With core c-representations we develop a new class of ranking models for conditional belief bases that combine the advantages of c-representations and System Z. On the one hand, they exhibit high-quality inferential behavior, just like c-representations, and on the other hand, they are stratified like the System Z ranking function, and can thus be constructed layer by layer. This allows for the identification of a unique minimal core c-representation from which we derive a new inductive inference operator, the c-core closure operator. This inference operator features conditional syntax splitting, like skeptical c-inference, and therefore does not suffer from the drowning problem, in contrast to System Z. Additionally, c-core closure satisfies rational monotony and inductive enforcement, and belongs to the class of basic defeasible entailment operators.
The connection between subset-maximal repairs for inconsistent databases involving various integrity constraints and acceptable sets of arguments within argumentation frameworks has recently drawn growing interest. In this paper, we contribute to this domain by establishing a new connection when integrity constraints (ICs) include denial constraints and local-as-view tuple-generating dependencies. It turns out that SET-based Argumentation Frameworks (SETAFs), an extension of Dung's argumentation frameworks (AFs) allowing collective attacks, are needed. It is known that subset-maximal repairs under denial constraints correspond to the naive extensions, which also coincide with the preferred and stable extensions in the resulting SETAFs. Our main findings establish that repairs under the considered fragment of tuple-generating dependencies correspond to the preferred extensions. Moreover, for these dependencies, additional preprocessing allows computing a unique extension that is stable and naive. Allowing both types of constraints breaks this relationship, and even the pre-processing does not help as only preferred semantics captures these repairs. Finally, while it is known that functional dependencies do not require set-based attacks, we prove the same regarding inclusion dependencies. Thus, one can translate inconsistent databases under these restricted classes of ICs to plain AFs with attacks only between arguments.
Combinatorial optimization problems are highly relevant for real-world applications. For complex problems, the use of exact solution techniques is limited to small problem sizes, and, hence, effective heuristic approaches are needed. Furthermore, most approaches require that for different input data, solutions have to be computed individually for each problem instance. Recent developments in Reinforcement Learning (RL) provide promising alternatives, as they allow for heuristic out-of-the-box solutions for arbitrary input data after being trained. Transformer-based RL approaches even have the capability to generalize with regard to the problem size and allow for the provision of quick solutions for problems that are larger than they have been trained on. However, despite their potential, the amount of different RL algorithms is large, and their performance for combinatorial optimization problems is unclear. To resolve this issue, the performance of different state-of-the-art RL algorithms are compared when applied to the classical Traveling Salesman Problem (TSP), and the Orienteering Problem (OP). Some RL algorithms are found to achieve promising results with: (i) near-optimal performance compared to optimal solutions for single tractable problem instances; while (ii) providing the capability to generalize regarding both the input data (continuous coordinates), and the problem size (number of nodes).
The article is concerned with realizability in abstract argumentation. It provides characterization theorems for the most basic types of labelling-based semantics, namely conflict-free and naive labellings . It turns out that existing characterizations for extension-based semantics are of little help in characterizing labelling-based semantics. To this end, we introduce several new criteria like L-tightness , reject-witnessing , reject-compositionality as well as the new construct of a labelling-downward-closure , which help determine whether a given set of labellings is realizable regarding conflict-free or naive semantics. Moreover, we present standard constructions and analyse their uniqueness status. Further classical concepts like ordinary and strong equivalence are studied too. Last but not least, we delve into the characterization of stable labellings. It turns out that this endeavour is a highly non-trivial task with many parallels to so-called compact realizability , an open problem for stable semantics in abstract argumentation.
Plan repair is the problem of solving a given planning problem by using a solution plan of a similar problem. This paper presents the first approach where the repair has to be done optimally, that is, we aim at finding a minimum distance plan from an input plan; we do so by introducing a number of compilation schemes that convert a classical planning problem into another where optimal plans correspond to plans with the minimum distance from an input plan. We also address the problem of finding a minimum distance plan from a set of input plans, instead of just one plan. Our experiments using a number of planners show that such a simple approach can solve many problems optimally and more effectively than replanning from scratch for a large number of cases. Also, the approach proves competitive with ${\mathsf{LPG}\textrm{-}\mathsf{adapt}}$ , a state-of-the-art approach for the plan repair problem.
We consider nonmonotonic inferences from belief bases that contain conditionals enforcing some of the possible worlds to be infeasible and thus completely implausible. In contrast to belief bases satisfying the strong notion of consistency requiring every world to be at least somewhat plausible, we call such belief bases weakly consistent. First, we review the treatment of weakly consistent belief bases by the seminal approaches of p-entailment, which coincides with system P, and of system Z, which coincides with rational closure. Then we focus on c-inference, an inductive inference operator that has been shown to exhibit many desirable properties put forward for nonmonotonic reasoning. It is based on c-representations, which are a special kind of ranking model ordering worlds according to their plausibility. While c-representation is defined for strongly consistent belief bases only, in this article, we extend the notions of c-representation and of c-inference to cover also weakly consistent belief bases. We adapt a constraint satisfaction problem (CSP) characterizing c-representations to capture extended c-representations, and we show how this extended CSP can be used to characterize extended c-inference, providing a basis for its implementation. We show various properties of extended c-inference and in particular, we prove that also the extended notion of c-inference fully satisfies syntax splitting. Furthermore, we extend and evaluate credulous and weakly skeptical c-inference to weakly consistent belief bases and provide characterizations for them as CSPs.
While modern definitions of business processes exist and are shared in the business process management (BPM) community, a commonly agreed meta-model is still missing. Nonetheless, several different business process meta-models have been proposed and discussed in the literature, which look at business process models from different perspectives, focusing on different aspects and often using different labels for denoting the same element or element relation. In this paper, we extend and consolidate an effort of building a business process meta-model starting from elements and relations discovered inspecting relevant literature through a systematic literature review. The obtained literature-based business process meta-model , which is on purpose built to disclose critical issues, is then inspected, compared to a previous, more restricted, version, and discussed. The analysis confirms a lack of attention to some crucial business process elements, as well as the presence of some unclear relations and subsumption cycles. Moreover it brings about new issues and inconsistencies in the meta-models proposed in literature, which we address - at least in part - using an ontological analysis.
Exception handling has been successfully proposed in the past years as a simple yet powerful software engineering tool to promote modularity and decoupling, while also preserving robustness. Multi-agent systems (MAS) and organizations (MAOs), in turn, offer powerful abstractions to build distributed systems; current models and methodologies, however, fall short in addressing exception handling in a systematic way, not considering exceptions as part of their design. Thus, the problem is usually approached by ad hoc solutions that hamper code modularization and decoupling. In this work, we outline a vision of how exception handling in MAS can be granted by design. We present an extension of the organizational model and infrastructure adopted in JaCaMo, that explicitly encompasses the notion of exception as a first-class element in the design of an organization. Relying on such a model, we propose an exception handling mechanism that is seamlessly integrated with organizational concepts, such as responsibilities, goals, and norms. In an organization, besides responsibilities for organizational goals, we propose to specify also responsibilities for managing exceptions, that is, for providing feedback about the context in which exceptions occur, and for handling it.
As the global population ages, effective home healthcare solutions become essential. Over a decade ago, ambient-assisted living (AAL) emerged as a promising solution, especially when combined with the potential of the Internet of Things (IoT) to revolutionize healthcare delivery. However, integrating diverse smart home devices with healthcare systems poses challenges regarding interoperability and real-time, context-aware responses. Addressing these challenges, this study introduces an ontology for AAL that seamlessly merges IoT and Smart Home ontologies with the established healthcare ontology, SNOMED CT. This ontology-centric approach facilitates semantic interoperability and knowledge sharing, paving the way for more personalized healthcare delivery. The core of this work lies in developing an AAL monitoring system grounded in this ontology. By incorporating Semantic Web Rule Language (SWRL) rules, the system can provide context-sensitive automated alerts and responses, taking into account patient-specific attributes, household features, and instantaneous sensor data. Empirical testing in the Halmstad Intelligent Home (HINT) highlights the system’s viability for practical deployment. Preliminary results indicate that the proposed integrative ontology-driven strategy holds significant potential to enhance healthcare services in AAL environments, marking an essential step towards achieving personalized, patient-centric care.
Traditional frequent itemset mining (FIM) is constrained by several limitations, mainly due to its failure to account for item quantity and significance, including factors such as price and profit. To address these limitations, high utility itemset mining (HUIM) is presented. Traditional HUIM algorithms are designed to operate solely on static transactional datasets. Nevertheless, in practical applications, datasets tend to be dynamic, with examples like market basket analysis and business decision-making involving regular updates to the data. Dynamic datasets are updated incrementally with the frequent addition of new data. Incremental HUIM (iHUIM) approaches mine the high utility itemsets (HUIs) from incremental datasets without scanning the whole dataset. In contrast, traditional HUIM approaches require a full dataset scan each time the dataset is updated. Consequently, iHUIM approaches effectively reduce the computational cost of identifying HUIs whenever a new record is added. This survey provides a novel taxonomy that includes two-based, pattern-growth-based, projection-based, utility-list-based, and pre-large-based algorithms. The paper delivers an in-depth analysis, covering the features and characteristics of the existing state-of-the-art algorithms. Additionally, it supplies a detailed comparative overview, advantages, disadvantages, and future research directions of these algorithms. The survey provides both a categorized analysis and a comprehensive, consolidated summary and analysis of all current state-of-the-art iHUIM algorithms. It offers a more in-depth comparative analysis than the currently available state-of-the-art surveys. Additionally, the survey highlights several research opportunities and future directions for iHUIM.
Advertising click-through rate (CTR) prediction is a fundamental task in recommender systems, aimed at estimating the likelihood of users interacting with advertisements based on their historical behavior. This prediction process has evolved through two main stages: from traditional shallow interaction models to more advanced deep learning approaches. Shallow models typically operate at the level of individual features, failing to fully leverage the rich, multilevel information available across different feature sets, leading to less accurate predictions. In contrast, deep learning models exhibit superior feature representation and learning capabilities, enabling a more realistic simulation of user interactions and improving the accuracy of CTR prediction. This paper provides a comprehensive overview of CTR prediction algorithms in the context of recommender systems. The algorithms are categorized into two groups: shallow interactive models and deep learning-based prediction models, including deep neural networks, convolutional neural networks, recurrent neural networks, and graph neural networks. Additionally, this paper also discusses the advantages and disadvantages of the aforementioned algorithms, as well as the benchmark datasets and model evaluation methods used for CTR prediction. Finally, it identifies potential future research directions in this rapidly advancing field.
One common approach to solve multi-objective reinforcement learning (MORL) problems is to extend conventional Q-learning by using vector Q-values in combination with a utility function. However issues can arise with this approach in the context of stochastic environments, particularly when optimising for the Scalarised Expected Reward (SER) criterion. This paper extends prior research, providing a detailed examination of the factors influencing the frequency with which value-based MORL Q-learning algorithms learn the SER-optimal policy for an environment with stochastic state transitions. We empirically examine several variations of the core multi-objective Q-learning algorithm as well as reward engineering approaches, and demonstrate the limitations of these methods. In particular, we highlight the critical impact of the noisy Q-value estimates issue on the stability and convergence of these algorithms.
Abstract The information deployment on social networks through word-of-mouth spreading by online users has contributed well to forming opinions, social groups, and connections. This process of information deployment is known as information diffusion. Its process and models play a significant role in social network analysis. Seeing this importance, the present paper focuses on the process, model, deployment, and applications of information diffusion analysis. First, this article discusses the background of the diffusion process, such as process, components, and models. Next, information deployment in social networks and their application have been discussed. A comparative analysis of literature corresponding to applications like influence maximization, link prediction, and community detection is presented. A brief description of performative evaluation metrics is illustrated. Current research challenges and the future direction of information diffusion analysis regarding social network applications have been discussed. In addition, some open problems of information diffusion for social network analysis are also presented.
Abstract Real-time strategy (RTS) games have provided a fertile ground for AI research with notable recent successes based on deep reinforcement learning (RL). However, RL remains a data-hungry approach featuring a high sample complexity. In this paper, we focus on a sample complexity reduction technique called reinforcement learning as a rehearsal (RLaR) and on the RTS game of MicroRTS to formulate and evaluate it. RLaR has been formulated in the context of action-value function based RL before. Here, we formulate it for a different RL framework, called actor-critic RL. We show that on the one hand the actor-critic framework allows RLaR to be much simpler, but on the other hand, it leaves room for a key component of RLaR–a prediction function that relates a learner’s observations with that of its opponent. This function, when leveraged for exploration, accelerates RL as our experiments in MicroRTS show. Further experiments provide evidence that RLaR may reduce actor noise compared to a variant that does not utilize RLaR’s exploration. This study provides the first evaluation of RLaR’s efficacy in a domain with a large strategy space.
Abstract Several real-world optimization problems are dynamic and involve a number of objectives. Different researches using evolutionary algorithms focus on these characteristics, but few works investigate problems that are both dynamic and many-objective. Although widely investigated in formulations with multiple objectives, the evolutionary approaches are still challenged by the dynamic multiobjective optimization problems defining a relevant research topic. Some models have been proposed specifically to attack them as the well-known DNSGA-II and MS-MOEA algorithms, which have been extensively investigated on formulations with two or three objectives. Recently, the D-MEANDS algorithm was proposed for dynamic many-objective problems (DMaOPs). In a previous work, D-MEANDS was confronted to DNSGA-II and MS-MOEA solving dynamic many-objective scenarios of the knapsack problem: up to six objectives with five changes or four objectives with ten changes. In this work, we evaluate the behavior of such algorithms in instances up to eight objectives and twenty environmental changes. These enabled us to better understand D-MEANDS weak points which led us to the proposition of D-MEANDS-MD. The proposal offers a better balance between memory and diversity. We also included a more recent MOEA in this comparison: the DDIS-MOEA/D-DE. From the results obtained using 27 instances of the dynamic multiobjective knapsack problem, D-MEANDS-MD showed promise for solving discrete DMaOPs compared with the others.
Abstract Online reviews have a significant impact on the purchasing decisions of potential consumers. Positive reviews often sway buyers, even when faced with higher prices. This phenomenon has given rise to a deceptive industry dedicated to crafting counterfeit reviews. Companies frequently indulge in procuring bulk fake reviews, employing them to tarnish their rivals’ reputations or artificially bolster their credibility. These spurious reviews materialize through automated systems or compensated individuals. Thus, detecting fake reviews is becoming increasingly important due to their deceptive nature, as they are extremely difficult for humans to identify. To address this issue, current work has focused on machine learning and deep learning techniques to identify fake reviews. However, they have several limitations, including a lack of sufficient training data, inconsistency in providing accurate solutions across different datasets, concept drift, and inability to address new methods that evolved to create fake reviews over time. The objective of this review paper is to find the gaps in the existing research in the field of fake review detection and provide future directions. This paper provides the latest, comprehensive overview and analysis of research efforts focusing on various techniques employed so far, distinguishing characteristics utilized, and the existing datasets used.
Abstract Recently, the field of robotics development and control has been advancing rapidly. Even though humans effortlessly manipulate everyday objects, enabling robots to interact with human-made objects in real-world environments remains a challenge despite years of dedicated research. For example, typing on a keyboard requires adapting to various external conditions, such as the size and position of the keyboard, and demands high accuracy from a robot to be able to use it properly. This paper introduces a novel hierarchical reinforcement learning algorithm based on the Deep Deterministic Policy Gradient (DDPG) algorithm to address the dual-arm robot typing problem. In this regard, the proposed algorithm employs a Convolutional Auto-Encoder (CAE) to deal with the associated complexities of continuous state and action spaces at the first stage, and then a DDPG algorithm serves as a strategy controller for the typing problem. Using a dual-arm humanoid robot, we have extensively evaluated our proposed algorithm in simulation and real-world experiments. The results showcase the high efficiency of our approach, boasting an average success rate of 96.14% in simulations and 92.2% in real-world settings. Furthermore, we demonstrate that our proposed algorithm outperforms DDPG and Deep Q-Learning, two frequently employed algorithms in robotic applications.
Advances in artificial intelligence (AI) have great potential to help address societal challenges that are both collective in nature and present at national or trans-national scale. Pressing challenges in healthcare, finance, infrastructure and sustainability, for instance, might all be productively addressed by leveraging and amplifying AI for national-scale collective intelligence. The development and deployment of this kind of AI faces distinctive challenges, both technical and socio-technical. Here, a research strategy for mobilising inter-disciplinary research to address these challenges is detailed and some of the key issues that must be faced are outlined.
