rule based approach
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Artificial intelligence (AI) is expanding into every niche of human life, organizing our activity, expanding our agency and interacting with us to an exponentially increasing extent. At the same time, AI’s efficiency, complexity and refinement are growing at an accelerating speed. An expanding, ubiquitous intelligence that does not have a means to care about us poses a species-level risk. Justifiably, there is a growing concern with the immediate problem of how to engineer an AI that is aligned with human interests. Computational approaches to the alignment problem currently focus on engineering AI systems to (i) parameterize human values such as harm and flourishing, and (ii) avoid overly drastic solutions, even if these are seemingly optimal. In parallel, ongoing work in applied AI (caregiving, consumer care) is concerned with developing artificial empathy, teaching AI’s to decode human feelings and behavior, and evince appropriate emotional responses.We propose that in the absence of affective empathy (which allows us to share in the states of others), existing approaches to artificial empathy may fail to reliably produce the pro-social, caring component of empathy, potentially resulting in increasingly cognitively complex sociopaths. We adopt the colloquial usage of the term “sociopath” to signify an intelligence possessing cognitive empathy (i.e., the ability to decode, infer, and model the mental and affective states of others), but crucially lacking pro-social, empathic concern arising from shared affect and embodiment. It is widely acknowledged that aversion to causing harm is foundational to the formation of empathy and moral behavior. However, harm aversion is itself predicated on the experience of harm, within the context of the preservation of physical integrity. Following from this, we argue that a “top-down” rule-based approach to achieving caring AI may be inherently unable to anticipate and adapt to the inevitable novel moral/logistical dilemmas faced by an expanding AI. Crucially, it may be more effective to coax caring to emerge from the bottom up, baked into an embodied, vulnerable artificial intelligence with an incentive to preserve its physical integrity. This may be achieved via iterative optimization within a series of tailored environments with incentives and contingencies inspired by the development of empathic concern in humans. Here we attempt an outline of what these training steps might look like. We speculate that work of this kind may allow for AI that surpasses empathic fatigue and the idiosyncrasies, biases, and computational limits that restrict human empathy. While for us, “a single death is a tragedy, a million deaths are a statistic”, the scaleable complexity of AI may allow it to deal proportionately with complex, large-scale ethical dilemmas. Hopefully, by addressing this problem seriously in the early stages of AI’s integration with society, we may one day be accompanied by AI that plans and behaves with a deeply ingrained weight placed on the welfare of others, coupled with the cognitive complexity necessary to understand and solve extraordinary problems.

Machine Translation is best alternative to traditional manual translation. The corpus of Sanskrit literature includes a rich tradition of philosophical and religious texts as well as poetry, music, drama, scientific, technical and other texts. Due to the modernization of tradition and languages, Sanskrit is not on everyone's lips. Translation makes it convenient for users to understand the unknown text. This paper presents a language Machine Translation System from Hindi to Sanskrit and Sanskrit to Hindi using a rule-based technique. We developed a machine translation tool 'anuvaad' which translates Sanskrit prose text into Hindi & vice versa. We also developed bi-lingual corpora to deal with Sanskrit and Hindi grammar rules and text applied rule based method to perform the translation. The experimental results on different 110 examples show that the proposed anuvaad tool achieves overall 93% accuracy for both types of translations. The objective of our work is to ensure confidentiality and multilingual support, which can be tedious and time consuming in case of manual translation.

This paper proposes a new rule-based approach to automated question generation. The proposed approach focuses on the analysis of both sentence syntax and semantic structure. The design and implementation of the proposed approach is also described in detail. Although the primary purpose of a design system is to generate query from sentences, automated evaluation results show that it can also perform great when reading comprehension datasets that focus on question output from paragraphs. With regard to human evaluation, the designed system performs better than all other systems and generates the most natural (human-like) questions. We present a fresh approach to automatic question generation that significantly increases the percentage of acceptable questions compared to prior state-of-the-art systems. In our system, we will take data from various sources for a particular topic and summarize it for the convenience of the people, so that they don't have to go through so multiple sites for relevant data.

Map matching deals with matching GPS coordinates to corresponding points or segments on a road network map. The work has various applications in both vehicle navigating and tracking domains. Traditional rule-based approach for solving the Map matching problem yielded great matching results. However, its performance depends on the underlying algorithm and Mathematical/Statistical models employed in the approach. For example, HMM Map Matching yielded O(N2) time complexity, where N is the number of states in the underlying Hidden Markov Model. Map matching techniques with large order of time complexity are impractical for providing services, especially within time-sensitive applications. This is due to their slow responsiveness and the critical amount of computing power required to obtain the results. This paper proposed a novel data-driven approach for projecting GPS trajectory onto a road network. We constructed a supervised-learning classifier using the Multi-Label Classification (MLC) technique and HMM Map Matching results. Analytically, our approach yields O(N) time complexity, suggesting that the approach has a better running performance when applied to the Map matching-based applications in which the response time is the major concern. In addition, our experimental results indicated that we could achieve Jaccard Similarity index of 0.30 and Overlap Coefficient of 0.70.

This paper considers the problem of extracting geoattributed entities from natural language texts to visualize the spatial relations of geographical objects. For visualization we use the technology of automated generation of schematic maps as subject-oriented components of geographic information systems. The paper describes the information technology that allows extracting geoattributed entities from natural language texts by combining several approaches. These are the neural network approach, the rule-based approach and the approach based on the use of lexico-syntactic patterns for the analysis of natural language texts. For data visualization we propose to use automated geocoding tools in conjunction with the capabilities of modern geographic information systems. The result of this technology is a cartogram that displays the spatial relations of the objects mentioned in the text.

This research work is aimed at bridging the knowledge gap between the most popular knowledge rich English language and the minority Ebira language spoken by the Ebira people, a minority ethnic group in part of Nigeria. Across the globe and on the internet, English language has become the most widely used language for knowledge dissemination. And presently, the majority of the indigenous people of Ebiral and also known as “Anebira” are still not proficient in their use of English language which as a result prevents them from gaining full knowledge disseminated in English language. Hence, the need to develop an automated Machine Translation System capable of translating English text to Ebira text which will help the people to tap from the abundant knowledge conveyed in English language for effective and fast development in their social, political, scientific, philosophical and economic areas of life. The system was designed to consolidate on human translators’ effort and not to replace them. A comprehensive study and analysis of the two languages was carried out with the help of Ebira native speakers in Ebiraland Kogi central and some professional English language tutors at FCE Okene. The knowledge gathered provided the basis for the design and testing of the rule base, inference engine, bilingual dictionary which are important components for the proposed automated system for translation of English text to Ebira text using PHP. Making use of the word in the bilingual dictionary, the system will successfully translate your English text to Ebira. The system was evaluated using one of the popular automatic method of evaluating MT systems BLEU (Bilingual Evaluation Understudy). And an accuracy of 81.5% in translation was achieved. An improved system in the future is recommended to accommodate more complex sentences for the more benefit of the good people of Enebira.

Abstract This article will show how a standardized rule-based approach was used by Petroleum Development Oman (PDO) to shorten the cycle time required to mature the opportunity of implementing waterflood developments in small-to-medium sized satellite oil fields in the South of the Sultanate of Oman. The standardized concept relies on a common development strategy for a portfolio of satellite fields with similar reservoir and fluid characteristics that are still under depletion or in the early stage of waterflood. The targets are early monetization, driving cost efficiency through standardization & replication, and increasing recovery factor through the accelerated implementation of field-wide waterflood. This is achieved by leveraging excess capacity in existing facilities, applying analytical workflows for forecasting, standardizing well design and urban planning, and by applying the learnings and best practices from nearby fields that already have mature developments.