Artificial Intelligence - Scope and Limitations最新文献

{"title":"Team Exploration of Environments Using Stochastic Local Search","authors":"Ramoni O. Lasisi, R. Dupont","doi":"10.5772/INTECHOPEN.81902","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.81902","url":null,"abstract":"We investigate the use of Stochastic Local Search (SLS) technique to explore environments where agents ’ knowledge and the time to explore such environments are limited. We extend a work that uses evolutionary algorithms to evolve teams in simulated environments. Our work proposes a formalization of the concept of state and neighborhood for SLS and provides evaluation of agents ’ teams using number of interesting cells. Further, we modify the environments to include goals that are randomly distributed among interesting cells. Agents in this case are then required to search for goals. Experiments using teams of different sizes show the effectiveness of our technique. Teams were able to complete exploration of more than 70% of the environments, while in the best cases , they were able to complete explorations of more than 80% of the environments within limited time steps. These results compare with those of the previous work. It is interesting to note that all teams of agents were able to find on average all the goals in the three environments when the size of the grid is 12. This is a 100% achievement by the agents ’ teams. However, performance can be seen to degrade as the environments ’ sizes become larger.","PeriodicalId":170040,"journal":{"name":"Artificial Intelligence - Scope and Limitations","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123337207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Introductory Chapter: Artificial Intelligence - Challenges and Applications","authors":"D. G. Harkut, K. Kasat","doi":"10.5772/INTECHOPEN.84624","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.84624","url":null,"abstract":"Artificial intelligence (AI) is any task performed by program or machine, which otherwise human needs to apply intelligence to accomplish it. It is the science and engineering of making machines to demonstrate intelligence especially visual perception, speech recognition, decision-making, and translation between languages like human beings. AI is the simulation of human intelligence processes by machines, especially computer systems. This includes learning, reasoning, planning, self-correction, problem solving, knowledge representation, perception, motion, manipulation, and creativity. It is a science and a set of computational techniques that are inspired by the way in which human beings use their nervous system and their body to feel, learn, reason, and act. AI is related to machine learning and deep learning wherein machine learning makes use of algorithms to discover patterns and generate insights from the data they are working on. Deep learning is a subset of machine learning, one that brings AI closer to the goal of enabling machines to think and work as human as possible. AI is a debatable topic and is often represented in a negative way; some would call it a blessing in disguise for businesses, while for some it is a technology that endangers the mere existence of humankind as it is potentially capable of taking over and dominating human being, but in reality artificial intelligence has affected our lifestyle either directly or indirectly and shaping the future of tomorrow. AI has already become an intrinsic part of our daily life and has greatly impacted our lifestyle despite the imperative uses of digital assistants of mobile phones, driverassistance systems, the bots, texts and speech translators, and systems that assist in recommending products and services and customized learning. Every emerging technology is a source of both enthusiasm and skepticism. AI is a source of both advantages and disadvantages in different perspectives. However, we need to overcome certain challenges before we can realize the true potential and immense transformational capabilities of this emerging technology. Some of the challenges related to artificial intelligence are:","PeriodicalId":170040,"journal":{"name":"Artificial Intelligence - Scope and Limitations","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131312055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Information and Communication Systems Including Artificial Intelligence and Big Data as Objects of International Legal Protection","authors":"V. P. Talimonchik","doi":"10.5772/INTECHOPEN.83565","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.83565","url":null,"abstract":"The objective of this study is identifying prospective for international legal protection of information and communication systems including artificial intelligence on the universal and regional levels, and analysis of legal instruments for protection of artificial intelligence and Big Data in the context of regulation of relations in the global information society. A complex of general scientific and philosophical methods, including the logical, comparative-legal, formal-legal, systemic-structural, and problematic-theoretical methods, as well as methods of analysis and synthesis, gen-eralization and description were used in the research. It was found that the existing international agreements in the field of intellectual property protection take no account of the particular features of protection of complex objects. Complex objects comprise information and communication systems including artificial intelligence and Big Data. There is an objective necessity to establish a legal regime for complex objects on the universal level. The findings can be used in activities of international organizations in execution of their functions of unification and harmonization of the international information law.","PeriodicalId":170040,"journal":{"name":"Artificial Intelligence - Scope and Limitations","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121294347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intention to Use WhatsApp","authors":"Cristóbal Fernández-Robin, Diego Yáñez, Scott McCoy","doi":"10.5772/INTECHOPEN.81999","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.81999","url":null,"abstract":"More than 1.8 billion people use WhatsApp nowadays, out of which 70% uses it daily. In this scenario, this study seeks modeling the variables that positively influence the intention to use WhatsApp. To this end, 579 surveys based on the unified theory of acceptance and use of technology are conducted. The descriptive results show that individuals use WhatsApp mainly motivated by leisure. In this sense, according to the structural equation model, the variable with the greatest influence on behavioral intention is hedonic motivation, followed by social influence, performance expectancy, and effort expectancy. These results indicate that most people use WhatsApp principally because they find it fun, enjoyable, and very entertaining, something more inherent to an entertainment application than to a messaging application. Nevertheless, a cluster analysis indicates the existence of two consumer segments: one showing a certain indifference and disagreement regarding the usefulness of WhatsApp for their activities and duties and the other manifesting that it uses WhatsApp not only for leisure but also for work, academic, and informative reasons. These differences in consumer drivers might have a great impact on WhatsApp and its competition marketing strategies.","PeriodicalId":170040,"journal":{"name":"Artificial Intelligence - Scope and Limitations","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114464482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of Cancer Patient Outcomes Based on Artificial Intelligence","authors":"Suk Lee, E. Ju, Suk Woo Choi, Hyungju Lee, J. Shim, K. Chang, Kwang Hyeon Kim, C. Kim","doi":"10.5772/INTECHOPEN.81872","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.81872","url":null,"abstract":"Knowledge-based outcome predictions are common before radiotherapy. Because there are various treatment techniques, numerous factors must be considered in predicting cancer patient outcomes. As expectations surrounding personalized radiotherapy using complex data have increased, studies on outcome predictions using artificial intelligence have also increased. Representative artificial intelligence techniques used to predict the outcomes of cancer patients in the field of radiation oncology include collecting and processing big data, text mining of clinical literature, and machine learning for implementing prediction models. Here, methods of data preparation and model construction to predict rates of survival and toxicity using artificial intelligence are described.","PeriodicalId":170040,"journal":{"name":"Artificial Intelligence - Scope and Limitations","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131809819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}