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Combining Rule-based reasoning and Case-based reasoning has been widely used, exhibiting quite successful results, since they have complementary capabilities. A system that can utilize both approaches could potentially take advantage of the positive aspects of both while minimizing their negative aspects. In this paper, we propose a hybrid reasoner which combines Rule based reasoning, Case-based reasoning, as well as Process Mining. To our knowledge, this work is the first one that incorporates process modeling in a hybrid reasoner. Process Mining is used in order to take into consideration possible future facts, which are justified by the current ones through a process model extracted by the cases themselves. Each case is considered as an instance of a process and a process model is extracted by applying process mining techniques. We also present PAVEFS, an intelligent system based on the proposed hybrid reasoner, supporting personalized provision of services for individuals facing autism spectrum disorders. [ABSTRACT FROM AUTHOR]

The rehabilitation robot can assist hemiplegic patients to complete the training program effectively, but it only focuses on helping the patient’s training process and requires the rehabilitation therapists to manually adjust the training parameters according to the patient’s condition. Therefore, there is an urgent need for intelligent training prescription research of rehabilitation robots to promote the clinical applications. This study proposed a decision support system for the training of upper limb rehabilitation robot based on hybrid reasoning with rule-based reasoning (RBR) and case-based reasoning (CBR). The expert knowledge base of this system is established base on 10 professional rehabilitation therapists from three different rehabilitation departments in Shanghai who are enriched with experiences in using desktop-based upper limb rehabilitation robot. The rule-based reasoning is chosen to construct the cycle plan inference model, which develops a 21-day training plan for the patients. The case base consists of historical case data from 54 stroke patients who underwent rehabilitation training with a desktop-based upper limb rehabilitation robot. The case-based reasoning, combined with a Random Forest optimized algorithm, was constructed to adjust the training parameters for the patients in real-time. The system recommended a rehabilitation training program with an average accuracy of 91.5%, an average AUC value of 0.924, an average recall rate of 88.7%, and an average F1 score of 90.1%. The application of this system in rehabilitation robot would be useful for therapists.

Farmed fish disease diagnosis is an important problem in the fish farming industry, affecting quality of production and financial losses. In this paper, we present a web-based intelligent system that tackles the problem of fish disease diagnosis. To this end, it uses multiple knowledge representation and reasoning methods: rule-based, case-based, weight-based, and voting. Knowledge, which concerns the diagnosis of sea bass diseases, was acquired from experts in the field and represented in the form of decision trees. The diagnostic process is performed in two stages: a general one and a specialized one. In the general stage, a level-based diagnosis is performed, where environmental parameters, external signs, and internal signs are successively examined, and the three most probable diseases are identified. In the specialized stage, which is optional, a specialized expert system is used for each of the resulting diseases, where additional parameters concerning laboratory tests (microbiological, microscopic, molecular, and chemical) are considered. The general stage is the most useful, given that it can be performed on-site in real-time, whereas the specialized one requires time-consuming lab tests. The system also provides explanations for its decisions. Evaluation of the general-stage diagnostic process showed a top-3 accuracy of 78.79% on expert test cases and 94% on an artificial dataset. [ABSTRACT FROM AUTHOR]

Over the last decades, the development of Answer Set Programming (ASP) has brought about an expressive modeling language powered by highly performant systems. At the same time, it gets more and more difficult to provide semantic underpinnings capturing the resulting constructs and inferences. This is even more severe when it comes to hybrid ASP languages and systems that are often needed to handle real-world applications. We address this challenge and introduce the concept of abstract and structured theories that allow us to formally elaborate upon their integration with ASP. We then use this concept to make the semantic characterization of clingo's theory-reasoning framework precise. This provides us with a formal framework in which we can elaborate upon the formal properties of existing hybridizations of clingo, such as clingcon, clingo[dl], and clingo[lp]. [ABSTRACT FROM AUTHOR]

A warehouse delivery problem consists of a set of robots that undertake delivery jobs within a warehouse. Items are moved around the warehouse in response to events. A solution to a warehouse delivery problem is a collision-free schedule of robot movements and actions that ensures that all delivery jobs are completed and each robot is returned to its docking station. While the warehouse delivery problem is related to existing research, such as the study of multi-agent path finding (MAPF), the specific industrial requirements necessitated a novel approach that diverges from these other approaches. For example, our problem description was more suited to formalizing the warehouse in terms of a weighted directed graph rather than the more common grid-based formalization. We formalize and encode the warehouse delivery problem in Answer Set Programming (ASP) extended with difference constraints. We systematically develop and study different encoding variants, with a view to computing good quality solutions in near real-time. In particular, application specific criteria are contrasted against the traditional notion of makespan minimization as a measure of solution quality. The encoding is tested against both crafted and industry data and experiments run using the Hybrid ASP solver clingo[dl]. [ABSTRACT FROM AUTHOR]

Farmed fish disease diagnosis is an important problem in the fish farming industry, affecting quality of production and financial losses. In this paper, we present a web-based intelligent system that tackles the problem of fish disease diagnosis. To this end, it uses multiple knowledge representation and reasoning methods: rule-based, case-based, weight-based, and voting. Knowledge, which concerns the diagnosis of sea bass diseases, was acquired from experts in the field and represented in the form of decision trees. The diagnostic process is performed in two stages: a general one and a specialized one. In the general stage, a level-based diagnosis is performed, where environmental parameters, external signs, and internal signs are successively examined, and the three most probable diseases are identified. In the specialized stage, which is optional, a specialized expert system is used for each of the resulting diseases, where additional parameters concerning laboratory tests (microbiological, microscopic, molecular, and chemical) are considered. The general stage is the most useful, given that it can be performed on-site in real-time, whereas the specialized one requires time-consuming lab tests. The system also provides explanations for its decisions. Evaluation of the general-stage diagnostic process showed a top-3 accuracy of 78.79% on expert test cases and 94% on an artificial dataset.

Several methods have been proposed in the last two decades to recognize human activities based on sensor data acquired in smart-homes. While most existing methods assume the presence of a single inhabitant, a few techniques tackle the challenging issue of multi-resident activity recognition. To the best of our knowledge, all existing methods for multi-inhabitant activity recognition require the acquisition of a labeled training set of activities and sensor events. Unfortunately, activity labeling is costly and may disrupt the users' privacy. In this article, we introduce a novel technique to recognize multi-inhabitant activities without the need of labeled datasets. Our technique relies on an unlabeled sensor data stream acquired from a single resident, and on ontological reasoning to extract probabilistic associations among sensor events and activities. Extensive experiments with a large dataset of multi-inhabitant activities show that our technique achieves an average accuracy very close to the one of state-of-the-art supervised methods, without requiring the acquisition of labeled data.

Recently, there has been a big progress in developing artificial deep-learning neural networks and large-scale knowledge graphs. However, the results in these two research fields have serious drawbacks. The solutions offered by neural networks remain unstable and prone to adversarial attacks: while the percentage of correct answers increases, the incorrect answers often contain glaring errors. Large knowledge graphs contain a lot of facts but little knowledge; they are mostly used for information search and retrieval. The ability to reason conclusions on them is limited, and the majority of modern research turns to approximate methods like neural networks and graph embeddings to draw conclusions and use the accumulated knowledge. In this work, I propose a hybrid cognitive architecture inspired by the observable features of human thinking. Pruning obviously wrong solutions seems to be more natural for human symbolic reasoning than making far-fetched strict logical conclusions, while generating new ideas is often intuitive. So a hybrid cognitive architecture can employ generative neural networks as a sort of "intuition" (generating possible solutions) and symbolic inference as a control contour to verify and filter the generated solutions, weeding out dangerous and wrong ideas. This requires creating knowledge graphs containing negative information: the information of what cannot happen or should not be done and why. The problems of creating negative knowledge graphs are discussed. Hybrid cognitive systems using the proposed architecture will be a lot more trustworthy as they will have a system of human-verifiable rules that ensures avoiding the worst errors which can be used in many fields from decision making to natural-language parsing. [ABSTRACT FROM AUTHOR]

For planning an assembly of a product from a given set of parts, robots necessitate certain cognitive skills: high-level planning is needed to decide the order of actuation actions, while geometric reasoning is needed to check the feasibility of these actions. For collaborative assembly tasks with humans, robots require further cognitive capabilities, such as commonsense reasoning, sensing, and communication skills, not only to cope with the uncertainty caused by incomplete knowledge about the humans' behaviors but also to ensure safer collaborations. We propose a novel method for collaborative assembly planning under uncertainty, that utilizes hybrid conditional planning extended with commonsense reasoning and a rich set of communication actions for collaborative tasks. Our method is based on answer set programming. We show the applicability of our approach in a real-world assembly domain, where a bi-manual Baxter robot collaborates with a human teammate to assemble furniture. [ABSTRACT FROM AUTHOR]

Large-scale disasters pose significant response challenges for all governmental organizations and the general public. Several difficulties usually occur during the response efforts, making it important for the authorities to take timely key decisions to mitigate and recover from disastrous or emergency situations. We herein present an ontology-supported hybrid reasoning model by integrating case-based reasoning and rule-based reasoning with implementation support for decision-makers to effectively respond in case of emergencies. We also introduce a new hierarchically organized semantic knowledge representation model to represent the case base structure that enhances case-based reasoning to knowledge-intensive case-based reasoning. In addition, we obtain experimental results on the analysis of the proposed approach in terms of the efficiency of the decision support system. Hence, it seems reasonable to merge the advantages of both approaches using a hybrid model of knowledge representation. The model output presents an estimation of the number of resources to be deployed if an emergency occurs. The proposed approaches for both the knowledge representation structure and the inference algorithm have proved to improve the accuracy of recommendation in emergencies. The results show that our hybrid system approach is efficient in decision support. The ontology-supported hybrid reasoning approach is also further validated using subjective evaluation. [ABSTRACT FROM AUTHOR]

Knowledge graphs (KGs) contain rich resources that represent human knowledge in the world. There are mainly two kinds of reasoning techniques in knowledge graphs, symbolic reasoning and statistical reasoning. However, both of them have their merits and limitations. Therefore, it is desirable to combine them to provide hybrid reasoning in a knowledge graph. In this paper, we present the first work on the survey of methods for hybrid reasoning in knowledge graphs. We categorize existing methods based on applications of reasoning techniques, and introduce the key ideas of them. Finally, we re-examine the remaining research problems to be solved and provide an outlook to future directions for hybrid reasoning in knowledge graphs. [ABSTRACT FROM AUTHOR]

Accurate activity recognition plays a major role in smart homes to provide assistance and support for users, especially elderly and cognitively impaired people. To realize this task, knowledge-driven approaches are one of the emerging research areas that have shown interesting advantages and features. However, several limitations have been associated with these approaches. The produced models are usually incomplete to capture all types of human activities. This resulted in the limited ability to accurately infer users' activities. This paper presents an alternative approach by combining knowledge-driven with data-driven reasoning to allow activity models to evolve and adapt automatically based on users' particularities. Firstly, a knowledge-driven reasoning is presented for inferring an initial activity model. The model is then trained using data-driven techniques to produce a dynamic activity model that learns users' varying action. This approach has been evaluated using a publicly available dataset and the experimental results show the learned activity model yields significantly higher recognition rates compared to the initial activity model. [ABSTRACT FROM AUTHOR]

Diagnostic tests are used to determine anomalies in complex systems such as organisms or built structures. Once a set of tests is performed, the experts interpret their results and make decisions based on them. This process is named diagnostic reasoning. In diagnostic reasoning a decision is established by using both rules and general knowledge on the tests and the domain. The artificial intelligence community has focused on devising and automating different methods of diagnosis for medicine and engineering, but, to the best of our knowledge, the decision process in logical terms has not yet been investigated thoroughly. The automation of the diagnostic process would be helpful in a number of contexts, in particular when the number of test sets to make decision is too wide to be dealt with manually. To tackle such challenges, we shall study logical frameworks for diagnostic reasoning, automation methods and their computational properties and technologies implementing these methods. In this paper, we present the formalization of a hybrid reasoning framework TL that hosts tests and deduction rules on tests, and an algorithm that transforms a TL theory into defeasible logic, for which an implemented automated deduction technology (called Spindle) exists. We evaluate the methodology by means of a real-world example related to the Open Web Application Security Project requisites. The full diagnostic process is driven from the definition of the issue to the decision. [ABSTRACT FROM AUTHOR]

Brain tissue segmentation is one of the most important parts of clinical diagnostic tools. Fuzzy C-mean (FCM) is one of the most popular clustering based segmentation methods. However FCM does not robust against noise and artifacts such as partial volume effect (PVE) and inhomogeneity. In this paper, a new approach for robust brain tissue segmentation is described. The proposed method quantifies the volumes of white matter (WM), gray matter (GM) and cerebrospinal fluid(CSF) tissues using hybrid clustering process which based on: (1) FCM algorithm to get the initial center partition. (2) Genetic algorithms (GA) to achieve optimization and to determine the appropriate cluster centers and the fuzzy corresponding partition matrix. (3) Possibilistic C-Means (PCM) algorithm for volumetric measurements of WM, GM, and CSF brain tissues. (4) Rule of the possibility maximum to compute the labeled image in decision step. The experiments were realized using different real and synthetic brain images from patients with Alzheimer’s disease. We used Tanimoto coefficient, sensitivity and specificity validity indexes to validate the proposed hybrid approach and we compared the performance with several competing methods namely FCM and PCM algorithms. Good result was achieved which demonstrates the efficiency of proposed clustering approach and that it can outperforms competing methods especially in the presence of PVE and when the noise and spatial intensity inhomogeneity are high.

Abstract Recent developments in Information and Communication Technologies (ICT) and online healthcare services have created a huge volume of health data. With the advancements in machine learning approaches, the research on Disease Prediction Support System (DPSS) has attracted many researchers globally. In this article, we present a hybrid reasoning-based methodology on predicting diseases. The combinatorial advantage of Fuzzy sety theory, k-nearest neighbor and case-based reasoning helps to yield enhanced prediction results. Though DPSS facilitates promising healthcare services, data security and privacy are still crucial challenging issues to be addressed. The DPSS is extended as a Privacy-Aware Disease Prediction Support System (PDPSS) using Paillier Homomorphic Encryption to preserve patients' sensitive information from unauthorized user access. The proposed prediction model is evaluated with the statistical evaluation metrics, and the experimental results reveal the improved performance of PDPSS in enhanced prediction accuracy and better security. [ABSTRACT FROM AUTHOR]

Abstract Materials failure problems are becoming a serious concern because there would be a variety of consequences possibly affecting public safety. Therefore, materials failure analysis is needed to find out the reason of failure and avoid similar failures' reoccurring. However, materials failure cases mostly rely on manual analysis by experts with sufficient domain knowledge, leading to the situation of time-consuming, low efficiency and difficult evaluation. This paper proposes a rule-case based hybrid reasoning approach for materials failure analysis with the aid of ontology. Hundreds of materials failure cases from different industries were collected and analyzed by rule-based reasoning, case-based reasoning and our hybrid method. It is demonstrated that the rule-case based hybrid reasoning method can obviously provide better analysis results in comparison with rule-based reasoning and case-based reasoning alone. Highlights • AI-based method is introduced into materials failure case analysis. • A hybrid reasoning method combines the advantages of rule-based and case-based reasoning and gives the best result. • With enough failure cases and computable rules, the method can reduce manual failure analysis work of domain experts. [ABSTRACT FROM AUTHOR]

We consider cognitive factories with multiple teams of heterogenous robots, and address two key challenges of these domains, hybrid reasoning for each team and finding an optimal global plan (with minimum makespan) for multiple teams. For hybrid reasoning, we propose modeling each team's workspace taking into account capabilities of heterogeneous robots, embedding continuous external computations into discrete symbolic representation and reasoning, not only optimizing the makespans of local plans but also minimizing the total cost of robotic actions. To find an optimal global plan, we propose a semi-distributed approach that does not require exchange of information between teams but yet achieves on an optimal coordination of teams that can help each other. We prove that the optimal coordination problem is NP-complete, and describe a solution using automated reasoners. We experimentally evaluate our methods, and show their applications on a cognitive factory with dynamic simulations and a physical implementation. [ABSTRACT FROM AUTHOR]

Due to the urgent need for more flexible and intelligent response to rapid changing customer demand in highly competitive market environment, intelligent optimization of the cutting process parameters based on the cutting process cases were researched, and an expert system (ES) of the green cutting process technology was constructed. Its processor was performed by hybrid reasoning method of cases and rules while part model and process model of the workpiece to be manufactured were carried out. The mathematical model of machine tool energy consumption was given using response surface methodology (RSM) based on experimental data collected from the machining tool manufacturing enterprise. Based on the case library, the basic database, the model library and the rule library, the part model and the process model of the typical machine tool parts were established, thus those formed a knowledge base. The cutting process technology of the head spindle on certain machine tool was optimized as one instance, the process of similar calculation and the optimization procedure was given. The optimization process technology was verified, and it proved the feasibility and applicability of the ES. Those provided one optimization plan and program of green processing technology for the sustainable manufacturing enterprises. The ES of the green cutting process technology could help the technologist of enterprises more rapidly respond to the demands of customers in highly competitive market environment and relieve the impact to the environment. [ABSTRACT FROM AUTHOR]

This article is concerned with the role empirical research can play in normative practical ethics. There is no doubt that ethical research requires some kind of collaboration between normative disciplines and empirical sciences. But many researchers hold that empirical science is only assigned a subordinate role, due to the doctrine that normative conclusions cannot be justified by descriptive premises. Scientists working in the field of ethics commonly hold, however, that the empirical sciences should play a much bigger role in ethical research. The aim of this paper is to show that empirical sciences can play a substantive role in normative ethics, with no illicit inferences from is to ought. To achieve this aim, I explain (in "The Is-Ought Problem Explained" section) Hume's thesis. In the following sections, I am concerned with different uses of empirical data that do not imply an illicit inference from descriptive premises to a normative conclusion. The article demonstrates that there are many modes of ethical reasoning that allow for a substantive use of empirical data, and it also shows the importance of Hume's thesis for clarifying ethical reasoning. [ABSTRACT FROM AUTHOR]

Planners for real robotic systems should not only reason about abstract actions, but also about aspects related to physical execution such as kinematics and geometry. We present an approach to hybrid task and motion planning, in which state-based forward-chaining task planning is tightly coupled with motion planning and other forms of geometric reasoning. Our approach is centered around the problem of geometric backtracking that arises in hybrid task and motion planning: in order to satisfy the geometric preconditions of the current action, a planner may need to reconsider geometric choices, such as grasps and poses, that were made for previous actions. Geometric backtracking is a necessary condition for completeness, but it may lead to a dramatic computational explosion due to the large size of the space of geometric states. We explore two avenues to deal with this issue: the use of heuristics based on different geometric conditions to guide the search, and the use of geometric constraints to prune the search space. We empirically evaluate these different approaches, and demonstrate that they improve the performance of hybrid task and motion planning. We demonstrate our hybrid planning approach in two domains: a real, humanoid robotic platform, the DLR Justin robot, performing object manipulation tasks; and a simulated autonomous forklift operating in a warehouse. [ABSTRACT FROM AUTHOR]

Logic-based approaches to legal problem solving model the rule-governed nature of legal argumentation, justification, and other legal discourse but suffer from two key obstacles: the absence of efficient, scalable techniques for creating authoritative representations of legal texts as logical expressions; and the difficulty of evaluating legal terms and concepts in terms of the language of ordinary discourse. Data-centric techniques can be used to finesse the challenges of formalizing legal rules and matching legal predicates with the language of ordinary parlance by exploiting knowledge latent in legal corpora. However, these techniques typically are opaque and unable to support the rule-governed discourse needed for persuasive argumentation and justification. This paper distinguishes representative legal tasks to which each approach appears to be particularly well suited and proposes a hybrid model that exploits the complementarity of each. [ABSTRACT FROM AUTHOR]

Abstract: Although the OWL (Web Ontology Language) and SWRL (Semantic Web Rule Language) add considerable expressiveness to the Semantic Web they do have expressive limitations. For some reasoning problems it is necessary to infer the existence of new individuals satisfying stated condition. This kind of problems can not be fully resolved by OWL and SWRL. We present the XSWRL (Extended Semantic Web Rule Language), an extension to SWRL, to overcome these problems. XSWRL introduces existentially quantified variables to rules. XSWRL extends SWRL in a syntactically and semantically coherent manner. We show that while the expressiveness of the Semantic Web language is improved, the undecidability and infinite chains are brought on. We define the inseparable set of atoms to separate XSWRL rules, and discuss a prototype implementation of reasoning support for XSWRL. [ABSTRACT FROM AUTHOR]

In this paper, the components of a high-speed cutting system are analyzed firstly. The component variables of the high-speed cutting system are classified into four types: uncontrolled variables, process variables, control variables, and output variables. The relationships and interactions of these variables are discussed. Then, by analyzing and comparing intelligent reasoning methods frequently used, the hybrid reasoning is employed to build the high-speed cutting database system. Then, the data structures of high-speed cutting case base and databases are determined. Finally, the component parts and working process of the high-speed cutting database system on the basis of hybrid reasoning are presented. [ABSTRACT FROM AUTHOR]

Fixture design is a complex and intuitive process. An efficient fixture design system is essential to cut costs and reduce product lead time. For networked manufacturing, the fixture design system must contain characteristics of rapid configuration designing, three-dimensional (3D) modelling, a completely standardized elements database, and it should be able to transfer information with various other systems. For the requirement of rapid configuration design, based on the analysis of the contents and characteristics of fixture design, a hybrid CBR/KBR (case-based reasoning/knowledge-based reasoning) fixture design method is proposed, with CBR as its core, KBR as its assistant and CBR as the support of case-adaptation process. Using design information, the designer can obtain a satisfactory subset of possible configuration designs of a fixture from the KBR process. Then the designer, using this subset and other information (via FIXCOD system), can obtain a similar design of the fixture from the retrieving case database, and then finally use the CBR adaptation technique to obtain the satisfied design of fixture. Networked information releasing technology is applied to part design in the computer aided design (CAD) system, and a new 3D modelling method is presented, driven by networked parameters. The data on the web are integrated into the CAD system to realize multithreading data exchange between the web information releasing system and the CAD system, which will make the CAD system obtain data from the web. Then networked 3D modelling will be realized by the driving of parameters from the web, and a proved model based on CATIA is provided. The internet and the use of extensible mark-up language (XML) as a file format provide a means for the transfer of information and knowledge between the various computer aided manufacturing (CAM) systems. The system has been implemented using Java and is based on a three-tier browser/server (B/S) architecture. This ensures the platform independent performance of the system. [ABSTRACT FROM AUTHOR]

In this paper, we present an approach that integrates symbolic rules, neural networks and cases. To achieve it, we integrate a kind of hybrid rules, called neurules, with cases. Neurules integrate symbolic rules with the Adaline neural unit. In the integration, neurules are used to index cases representing their exceptions. In this way, the accuracy of the neurules is improved. On the other hand, due to neurule-based efficient inference mechanism, conclusions can be reached more efficiently. In addition, neurule-based inferences can be performed even if some of the inputs are unknown, in contrast to symbolic rule-based inferences. Furthermore, an existing symbolic rule-base with indexed exception cases can be converted into a neurule-base with corresponding indexed exception cases. Finally, empirical data can be used as a knowledge source, which facilitates knowledge acquisition. We also present a new high-level categorization of the approaches integrating rule-based and case-based reasoning. [Copyright &y& Elsevier]

We present an extension to DL-based taxonomic reasoning by means of inference fusion, i.e. the dynamic combination of inferences from distributed heterogeneous reasoners. Our approach integrates results from a DL-based system with results from a constraint solver under the direction of a global reasoning coordinator. Inference fusion is performed by (i) processing heterogeneous input knowledge, producing suitable homogeneous input knowledge for each specialised reasoner; (ii) activating each reasoner when necessary, collecting its results and passing them to the other reasoner if appropriate; (iii) combining the results of the two reasoners. We discuss the benefits of our approach and demonstrate our ideas by proposing a language (DL(D)/S) and a reasoning system (Concor) which uses knowledge bases written in DL(D)/S and supports hybrid reasoning. We illustrate our ideas with an example. [Copyright &y& Elsevier]

Activity recognition on mobile device sensor data has been an active research area in mobile and pervasive computing for several years. While the majority of the proposed techniques are based on supervised learning, semi-supervised approaches are being considered to reduce the size of the training set required to initialize the model. These approaches usually apply self-training or active learning to incrementally refine the model, but their effectiveness seems to be limited to a restricted set of physical activities. We claim that the context which surrounds the user (e.g., time, location, proximity to transportation routes) combined with common knowledge about the relationship between context and human activities could be effective in significantly increasing the set of recognized activities including those that are difficult to discriminate only considering inertial sensors, and the highly context-dependent ones. In this paper, we propose CAVIAR, a novel hybrid semi-supervised and knowledge-based system for real-time activity recognition. Our method applies semantic reasoning on context-data to refine the predictions of an incremental classifier. The recognition model is continuously updated using active learning. Results on a real dataset obtained from 26 subjects show the effectiveness of our approach in increasing the recognition rate, extending the number of recognizable activities and, most importantly, reducing the number of queries triggered by active learning. In order to evaluate the impact of context reasoning, we also compare CAVIAR with a purely statistical version, considering features computed on context-data as part of the machine learning process. • We propose a novel context-aware and semi-supervised activity recognition method. • Results show that context reasoning improves semi-supervised activity recognition. • We show that knowledge-based reasoning outperforms purely data-driven approaches. [ABSTRACT FROM AUTHOR]