TY  - JOUR
A1  - Gautam, Khem Raj
A1  - Zhang, Guoqiang
A1  - Landwehr, Niels
A1  - Adolphs, Julian
T1  - Machine learning for improvement of thermal conditions inside a hybrid ventilated animal building
JF  - Computers and electronics in agriculture : COMPAG online ; an international journal
N2  - In buildings with hybrid ventilation, natural ventilation opening positions (windows), mechanical ventilation rates, heating, and cooling are manipulated to maintain desired thermal conditions. The indoor temperature is regulated solely by ventilation (natural and mechanical) when the external conditions are favorable to save external heating and cooling energy. The ventilation parameters are determined by a rule-based control scheme, which is not optimal. This study proposes a methodology to enable real-time optimum control of ventilation parameters. We developed offline prediction models to estimate future thermal conditions from the data collected from building in operation. The developed offline model is then used to find the optimal controllable ventilation parameters in real-time to minimize the setpoint deviation in the building. With the proposed methodology, the experimental building's setpoint deviation improved for 87% of time, on average, by 0.53 degrees C compared to the current deviations.
KW  - Animal building
KW  - Natural ventilation
KW  - Automatically controlled windows
KW  - Machine learning
KW  - Optimization
Y1  - 2021
U6  - https://doi.org/10.1016/j.compag.2021.106259
SN  - 0168-1699
SN  - 1872-7107
VL  - 187
PB  - Elsevier Science
CY  - Amsterdam [u.a.]
ER  - 
TY  - JOUR
A1  - Brede, Nuria
A1  - Botta, Nicola
T1  - On the correctness of monadic backward induction
JF  - Journal of functional programming
N2  - In control theory, to solve a finite-horizon sequential decision problem (SDP) commonly means to find a list of decision rules that result in an optimal expected total reward (or cost) when taking a given number of decision steps. SDPs are routinely solved using Bellman's backward induction. Textbook authors (e.g. Bertsekas or Puterman) typically give more or less formal proofs to show that the backward induction algorithm is correct as solution method for deterministic and stochastic SDPs. Botta, Jansson and Ionescu propose a generic framework for finite horizon, monadic SDPs together with a monadic version of backward induction for solving such SDPs. In monadic SDPs, the monad captures a generic notion of uncertainty, while a generic measure function aggregates rewards. In the present paper, we define a notion of correctness for monadic SDPs and identify three conditions that allow us to prove a correctness result for monadic backward induction that is comparable to textbook correctness proofs for ordinary backward induction. The conditions that we impose are fairly general and can be cast in category-theoretical terms using the notion of Eilenberg-Moore algebra. They hold in familiar settings like those of deterministic or stochastic SDPs, but we also give examples in which they fail. Our results show that backward induction can safely be employed for a broader class of SDPs than usually treated in textbooks. However, they also rule out certain instances that were considered admissible in the context of Botta et al. 's generic framework. Our development is formalised in Idris as an extension of the Botta et al. framework and the sources are available as supplementary material.
Y1  - 2021
U6  - https://doi.org/10.1017/S0956796821000228
SN  - 1469-7653
SN  - 0956-7968
VL  - 31
PB  - Cambridge University Press
CY  - Cambridge
ER  - 
TY  - JOUR
A1  - Chen, Junchao
A1  - Lange, Thomas
A1  - Andjelkovic, Marko
A1  - Simevski, Aleksandar
A1  - Lu, Li
A1  - Krstić, Miloš
T1  - Solar particle event and single event upset prediction from SRAM-based monitor and supervised machine learning
JF  - IEEE transactions on emerging topics in computing / IEEE Computer Society, Institute of Electrical and Electronics Engineers
N2  - The intensity of cosmic radiation may differ over five orders of magnitude within a few hours or days during the Solar Particle Events (SPEs), thus increasing for several orders of magnitude the probability of Single Event Upsets (SEUs) in space-borne electronic systems. Therefore, it is vital to enable the early detection of the SEU rate changes in order to ensure timely activation of dynamic radiation hardening measures. In this paper, an embedded approach for the prediction of SPEs and SRAM SEU rate is presented. The proposed solution combines the real-time SRAM-based SEU monitor, the offline-trained machine learning model and online learning algorithm for the prediction. With respect to the state-of-the-art, our solution brings the following benefits: (1) Use of existing on-chip data storage SRAM as a particle detector, thus minimizing the hardware and power overhead, (2) Prediction of SRAM SEU rate one hour in advance, with the fine-grained hourly tracking of SEU variations during SPEs as well as under normal conditions, (3) Online optimization of the prediction model for enhancing the prediction accuracy during run-time, (4) Negligible cost of hardware accelerator design for the implementation of selected machine learning model and online learning algorithm. The proposed design is intended for a highly dependable and self-adaptive multiprocessing system employed in space applications, allowing to trigger the radiation mitigation mechanisms before the onset of high radiation levels.
KW  - Machine learning
KW  - Single event upsets
KW  - Random access memory
KW  - monitoring
KW  - machine learning algorithms
KW  - predictive models
KW  - space missions
KW  - solar particle event
KW  - single event upset
KW  - machine learning
KW  - online learning
KW  - hardware accelerator
KW  - reliability
KW  - self-adaptive multiprocessing system
Y1  - 2022
U6  - https://doi.org/10.1109/TETC.2022.3147376
SN  - 2168-6750
VL  - 10
IS  - 2
SP  - 564
EP  - 580
PB  - Institute of Electrical and Electronics Engineers
CY  - [New York, NY]
ER  - 
TY  - JOUR
A1  - Brewka, Gerhard
A1  - Ellmauthaler, Stefan
A1  - Kern-Isberner, Gabriele
A1  - Obermeier, Philipp
A1  - Ostrowski, Max
A1  - Romero, Javier
A1  - Schaub, Torsten
A1  - Schieweck, Steffen
T1  - Advanced solving technology for dynamic and reactive applications
JF  - Künstliche Intelligenz
Y1  - 2018
U6  - https://doi.org/10.1007/s13218-018-0538-8
SN  - 0933-1875
SN  - 1610-1987
VL  - 32
IS  - 2-3
SP  - 199
EP  - 200
PB  - Springer
CY  - Heidelberg
ER  - 
TY  - JOUR
A1  - Schaub, Torsten
A1  - Woltran, Stefan
T1  - Answer set programming unleashed!
JF  - Künstliche Intelligenz
N2  - Answer Set Programming faces an increasing popularity for problem solving in various domains. While its modeling language allows us to express many complex problems in an easy way, its solving technology enables their effective resolution. In what follows, we detail some of the key factors of its success. Answer Set Programming [ASP; Brewka et al. Commun ACM 54(12):92–103, (2011)] is seeing a rapid proliferation in academia and industry due to its easy and flexible way to model and solve knowledge-intense combinatorial (optimization) problems. To this end, ASP offers a high-level modeling language paired with high-performance solving technology. As a result, ASP systems provide out-off-the-box, general-purpose search engines that allow for enumerating (optimal) solutions. They are represented as answer sets, each being a set of atoms representing a solution. The declarative approach of ASP allows a user to concentrate on a problem’s specification rather than the computational means to solve it. This makes ASP a prime candidate for rapid prototyping and an attractive tool for teaching key AI techniques since complex problems can be expressed in a succinct and elaboration tolerant way. This is eased by the tuning of ASP’s modeling language to knowledge representation and reasoning (KRR). The resulting impact is nicely reflected by a growing range of successful applications of ASP [Erdem et al. AI Mag 37(3):53–68, 2016; Falkner et al. Industrial applications of answer set programming. K++nstliche Intelligenz (2018)]
Y1  - 2018
U6  - https://doi.org/10.1007/s13218-018-0550-z
SN  - 0933-1875
SN  - 1610-1987
VL  - 32
IS  - 2-3
SP  - 105
EP  - 108
PB  - Springer
CY  - Heidelberg
ER  - 
TY  - JOUR
A1  - Tavakoli, Hamad
A1  - Alirezazadeh, Pendar
A1  - Hedayatipour, Ava
A1  - Nasib, A. H. Banijamali
A1  - Landwehr, Niels
T1  - Leaf image-based classification of some common bean cultivars using discriminative convolutional neural networks
JF  - Computers and electronics in agriculture : COMPAG online ; an international journal
N2  - In recent years, many efforts have been made to apply image processing techniques for plant leaf identification. However, categorizing leaf images at the cultivar/variety level, because of the very low inter-class variability, is still a challenging task. In this research, we propose an automatic discriminative method based on convolutional neural networks (CNNs) for classifying 12 different cultivars of common beans that belong to three various species. We show that employing advanced loss functions, such as Additive Angular Margin Loss and Large Margin Cosine Loss, instead of the standard softmax loss function for the classification can yield better discrimination between classes and thereby mitigate the problem of low inter-class variability. The method was evaluated by classifying species (level I), cultivars from the same species (level II), and cultivars from different species (level III), based on images from the leaf foreside and backside. The results indicate that the performance of the classification algorithm on the leaf backside image dataset is superior. The maximum mean classification accuracies of 95.86, 91.37 and 86.87% were obtained at the levels I, II and III, respectively. The proposed method outperforms the previous relevant works and provides a reliable approach for plant cultivars identification.
KW  - Bean
KW  - Plant identification
KW  - Digital image analysis
KW  - VGG16
KW  - Loss
KW  - functions
Y1  - 2021
U6  - https://doi.org/10.1016/j.compag.2020.105935
SN  - 0168-1699
SN  - 1872-7107
VL  - 181
PB  - Elsevier
CY  - Amsterdam [u.a.]
ER  - 
TY  - JOUR
A1  - Cabalar, Pedro
A1  - Fandiño, Jorge
A1  - Fariñas del Cerro, Luis
T1  - Splitting epistemic logic programs
JF  - Theory and practice of logic programming / publ. for the Association for Logic Programming
N2  - Epistemic logic programs constitute an extension of the stable model semantics to deal with new constructs called subjective literals. Informally speaking, a subjective literal allows checking whether some objective literal is true in all or some stable models. As it can be imagined, the associated semantics has proved to be non-trivial, since the truth of subjective literals may interfere with the set of stable models it is supposed to query. As a consequence, no clear agreement has been reached and different semantic proposals have been made in the literature. Unfortunately, comparison among these proposals has been limited to a study of their effect on individual examples, rather than identifying general properties to be checked. In this paper, we propose an extension of the well-known splitting property for logic programs to the epistemic case. We formally define when an arbitrary semantics satisfies the epistemic splitting property and examine some of the consequences that can be derived from that, including its relation to conformant planning and to epistemic constraints. Interestingly, we prove (through counterexamples) that most of the existing approaches fail to fulfill the epistemic splitting property, except the original semantics proposed by Gelfond 1991 and a recent proposal by the authors, called Founded Autoepistemic Equilibrium Logic.
KW  - knowledge representation and nonmonotonic reasoning
KW  - logic programming methodology and applications
KW  - theory
Y1  - 2021
U6  - https://doi.org/10.1017/S1471068420000058
SN  - 1471-0684
SN  - 1475-3081
VL  - 21
IS  - 3
SP  - 296
EP  - 316
PB  - Cambridge Univ. Press
CY  - Cambridge [u.a.]
ER  - 
TY  - JOUR
A1  - Dimopoulos, Yannis
A1  - Gebser, Martin
A1  - Lühne, Patrick
A1  - Romero Davila, Javier
A1  - Schaub, Torsten
T1  - plasp 3
BT  - Towards Effective ASP Planning
JF  - Theory and practice of logic programming
N2  - We describe the new version of the Planning Domain Definition Language (PDDL)-to-Answer Set Programming (ASP) translator plasp. First, it widens the range of accepted PDDL features. Second, it contains novel planning encodings, some inspired by Satisfiability Testing (SAT) planning and others exploiting ASP features such as well-foundedness. All of them are designed for handling multivalued fluents in order to capture both PDDL as well as SAS planning formats. Third, enabled by multishot ASP solving, it offers advanced planning algorithms also borrowed from SAT planning. As a result, plasp provides us with an ASP-based framework for studying a variety of planning techniques in a uniform setting. Finally, we demonstrate in an empirical analysis that these techniques have a significant impact on the performance of ASP planning.
KW  - knowledge representation and nonmonotonic reasoning
KW  - technical notes and rapid communications
KW  - answer set programming
KW  - automated planning
KW  - action and change
Y1  - 2019
U6  - https://doi.org/10.1017/S1471068418000583
SN  - 1471-0684
SN  - 1475-3081
VL  - 19
IS  - 3
SP  - 477
EP  - 504
PB  - Cambridge Univ. Press
CY  - New York
ER  - 
TY  - JOUR
A1  - Gebser, Martin
A1  - Kaminski, Roland
A1  - Kaufmann, Benjamin
A1  - Lühne, Patrick
A1  - Obermeier, Philipp
A1  - Ostrowski, Max
A1  - Romero Davila, Javier
A1  - Schaub, Torsten
A1  - Schellhorn, Sebastian
A1  - Wanko, Philipp
T1  - The Potsdam Answer Set Solving Collection 5.0
JF  - Künstliche Intelligenz
N2  - The Potsdam answer set solving collection, or Potassco for short, bundles various tools implementing and/or applying answer set programming. The article at hand succeeds an earlier description of the Potassco project published in Gebser et al. (AI Commun 24(2):107-124, 2011). Hence, we concentrate in what follows on the major features of the most recent, fifth generation of the ASP system clingo and highlight some recent resulting application systems.
Y1  - 2018
U6  - https://doi.org/10.1007/s13218-018-0528-x
SN  - 0933-1875
SN  - 1610-1987
VL  - 32
IS  - 2-3
SP  - 181
EP  - 182
PB  - Springer
CY  - Heidelberg
ER  - 
TY  - JOUR
A1  - Haubelt, Christian
A1  - Neubauer, Kai
A1  - Schaub, Torsten
A1  - Wanko, Philipp
T1  - Design space exploration with answer set programming
JF  - Künstliche Intelligenz
N2  - The aim of our project design space exploration with answer set programming is to develop a general framework based on Answer Set Programming (ASP) that finds valid solutions to the system design problem and simultaneously performs Design Space Exploration (DSE) to find the most favorable alternatives. We leverage recent developments in ASP solving that allow for tight integration of background theories to create a holistic framework for effective DSE.
Y1  - 2018
U6  - https://doi.org/10.1007/s13218-018-0530-3
SN  - 0933-1875
SN  - 1610-1987
VL  - 32
IS  - 2-3
SP  - 205
EP  - 206
PB  - Springer
CY  - Heidelberg
ER  -