Reviewerly/exHarmony huggingface dataset:
Can They Come Together? A Computational Complexity Analysis of Conjunctive Possible Effects of Compound HTN Planning Tasks

Recently, inferred effects of compound (totally ordered) HTN planning tasks were introduced. Guaranteed effects are those which hold true after all executable refinements of such a task, whereas possible effects are only required to hold after some of them. It is known that we can decide in P whether a single fact is a precondition-relaxed possible effect. For this relaxation, it was not clear whether groups of effects could be determined in P as well. We show that the problem turns NP-complete for conjunctive possible effects of arbitrary size. A more positive result is that this problem is fixed-parameter tractable, i.e., for any fixed number of possible effects, we can verify (and compute) them in P. As a side product of our investigations, we obtain novel results for total-order HTN planning problems with goal description: When ignoring action preconditions, plan existence is NP-complete and remains NP-hard even when the problem is additionally acyclic, regular, and delete-relaxed.
A Planning Approach to Repair Domains with Incomplete Action Effects

Automated planning is a problem solving technique for a wide range of scenarios and goals, which typically involves the creation of domain and problem files in formal languages. However, producing complete model descriptions can be challenging and time-consuming, especially for non-experts. Although many tools have been developed to support file editing, mistakes can still be made, such as incomplete or improper specification of the initial state or the set of actions. These errors often result in unsolvable tasks for planners, making it impossible to generate a plan. Explaining the absence of a solution in such cases is essential to support humans in the development of automated planning tasks. In this paper, we introduce a novel approach to repair planning models where the effects of some actions are incomplete, without further information from the user side. We propose a compilation of the unsolvable task to a new extended planning task, in which new actions are permitted to insert possible missing effects. The solution is a plan that achieves the goals of the original problem while also alerting users of the modifications made to do so. Experimental results demonstrate that this approach can effectively repair incomplete planning domains.
Explainable Goal Recognition: A Framework Based on Weight of Evidence

We introduce and evaluate an eXplainable goal recognition (XGR) model that uses the Weight of Evidence (WoE) framework to explain goal recognition problems. Our model provides human-centered explanations that answer `why?' and `why not?' questions. We computationally evaluate the performance of our system over eight different goal recognition domains showing it does not significantly increase the underlying recognition run time. Using a human behavioral study to obtain the ground truth from human annotators, we further show that the XGR model can successfully generate human-like explanations. We then report on a study with 40 participants who observe agents playing a Sokoban game and then receive explanations of the goal recognition output. We investigated participants’ understanding obtained by explanations through task prediction, explanation satisfaction, and trust.
Deceptive Reinforcement Learning in Model-Free Domains

This paper investigates deceptive reinforcement learning for privacy preservation in model-free and continuous action space domains. In reinforcement learning, the reward function defines the agent's objective. In adversarial scenarios, an agent may need to both maximise rewards and keep its reward function private from observers. Recent research presented the ambiguity model (AM), which selects actions that are ambiguous over a set of possible reward functions, via pre-trained Q-functions. Despite promising results in model-based domains, our investigation shows that AM is ineffective in model-free domains due to misdirected state space exploration. It is also inefficient to train and inapplicable in continuous action spaces. We propose the deceptive exploration ambiguity model (DEAM), which learns using the deceptive policy during training, leading to targeted exploration of the state space. DEAM is also applicable in continuous action spaces. We evaluate DEAM in discrete and continuous action space path planning environments. DEAM achieves similar performance to an optimal model-based version of AM and outperforms a model-free version of AM in terms of path cost, deceptiveness and training efficiency. These results extend to the continuous domain.
Heuristic Search for Physics-Based Problems: Angry Birds in PDDL+ [Extended Abstract]

Angry Birds is a very popular game that requires reasoning about sequential actions in a continuous world with discrete exogenous events. Different versions of the game are hard computationally, and the reigning world champion is still a human despite a long-running yearly competition in IJCAI conferences. In this work, we present the Hydra, the first successful game-playing agent for Angry Birds that uses a domain-independent planner and combinatorial search techniques. Hydra models the game using PDDL+, a rich planning language designed for mixed discrete/continuous domains. To reason about continuous aspects of the domain, Hydra employs time discretization techniques that raise a combinatorial search challenge. To meet this challenge, we propose domain-specific heuristics and a novel "preferred states" mechanism similar to the preferred operators mechanism from classical planning. We compared Hydra with state-of-the-art Angry Birds agents. The results show Hydra can solve a greater diversity of Angry Birds levels compared to other agents and highlight its current limitations.
Adapting to Planning Failures in Lifelong Multi-Agent Path Finding

Multi-Agent Path Finding (MAPF) is the problem of finding collision-free paths for multiple agents operating in the same environment. In Lifelong MAPF (LMAPF), these agents continuously receive new destinations, and the task is to constantly update their paths while optimizing for a high throughput over time. Therefore, many MAPF sub-problems must be solved over time in order to solve a single LMAPF problem. LMAPF problems manifest in real-world applications, such as automated warehouses, where strict responsiveness requirements limit the amount of time allocated to planning. MAPF algorithms occasionally fail to produce a plan within the allotted time. We propose a system design for LMAPF that is robust to such planning failures. Then, we explore different approaches to avoid planning failures, reduce their severity, and handle them when they occur. In particular, we describe and analyze different Fail Policies that are applied when planning failures occur and ensure collisions and unnecessary degradation of throughput are avoided. To our knowledge, while such Fail Policies are used in practice in the industry, they have yet to be researched academically.
Learning Local Heuristics for Search-Based Navigation Planning

Graph search planning algorithms for navigation typically rely heavily on heuristics to efficiently plan paths. As a result, while such approaches require no training phase and can directly plan long horizon paths, they often require careful hand designing of informative heuristic functions. Recent works have started bypassing hand designed heuristics by using machine learning to learn heuristic functions that guide the search algorithm. While these methods can learn complex heuristic functions from raw input, they i) require significant training and ii) do not generalize well to new maps and longer horizon paths. Our contribution is showing that instead of learning a global heuristic estimate, we can define and learn local heuristics which results in a significantly smaller learning problem and improves generalization. We show that using such local heuristics can reduce node expansions by 2-20x while maintaining bounded suboptimality, are easy to train, and generalize to new maps &amp; long horizon plans.
Adaptation and Communication in Human-Robot Teaming to Handle Discrepancies in Agents’ Beliefs about Plans

When agents collaborate on a task, it is important that they have some shared mental model of the task routines -- the set of feasible plans towards achieving the goals. However, in reality, situations often arise that such a shared mental model cannot be guaranteed, such as in ad-hoc teams where agents may follow different conventions or when contingent constraints arise that only some agents are aware of. Previous work on human-robot teaming has assumed that the team has a set of shared routines, which breaks down in these situations. In this work, we leverage epistemic logic to enable agents to understand the discrepancy in each other's beliefs about feasible plans and dynamically plan their actions to adapt or communicate to resolve the discrepancy. We propose a formalism that extends conditional doxastic logic to describe knowledge bases in order to explicitly represent agents' nested beliefs on the feasible plans and state of execution. We provide an online execution algorithm based on Monte Carlo Tree Search for the agent to plan its action, including communication actions to explain the feasibility of plans, announce intent, and ask questions. Finally, we evaluate the success rate and scalability of the algorithm and show that our agent is better equipped to work in teams without the guarantee of a shared mental model.
Giving Free English Lessons and Vocabulary Modules: Learning Beyond the Classroom Walls Through A Community Service Program

&lt;p&gt;&lt;em&gt;The community service aims at providing free English lessons and English vocabulary learning modules to grade 3 and 4 students in one learning community in Duren Sawit East Jakarta through a six-week teaching program that is integrated with one subject offered in the Master Program of Applied English Linguistics at Atma Jaya Catholic University of Indonesia. Ten master students became the voluntary teachers (together with other volunteers). The program is a continuation of a similar program held from January to June 2019 by the first author. The student-teachers used a variety of learning approaches and methods that are predicted to suit the students’ needs. They identified that different groups of students required different approaches, materials, and activities in order to learn English enjoyably. It is suggested that future teacher volunteers use the ready-made modules as a basis of their teaching and continually adjust them based on the students’ needs. Another recommendation is the scaffolding-based learning method and the sustenance of the English program that appreciates the cultural, social and linguistic assets students brought to the class. &lt;/em&gt;&lt;em&gt;&lt;/em&gt;&lt;/p&gt;
SISTEM INFORMASI PENERIMAAN CALON TENAGA KERJA INDONESIA PDA PT DEWI PENGAYOM BANGSA KABUPATEN PATI

&#x0D; One form of service in PT Dewi protector Nations Pati regency receive prospective workers to some of Taiwan, Hongkong, Singapore often have difficulty including data management is still done manually, the amount of data they accumulate, requiring a lot of space to save the files prospective migrants and the length of the search data. The system developed is expected to address the various issues of labor recruitment Indonesia. The system is designed with modeling UML (Unified Modeling Language) and programming languages PHP. The result of this design produces Candidate Information System Acceptance Indonesian Workers at PT Dewi protector Nation Pati regency.&#x0D; &#x0D;
Upaya Guru dalam Mengatasi Siswa yang Berkesulitan Belajar (Calistung) Baca, Tulis dan Hitung) di Kelas Rendah SDIT Bunayya

The teacher's efforts in overcoming students who have difficulty learning CALISTUNG (Read, Write, Count) in low grades at SDIT Bunayya, the results of direct observation show that some students in grades 1, 2 and 3 still have difficulty learning calistung, in low grade students it is an obstacle to the cycle. study activities. The various difficulties in learning calistung experienced in early childhood are: lack of order or not being able to focus on learning, children having difficulty understanding what is conveyed by the teacher and difficult to capture or absorb in the child's mind when counting, not yet familiar with the concept of subtraction, and not yet fluent when repeat letters a-z. In overcoming difficulties in learning, educators use interesting learning methods and media so that children become enthusiastic about learning and make it easier for students to learn CALISTUNG. Based on this, the researcher decided in this study to choose the title "Teacher's efforts in overcoming students who have learning difficulties with CALISTUNG (Read, Write and Count) in the lower grades of SDIT Bunayya." This research was carried out in four steps, namely: (1) Planning, (2) Implementation, (3) Observation, and (4) Evaluation.
USER EXPERIENCE RESEARCH USING A WEB-APPLICATION UX-QUESTIONNAIRE

The publication updates the issue of the quality of interface development and highlights the main problems in the study of user experience.The work contains an overview of the methodology for solving UX problems, namely the Evaluative Research Method.In summary, the results of this study once again emphasize the importance of user experience in business and in the interface design process.
INFLUENCE OF LEARNING RATE PARAMETER IN DIFFERENT TRANSFORMER MODELS IN TEXT CLASSIFICATION TASK

This article is focused on the influence of the learning rate parameter on the training results of pre-trained transformer models: BERT, DistilBERT, ALBERT, and XLM-RoBERTa. As data for models training and testing dataset from HuggingFace portal is used. This dataset contains labeled data for both testing and training purposes. Moreover, it contains unlabeled data for unsupervised models and algorithms. Instead of direct training and testing, Trainer and TrainingArgument classes from the HuggingFace portal were used. For batch formation, DataColator class was utilized. Different metrics of model training efficiency were considered: learning time, the output of validation and training loss functions. Work result allows comparing the efficiency of every observed model in binary text classification tasks standalone or in assembly with other models. Keywords : transformers, binary text classification, BERT, ALBERT, DistilBERT, XLM-RoBERTa.
ALGORITHM OF AMSGrad AND CHAOS OPTIMIZATION IN MULTILAYERED NEURON NETWORKS WITH STOCHASTIC GRADIENT DESCENT

In this paper, the AMSGrad stochastic optimization method was tested using the logistic function that describes the doubling process and the Fourier spectra of the error function. The implementation of the gradient descent optimization algorithm, using AMSGrad, was done for a multilayer neural network with hidden layers. The program for recognizing printed digits was written using the Python software environment. The array of each digit consisted of a set of "0" and "1" of size 4x7. The sample of each digit contained a set of 5 possible distortions and a set of 3 arrays that did not correspond to any of the digits. The analysis of the influence of the value of hyperparameters beta1 , beta2 , and learning rate on the optimizing process for teaching a multilayer neural network, which contained 3 hidden layers of 28 neurons each, was carried out. We constructed branching diagrams based on these parameters. We found that the hyperparameter beta1 , which describes the contribution of the linear gradient of the error function, is associated with a doubling of the number of local and global minima of the error function in the process of retraining the neural network. The hyperparameter beta2 , which describes the error function gradient square contribution, is associated with the block structure formation that blocks the number of local minima doubling processes. If the alpha learning rate is greater than the retraining rate, there is a transition to a chaotic state, which is accompanied by both multiple passage through the global minimum and, apparently, the appearance of local minima. At such a speed of learning, the optimizer practically does not work, but in the presence of the hyperparameter beta1 , i.e. a linear gradient, the general picture of the transition to chaos is described by the process of doubling the number of local minima. The application of the AMSGrad stochastic optimization method for teaching a multilayer neural network is shown to lead to better learning, compared to a conventional multilayer neural network, even at the optimal learning rate (the learning rate when the number of existing local and global minima doubles). Keywords: optimization methods, error function, AMSGrad, learning rate, branching diagrams.
Modelling and Simulation of Grid Connected Photovoltaic System using Matlab\Simulink Program

Renewable energy such as solar energy became an important alternative sustainable energy source because it is inexhaustibly compared with fossil sources. Therefore, Al-Furat Al-Awsat Technical University (ATU) is interested in relying entirely on renewable energy. There is an idea to duplicate the photovoltaic (PV) system of the Australian University of Queensland (UQ) and install it in ATU formations. Thus, this work aims to study the effectiveness of installing the PV system of UQ by modelling, simulation, and verification of a MATLAB/Simulink program that simulates the performance of the actual installed PV system. The main goal of the MATLAB/Simulink program is to conduct a feasibility study by studying the performance of the PV system according to the Iraqi climatic conditions to avoid material losses resulting from the direct installation of the system. The suggested model has been driven based on the principle mathematical equations that describe the behaviour of the PV system. Accordingly, environmental conditions such as sunlight irradiance and ambient temperature as well as the hardware system information are set as the essential inputs to the model. Whilst, the electrical power and the accumulative energy are chosen as the main output. The output of the proposed model was compared with the actual set of data conveyed from the University of Queensland, Australia. The results proved the effectiveness of the suggested model by presenting an acceptable match between the simulated output and the real data. Consequently, the authors recommend applying the proposed model for the entire ATU formations based on their respective climatic conditions, individually
Development of Based Learning Multimedia Flipbook on the Subject of Life Organization at SMPIT Al Multazam Kuningan

Learning patterns that only use blackboards, books, worksheets, and PowerPoint slides generally only focus on the teacher while students listen, take notes, and memorize more. This learning pattern can cause boredom and monotonous learning so students are not interested in the learning process. One effort to overcome this problem is to develop appropriate learning media such as flipbook-based learning multimedia. This research was conducted in class VII SMPIT Al Multazam Kuningan to develop flipbook-based learning multimedia. This research was conducted using the R&amp;D (Research and Development) method which refers to the ADDIE model. The percentage of media validation scores by experts is 87% from linguists, 90% from content/material experts, and 89% from media experts. Furthermore, at the trial stage, flipbook-based learning multimedia received an assessment percentage of 89.8% for the material aspect, 87.55% for the language aspect, 88.9% for the media aspect, and 88.75% for the average percentage. These results indicate that flipbook-based learning multimedia is very feasible to be applied in the learning process.
PENERAPAN MODEL PROBLEM BASED LEARNING UNTUK MENINGKATKAN HASIL BELAJAR PESERTA DIDIK KELAS 1 MATA PELAJARAN BAHASA INDONESIA DI SD NEGERI SAWAH BESAR 01

SD Negeri Sawah Besar 01 in the subject of Indonesian Language Chapter 7 (I Want) whose results do not meet the minimum completeness criteria, learning is still conventional, does not apply innovative learning models, thus making students passive. Improving learning by applying the Problem Based Learning (PBL) learning model aims to improve student learning outcomes in the subject of Indonesian Language Chapter 7 (I Want). The research subjects were class I students of SD Negeri Sawah Besar 01 with a total of 29 students consisting of 15 boys and 14 girls. Classroom action research was carried out in two cycles, each cycle consisting of the stages of planning, implementing, observing, and reflection. Research techniques using observation techniques, evaluation test as well as documentation and descriptive research methods. The results of the study show that applying the Problem Based Learning (PBL) model can improve student learning outcomes in Indonesian Chapter 7 (I Want). The results of the pre-cycle evaluation test, which was originally a class average of 69, increased to 93 in cycle 1, and in cycle 2 to 99. With KKM 70 the average pre-cycle completeness was 66%, cycle 1 became 93%, and cycle 2 became 100%. From the results of the study it was concluded that the application of the Problem Based Learning (PBL) model could improve the learning outcomes of class 1 students at SD Negeri Sawah Besar 01 in the subject of Indonesian Language Chapter 7 (I Want).
IMPLEMENTASI MODEL PEMBELAJARAN PROBLEM BASED INSTRUCTION UNTUK MENINGKATKAN HASIL BELAJAR SISWA KELAS III SD

&#x0D; This study aims to determine the increase in student learning outcomes after implementing thematic learning using the Problem-Based Instruction Model. This research is a class action research (CAR) which was carried out for two cycles. The subjects of this study were 28 grade III students at SDN Peterongan Semarang City. Collecting data using observation techniques and learning achievement tests. The collected data were analyzed by calculating the percentage of learning success and the average value achieved by students. The results showed that the thematic learning outcomes through the application of the Problem-Based Instruction (PBI) learning model in class III students in semester II of the 2022/2023 school year were stated to increase, this was evidenced by an increase in the average class III student learning outcomes in cycle I by 72, 85 and in cycle II to 80.35. Thus, the application of the Problem-Based Instruction (PBI) learning model to third-grade students at SDN Peterongan Semarang City can improve thematic learning outcomes.
The Implementation of the Reading Aloud Strategy in Enhancing Oral Transactions: Giving and Asking for Information

English is an international language currently used in various ways, including communication. Therefore, English needs to be developed from an early age to improve the ability to interact orally by giving and asking for information according to the basic competence of the subject. This study aims to determine how the Reading Aloud strategy is implemented and what aspects increase when the strategy is applied as a learning method to improve the ability to interact orally in class VIII MTs Darul Falah. This research used a descriptive qualitative approach. The participants used were 33 class VIII students of MTs Darul Falah. Sources of data and information for this research are English teachers and students. Data collection instruments were in the form of observation checklists, field notes, and interviews. Data analysis consists of data reduction, conclusion drawing, and verification. Based on the results of research and discussion, the Reading Aloud method can improve students' ability to interact orally to give and ask for information. The aspects that improved when the Reading Aloud method was implemented in class VIII students of MTs Darul Falah were 1) knowledge aspects consisting of vocabulary, fluency, accuracy and intonation; 2) attitude aspect consisting of respect, caring and self-confidence; 3) behavioral aspects consisting of working together and communicating.