Keysga asoslangan fikrlash va uni akademik metama’lumotlarni avtomatik ekstraksiya qilishda tadbiq qilinishi

Ma’lumki, qaror qabul qilishda, shu jumladan, metama’lumotlarni avtomatik ekstraksiya qilish jarayonida, sun’iy intellekt uslublari, qoidaga asoslangan uslublar, o‘rganishga asoslangan uslublar, tayanch vektorlar uslubi, yashirin Markov modeli, sodda Bayes klassifikatori, evristik uslublar va keysga asoslangan fikrlash uslubidan foydalaniladi. Mazkur maqolada qaror qabul qilishda keysga asoslangan fikrlash, u orqali olinadigan yechimning sifatga ta’sir qiluvchi omillari, keysga asoslangan fikrlashning muammoni yechish uslubi, sharhlovchi keysga asoslangan fikrlash, keysga asoslangan fikrlash va o‘rganish, fikrlashda keyslarni ishlatish uslublari, keyslarni xotiradan chaqirish, taxminiy yechim taklif qilish va uni moslashtirish, taklif qilingan yechimni asoslash, tanqid qilish va baholash, keyslar bazasini yangilash, keysga asoslangan fikrlashning qo‘llanilish imkoniyatlari, keysga asoslangan fikrlashni metama’lumotlarni avtomatik ekstraksiya qilishda qo‘llash va unga asoslangan mavjud tizimlar to‘g‘risida batafsil ma’lumot keltirilgan

Automation Mangrove Identification with Case Based Reasoning Process

Mangroves are ecosystems with unique functions in the environment. Because of its physical properties, mangroves are able to play a role as a wave retardant as well as retaining intrusion and abrasion of the sea. Mangroves themselves have various types of species that are spread throughout Indonesia and not yet widely known to people in general. In identifying the mangrove species itself cannot be done arbitrarily, it requires an expert who truly understands the mangrove species. This research was conducted with the aim of adopting the knowledge of mangrove experts to identify mangrove species into expert systems. The method used is case based reasoning method using the KNN algorithm which is used to calculate the similarity value between cases that will be applied to the expert system to identify mangrove species found in Taman Wisata Alam Pantai Panjang dan Pulau Baai Kota Bengkulu. This system is built using HTML, CSS, Javascript, Php, and Mysql programming languages and is designed using UML diagrams. The results of this study itself are, it has been successfully applied the case based reasoning method in the expert system to identify mangrove species found in Taman Wisata Alam Pantai Panjang dan Pulau Baai Kota Bengkul

Artificial Intelligence Technology and Ecological Transition -Analysis and Criticism-

Artificial intelligence (AI) refers to an application capable of processing tasks which are currently performed satisfactorily by human beings insofar as they involve high-level mental processes such as perceptual learning or the organization of memory (Marvin Lee Minsky, 1956). Until now, research in this field has shown a difficulty in validating and certifying artificial intelligence systems at the service of decarbonization, ecological and energy transition objectives. In this context, this article focuses on an effective analysis of 05 of today’s most popular AI technologies in the field of environment, Artificial Neural Networks, fuzzy logic, Case-based reasoning, the multi-agent system and the process of natural language. The results show that our analysis can be beneficial for developers to select the appropriate technology for a reliable and successful implementation of artificial intelligence

Episodic Memory for Cognitive Robots in Dynamic, Unstructured Environments

Elements from cognitive psychology have been applied in a variety of ways to artificial intelligence. One of the lesser studied areas is in how episodic memory can assist learning in cognitive robots. In this dissertation, we investigate how episodic memories can assist a cognitive robot in learning which behaviours are suited to different contexts. We demonstrate the learning system in a domestic robot designed to assist human occupants of a house. People are generally good at anticipating the intentions of others. When around people that we are familiar with, we can predict what they are likely to do next, based on what we have observed them doing before. Our ability to record and recall different types of events that we know are relevant to those types of events is one reason our cognition is so powerful. For a robot to assist rather than hinder a person, artificial agents too require this functionality. This work makes three main contributions. Since episodic memory requires context, we first propose a novel approach to segmenting a metric map into a collection of rooms and corridors. Our approach is based on identifying critical points on a Generalised Voronoi Diagram and creating regions around these critical points. Our results show state of the art accuracy with 98% precision and 96% recall. Our second contribution is our approach to event recall in episodic memory. We take a novel approach in which events in memory are typed and a unique recall policy is learned for each type of event. These policies are learned incrementally, using only information presented to the agent and without any need to take that agent off line. Ripple Down Rules provide a suitable learning mechanism. Our results show that when trained appropriately we achieve a near perfect recall of episodes that match to an observation. Finally we propose a novel approach to how recall policies are trained. Commonly an RDR policy is trained using a human guide where the instructor has the option to discard information that is irrelevant to the situation. However, we show that by using Inductive Logic Programming it is possible to train a recall policy for a given type of event after only a few observations of that type of event

Qualitative case-based reasoning and learning

The development of autonomous agents that perform tasks with the same dexterity as performed by humans is one of the challenges of artificial intelligence and robotics. This motivates the research on intelligent agents, since the agent must choose the best action in a dynamic environment in order to maximise the final score. In this context, the present paper introduces a novel algorithm for Qualitative Case-Based Reasoning and Learning (QCBRL), which is a case-based reasoning system that uses qualitative spatial representations to retrieve and reuse cases by means of relations between objects in the environment. Combined with reinforcement learning, QCBRL allows the agent to learn new qualitative cases at runtime, without assuming a pre-processing step. In order to avoid cases that do not lead to the maximum performance, QCBRL executes case-base maintenance, excluding these cases and obtaining new (more suitable) ones. Experimental evaluation of QCBRL was conducted in a simulated robot-soccer environment, in a real humanoid-robot environment and on simple tasks in two distinct gridworld domains. Results show that QCBRL outperforms traditional RL methods. As a result of running QCBRL in autonomous soccer matches, the robots performed a higher average number of goals than those obtained when using pure numerical models. In the gridworlds considered, the agent was able to learn optimal and safety policies.Paulo Eduardo Santos acknowledges support from FAPESP-IBM Proc. 2016/18792-9. Ramon Lopez de Mantaras has been partially supported by the Generalitat de Catalunya Grant number 2017 SGR 172, by the CSIC Grant PIE 201750E090, and by the European Project “HumanE-AI-Net” H2020-ICT-48-2020-RIA-952026. Anna Helena Reali Costa acknowledges support from CNPq Proc. 425860/2016-7 and 307027/2017-1. This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brazil (CAPES) - Finance Code 001

A hybrid approach to learn, retrieve, and reuse qualitative cases

The application of Artificial Intelligence methods is becoming indispensable in several domains, for instance in credit card fraud detection, voice recognition, autonomous cars and robotics. However, some methods fail in performances or solving some problems, and hybrid approaches can outperform the results when compared to traditional ones. In this paper we present a hybrid approach, named qualitative case-based reasoning and learning (QCBRL), that integrates three well-known AI methods: Qualitative Spatial Reasoning, Case-Based Reasoning and Reinforcement Learning. QCBRL system was designed to allow an agent to learn, retrieve and reuse qualitative cases in the robot soccer domain. We applied our method in the Half-Field Offense and we have obtained promising results.The authors would like to thank CNPq (grants 311608/2014-0 and 425860/2016-7), FAPESP (grants 2016/21047-3 and 2016/18792-9), Generalitat de Catalunya Research (grants 2014 SGR 118 and CSIC Project 201550E022) and CAPES for their financial supportPeer Reviewe

A Decomposition Framework for Inconsistency Handling in Qualitative Spatial and Temporal Reasoning (Extended Abstract)

International audienceDealing with inconsistency is a central problem in AI, due to the fact that inconsistency can arise for many reasons in real-world applications, such as context dependency, multi-source information, vagueness, noisy data, etc. Among the approaches that are involved in inconsistency handling, we can mention argumentation, non-monotonic reasoning, and paraconsistency, e.g., see [Philippe Besnard and Anthony Hunter, 2008; Gerhard Brewka et al., 1997; Koji Tanaka et al., 2013]. In the work of [Yakoub Salhi and Michael Sioutis, 2023], we are interested in dealing with inconsistency in the context of Qualitative Spatio-Temporal Reasoning (QSTR) [Ligozat, 2013]. QSTR is an AI framework that aims to mimic, natural, human-like representation and reasoning regarding space and time. This framework is applied to a variety of domains, such as qualitative case-based reasoning and learning [Thiago Pedro Donadon Homem et al., 2020] and visual sensemaking [Jakob Suchan et al., 2021]; the interested reader is referred to [Michael Sioutis and Diedrich Wolter, 2021] for a recent survey. Motivation. In [Yakoub Salhi and Michael Sioutis, 2023], we study the decomposition of an inconsistent constraint network into consistent subnetworks under, possible, mandatory constraints. To illustrate the interest of such a decomposition, we provide a simple example described in Figure 1. The QCN depicted in the top part of the figure corresponds to a description of an inconsistent plan. Further, we assume that the constraint Task A {before} Task B is mandatory. To handle inconsistency, this plan can be transformed into a decomposition of two consistent plans, depicted in the bottom part of the figure; this decomposition can be used, e.g., to capture the fact that Task C must be performed twice. More generally, network decomposition can be involved in inconsistency handling in several ways: it can be used to identify potential contexts that explain the presence of inconsistent information; it can also be used to restore consistency through a compromise between the components of a decomposition, e.g., by using belief merging [Jean-François Condotta et al., 2010]; in addition, QCN decomposition can be used as the basis for defining inconsistency measures. Contributions. We summarize the contributions of [Yakoub Salhi and Michael Sioutis, 2023] as follows. First, we propose a theoretical study of a problem that consists in decomposing an inconsistent QCN into a bounded number of consistent QCNs that may satisfy a specified part in the original QCN; intuitively, the required common part corresponds to the constraints that are considered necessary, if any. To this end, we provide upper bounds for the minimum number of components in a decomposition as well as computational complexity results. Secondly, we provide two methods for solving our decomposition problem. The first method corresponds to a greedy constraint-based algorithm, a variant of which involves the use of spanning trees; the basic idea of this variant is that any acyclic constraint graph in QSTR is consistent, and such a graph can be used as a starting point for building consistent components. The second method corresponds to a SAT-based encoding; every model of this encoding is used to construct a valid decomposition. Thirdly, we consider two optimization versions of the initial decomposition problem that focus on minimizing the number of components and maximizing the similarity between components, respectively. The similarity between two QCNs is quantified by the number of common non-universal constraints; the interest in maximizing the similarity lies mainly in the fact that it reduces the number of constraints that allow each component to be distinguished from the rest. Of course, our previous methods are adapted to tackle these optimization versions, too. Additionally, we introduce two inconsistency measures based on QCN decomposition, which can be seen as counterparts of measures for propositional KBs introduced in [Matthias Thimm, 2016; Meriem Ammoura et al., 2017], and show that they satisfy several desired properties in the literature. Finally, we provide implementations of our methods for computing decompositions and experimentally evaluate them using different metrics