Reviewerly/exHarmony huggingface dataset:
ODTlearn: A Package for Learning Optimal Decision Trees for Prediction and Prescription

ODTLearn is an open-source Python package that provides methods for learning optimal decision trees for high-stakes predictive and prescriptive tasks based on the mixed-integer optimization (MIO) framework proposed in Aghaei et al. (2019) and several of its extensions. The current version of the package provides implementations for learning optimal classification trees, optimal fair classification trees, optimal classification trees robust to distribution shifts, and optimal prescriptive trees from observational data. We have designed the package to be easy to maintain and extend as new optimal decision tree problem classes, reformulation strategies, and solution algorithms are introduced. To this end, the package follows object-oriented design principles and supports both commercial (Gurobi) and open source (COIN-OR branch and cut) solvers. The package documentation and an extensive user guide can be found at https://d3m-research-group.github.io/odtlearn/. Additionally, users can view the package source code and submit feature requests and bug reports by visiting https://github.com/D3M-Research-Group/odtlearn.
Mathematical Foundations for Joining Only Knowing and Common Knowledge

Common knowledge and only knowing capture two intuitive and natural notions that have proven to be useful in a variety of settings, for example to reason about coordination or agreement between agents, or to analyse the knowledge of knowledge-based agents. While these two epistemic operators have been extensively studied in isolation, the approaches made to encode their complex interplay failed to capture some essential properties of only knowing. We propose a novel solution by defining a notion of μ-biworld for countable ordinals μ, which approximates not only the worlds that an agent deems possible, but also those deemed impossible. This approach allows us to define a multi-agent epistemic logic with common knowledge and only knowing operators, and a three-valued model semantics for it. Moreover, we show that we only really need biworlds of depth at most ω²+1. Based on this observation, we define a Kripke semantics on a canonical Kripke structure and show that this semantics coincides with the model semantics. Finally, we discuss issues arising when combining negative introspection or truthfulness with only knowing and show how positive introspection can be integrated into our logic.
A²CoST: An ASP-based Avoidable Collision Scenario Testbench for Autonomous Vehicles

This paper addresses the challenge of generating safety-critical scenarios with multiple adversarial vehicles for testing autonomous vehicles. Such scenarios must be plausible and collision-avoidable while resulting in a collision with the vehicle-under-test. However, the tremendous number of scenarios and the low ratio of plausible scenarios makes previous methods squander primary resources on implausible scenarios, degenerating their efficiency. We propose a two-stage framework called the ASP-based Avoidable Collision Scenario Testbench (A²CoST) to overcome this obstacle and improve efficiency. In the former stage, we apply Answer Set Programming (ASP) for generating plausible logical scenarios. In the latter stage, we use a search algorithm to refine logical scenarios into safety-critical concrete scenarios. We also compute collision-free trajectories in these concrete scenarios while the vehicle-under-test fails to avoid the collision. We empirically show the A²CoST significantly decreases the time consumption for simple scenarios while still effectively generating complex critical scenarios. The comparison with real-world traffic data further demonstrates the value of A²CoST in generating plausible scenarios. The source codes of our method and the baselines are opened at https://github.com/Autonomous-Driving-Safety-Project/AACoST.
Counterfactual Reasoning via Grounded Distance

Conditional logics are usually interpreted in terms of closest world and minimal change. It relies on a measure of distance between worlds which is defined abstractly, i.e. as an element of the model. The typical example of a concrete measure in literature is the Hamming distance. We show that given countably infinite atomic propositions in the language, Hamming distance is not merely an example, but grounded for two arguably most important conditional logics, Lewis' VC and VCU. That means, a formula is satisfied in a VC (resp. VCU) model, if and only if it is satisfied in a VC (resp. VCU) model whose distance between worlds is Hammingian.
Property Directed Reachability for Planning Revisited

Property Directed Reachability (PDR) is a relatively new SAT-based search paradigm for classical AI planning. Compared to earlier SAT-based paradigms, PDR proceeds without unrolling the system transition function, and therefore without having the underlying procedure reason about potentially computationally expensive multi-step formulae. By maintaining a queue of obligations - i.e., a state at a timestep - and knowledge about what is possible at each planning step, PDR iteratively evaluates whether an obligation can be progressed by one step towards the goal. We develop and evaluate two new distributed PDR algorithms for planning, and additionally implement serial and portfolio PDR algorithms for planning. We are the first to consider distributed PDR for planning and the first to consider PDR based on incremental SAT solving in that setting. Our first new algorithm, PS-PDR, evaluates many obligations independently in parallel using a pool of incremental SAT workers. PS-PDR is unique amongst distributed PDR algorithms in centrally maintaining a single queue of obligations, enabling an efficient focused search compared to a PDR portfolio. Our second new algorithm, PD-PDR, sequences subproblems according to the compositional structure of the concrete problem at hand. Subproblems are solved independently in parallel, with a concrete plan obtained by combining subproblem plans. Our experimental evaluation exhibits strong runtime gains for both new algorithms in both satisfiable and unsatisfiable planning benchmarks.
PENGARUH MEDIA PEMBELAJARAN PERMAINAN MONOPOLI TERHADAP HASIL BELAJAR IPS TERPADU DI SMPN 3 SATU ATAP BOYAN TANJUNG

The problem in this research is that the students' activity in the class is still lacking and there are students who have low learning outcomes. The purpose of this study was to determine the effect of using monopoly game learning media on Integrated Social Studies learning outcomes. This research method uses quantitative research with a quasi-experimental approach and the form of research uses experimental research. The sample in this study consisted of 20 students in class VII A as the experimental class and class VII B with 20 students as the control class. Techniques and data collection tools using observation sheets, test questions and documentation. The results of this study indicate that there is an effect of using monopoly game learning media on the learning outcomes of Integrated Social Studies for seventh grade students at SMPN 3 Satu Atap Boyan Tanjung. The results of the pretest and posttest calculations in the experimental class, the value of sig (2-tailed) = 0.000 is smaller than 0.05, it means that H0 is rejected. Ha is accepted. Pretest and posttest in the control class, the value of sig (2-tailed) = 0.000 is smaller than 0.05, it means that H0 is rejected. Ha is accepted at a significance level of 5%.
Connecting Proof Theory and Knowledge Representation: Sequent Calculi and the Chase with Existential Rules

Chase algorithms are indispensable in the domain of knowledge base querying, which enable the extraction of implicit knowledge from a given database via applications of rules from a given ontology. Such algorithms have proved beneficial in identifying logical languages which admit decidable query entailment. Within the discipline of proof theory, sequent calculi have been used to write and design proof-search algorithms to identify decidable classes of logics. In this paper, we show that the chase mechanism in the context of existential rules is in essence the same as proof-search in an extension of Gentzen's sequent calculus for first-order logic. Moreover, we show that proof-search generates universal models of knowledge bases, a feature also exhibited by the chase. Thus, we formally connect the main tool for establishing decidability proof-theoretically with a central decidability tool in the context of knowledge representation.
Revising Typical Beliefs: One Revision to Rule Them All

Propositional Typicality Logic (PTL) extends propositional logic with a connective • expressing the most typical (alias normal or conventional) situations in which a given sentence holds. As such, it generalises e.g.~preferential logics that formalise reasoning with conditionals such as ``birds typically fly''. In this paper, we study revision of sets of PTL-sentences. We first show why it is necessary to extend the PTL-language with a possibility operator, and then define the revision of PTL-sentences syntactically and characterise it semantically. We show that this allows us to represent a wide variety of existing revision methods, such as propositional revision and revision of epistemic states. Furthermore, we provide several examples showing why our approach is innovative. In more detail, we study revision of a set of conditionals under preferential closure, and the addition and contraction of possible worlds from an epistemic state.
From Vision to Execution: Enabling Knowledge Representation and Reasoning in Hybrid Intelligent Robots Playing Mobile Games

Automating acts on touch surfaces opens a range of possibilities for researching and experimenting with hybrid AI approaches. In this paper, we propose a delta robot capable of playing match-3 games and ball-sorting puzzles by acting on mobile phones. The robot recognizes objects of different colors and shapes through a vision module, is capable of making strategic decisions based on declarative models of the game's rules and of the game playing strategy, and features an effector that executes moves on physical devices. Our solution integrates multiple AI methods, including vision processing and answer set programming. Helpful and reusable infrastructure is provided: the vision task is facilitated, while robot motion control is inherently simplified by the usage of a delta robot layout. We illustrate the components of our robotic application and how they were integrated. Then, we briefly showcase how recognition and general knowledge can be modeled and implemented, by overviewing the implementation of representative games. We argue that our application provides potential for KR and robotics to be combined in creative ways, and offers itself as a general controlled environment where to experiment with forms of hybrid reasoning, while relieving from implementation details.
Foundations for Projecting Away the Irrelevant in ASP Programs

Simplification of logic programs under the answer-set semantics has been studied from the very beginning of the field. One natural simplification is the removal of atoms that are deemed irrelevant. While equivalence-preserving rewritings are well understood and incorporated in state-of-the-art systems, more careful rewritings in the realm of strong or uniform equivalence have received considerably less attention. This might be due to the fact that these equivalence notions rely on comparisons with respect to context programs that remain the same for both the original and the simplified program. In this work, we pursue the idea that the atoms considered irrelevant are disregarded accordingly in the context programs of the simplification, and propose novel equivalence notions for this purpose. We provide necessary and sufficient conditions for these kinds of simplifiability of programs, and show that such simplifications, if possible, can actually be achieved by just projecting the atoms from the programs themselves. We furthermore provide complexity results for the problems of deciding simplifiability and equivalence testing.
Towards Parallelising Extension Construction for Serialisable Semantics in Abstract Argumentation

We consider the recently proposed notion of serialisability of semantics for abstract argumentation frameworks. This notion describes a method for the serialised non-deterministic construction of extensions through iterative addition of non-empty minimal admissible sets. Depending on the semantics, the task of enumerating all extensions for an argumentation framework can be computationally complex. Serialisability provides a natural way of parallelising the construction of extensions for most admissible-based semantics. In this work, we investigate the feasibility of using the serialisable construction scheme for a more efficient enumeration of extensions on the example of the recently introduced unchallenged semantics and provide an experimental evaluation.
Description Logics with Abstraction and Refinement

Ontologies often require knowledge representation on multiple levels of abstraction, but description logics (DLs) are not well-equipped for supporting this. We propose an extension of DLs in which abstraction levels are first-class citizens and which provides explicit operators for the abstraction and refinement of concepts and roles across multiple abstraction levels, based on conjunctive queries. We prove that reasoning in the resulting family of DLs is decidable while several seemingly harmless variations turn out to be undecidable. We also pinpoint the precise complexity of our logics and several relevant fragments.
Integrating Linear Arithmetic Constraints Into Conditional Maximum Entropy Reasoning

The principle of maximum entropy (MaxEnt principle) constitutes a valuable methodology for probabilistic commonsense reasoning by adding missing information to probabilistic conditional belief bases in an information theoretically optimal way. In this paper, we integrate linear arithmetic constraints over the integers and reals into propositional probabilistic conditionals in order to be able to formalize uncertain beliefs about arithmetic expressions. The satisfiability of (sets of) constraints is decided modulo theory such that probabilistic reasoning stays finite although the constraints range over infinite domains. Therewith, we provide a novel extension of the MaxEnt principle to beliefs about infinite domains.
Planning with Epistemic Preferences

Within the field of automated planning, two areas of study are planning with preferences and epistemic planning. Planning with preferences involves generating plans that optimize for properties of the plan instead of, or in addition to, trying to reach a fixed goal. Epistemic planning allows for planning over the knowledge or belief states of one or more agents for the purpose of achieving epistemic goals (where agents have particular states of knowledge or belief). In this paper we motivate and explore the task of planning with epistemic preferences, proposing a method by which existing automated planning techniques can be combined for this purpose.
UPAYA MENINGKATKAN HASIL BELAJAR IPS MELALUI MODEL PEMBELAJARAN MAKE A MATCH PADA SISWA KELAS IX SMP NEGERI 2 NANGA PINOH

The study aims to whether there is an effect of learning models make a match on Social Studies learning outcomes of ninth grade students of SMPN 2 Nanga Pinoh. The method used in this study is classroom action research (CAR) which consist of two cylcles. Each round consist of four stages; planning, observing, reflecting, and revising. The location of the study was at SMP Negeri 2 Nanga Pinoh. The research subjects were students of class IX in academic year 2021/2022. The data obtained in the form of formative test results, observation sheets of teaching and learning activities. From the results analysis, it was found that student achievement increase from cycle 1 to cycle II. Cycle 1 (65,38%), cycle II (88,46%). The conclusion of this study is the make a match method can positively influence the achievement and motivation of students in class IX SMPN 2 Nanga Pinoh I academic year 2021/2022 and this learning model can be used as an alternative to Social Studies learning.
Complexity of Inconsistency-Tolerant Query Answering in Datalog+/– under Preferred Repairs

Inconsistency-tolerant semantics have been proposed to provide meaningful ontological query answers even in the presence of inconsistencies. Several such semantics rely on the notion of a repair, which is a "maximal" consistent subset of the database, where different maximality criteria might be adopted depending on the application at hand. Previous work in the context of Datalog+/- has considered only the subset and cardinality maximality criteria. We take here a step further and study inconsistency-tolerant semantics under maximality criteria based on weights and priority levels. We provide a thorough complexity analysis for a wide range of existential rule languages and for several complexity measures.
Pushing the Boundaries of Tractable Multiperspective Reasoning: A Deduction Calculus for Standpoint EL+

Standpoint EL is a multi-modal extension of the popular description logic EL that allows for the integrated representation of domain knowledge relative to diverse standpoints or perspectives. Advantageously, its satisfiability problem has recently been shown to be in PTime, making it a promising framework for large-scale knowledge integration. In this paper, we show that we can further push the expressivity of this formalism, arriving at an extended logic, called Standpoint EL+, which allows for axiom negation, role chain axioms, self-loops, and other features, while maintaining tractability. This is achieved by designing a satisfiability-checking deduction calculus, which at the same time addresses the need for practical algorithms. We demonstrate the feasibility of our calculus by presenting a prototypical Datalog implementation of its deduction rules.
Global Model Explanation for Time Series

This is a novel technique to find global explanations in binary classification machine learning models by finding the salience of features. The explanation can be made on categorical, continuous, and time series data. Coefficients from a Cox Proportional Hazards regression explain the effect of variables upon the probability of an in-class response for a score output from the black box model. The analysis is conducted on a long short-term memory (LSTM) network.
Development of the non-banking market in the Republic of Moldova

The qualitative and continuous consolidation of the non-bank lending sector indicates a trend of transforming microfinance intermediaries into institutions that provide financial services professionally, these entities being an affordable alternative for lending to the population as well as small and medium enterprises. The significant dependence of the enterprises on the bank financing and the limited access to alternative financing sources determine that these enterprises are extremely vulnerable in the conditions of restricting the bank credit. The purpose of the research is to describe the situation in the field of research by elucidating the existing problems at the current stage of financing on the non-banking market in the Republic of Moldova.
Learning to Open Doors with an Aerial Manipulator

The field of aerial manipulation has seen rapid advances, transitioning from push-and-slide tasks to interaction with articulated objects. So far, when more complex actions are performed, the motion trajectory is usually handcrafted or a result of online optimization methods like Model Predictive Control (MPC) or Model Predictive Path Integral (MPPI) control. However, these methods rely on heuristics or model simplifications to efficiently run on onboard hardware, producing results in acceptable amounts of time. Moreover, they can be sensitive to disturbances and differences between the real environment and its simulated counterpart. In this work, we propose a Reinforcement Learning (RL) approach to learn motion behaviors for a manipulation task while producing policies that are robust to disturbances and modeling errors. Specifically, we train a policy to perform a door-opening task with an Omnidirectional Micro Aerial Vehicle (OMAV). The policy is trained in a physics simulator and experiments are presented both in simulation and running onboard the real platform, investigating the simulation to real world transfer. We compare our method against a state-of-the-art MPPI solution, showing a considerable increase in robustness and speed.
The Extended and Ensemble Kalman Filters

In this chapterwe describe the Extended Kalman Filter (ExKF)1 and the Ensemble Kalman Filter (EnKF). The ExKF approximates the predictive covariance by linearization, while the EnKF approximates it by the empirical covariance of a collection of particles. The ExKF is a provably accurate approximation of the filtering distribution if the dynamics are approximately linear and small noise is present in both signal and data, in which case the filtering distribution is well approximated by a Gaussian.
Online Clustered Codebook

Vector Quantisation (VQ) is experiencing a comeback in machine learning, where it is increasingly used in representation learning. However, optimizing the codevectors in existing VQ-VAE is not entirely trivial. A problem is codebook collapse, where only a small subset of codevectors receive gradients useful for their optimisation, whereas a majority of them simply ``dies off'' and is never updated or used. This limits the effectiveness of VQ for learning larger codebooks in complex computer vision tasks that require high-capacity representations. In this paper, we present a simple alternative method for online codebook learning, Clustering VQ-VAE (CVQ-VAE). Our approach selects encoded features as anchors to update the ``dead'' codevectors, while optimising the codebooks which are alive via the original loss. This strategy brings unused codevectors closer in distribution to the encoded features, increasing the likelihood of being chosen and optimized. We extensively validate the generalization capability of our quantiser on various datasets, tasks (e.g. reconstruction and generation), and architectures (e.g. VQ-VAE, VQGAN, LDM). Our CVQ-VAE can be easily integrated into the existing models with just a few lines of code.