations in real-time and lifetime
digital project enactment exercises. The results show that the theoretical 3E framing enables project due
diligence and environmental impact assessment reporting as it uniquely incorporates balanced scorecard
performance metrics, such as the water-energy-land-food resource impacts, environmental offset benefits
and financial feasibility of floatovoltaics. Embedded in a geoinformatics decision-support platform, the 3E
theory, framework and model enable numerical project decision-supporting through an analytical hierarchy
process. The experimental results obtained with the digital twin model and decision support system show
that the desktop-based parametric floatovoltaic synthesis toolset can uniquely characterise the broad and
diverse spectrum of performance benefits of floatovoltaics in a 3E sustainability profile. The model uniquely
predicts important impact aspects of the technology’s land, air and water preservation qualities, quantifying
these impacts in terms of the water, energy, land and food nexus parameters. The proposed GIS model
can quantitatively predict most FPV technology unknowns, thus solving a contemporary real-world prob lem that currently jeopardises floating PV project licensing and approvals. Overall, the posited theoretical
framework, methodology model, and reported results provide an improved understanding of floating PV renewable energy systems and their real-world behaviour. Amidst a rapidly growing international interest in
floatovoltaic solutions, the research advances fresh philosophical ideas with novel theoretical principles that
may have far-reaching implications for developing electronic, photovoltaic performance models worldwide.GeographyPh. D. (Geography
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