Program Book - Artificial Intelligence Association of Thailand (AIAT)
... This volume contains the papers presented at the 14th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2016) held during August 22-26, 2016 in Phuket, Thailand. PRICAI is a biennial conference inaugurated in Tokyo in 1990. It provides a common forum for researchers and practit ...
... This volume contains the papers presented at the 14th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2016) held during August 22-26, 2016 in Phuket, Thailand. PRICAI is a biennial conference inaugurated in Tokyo in 1990. It provides a common forum for researchers and practit ...
Here - School of Computer Science, University of Birmingham.
... increasingly higher quality [16]–[20]. In coevolutionary learning, games have played an important role not only in the development of various coevolutionary algorithms, but also in understanding its approach to problem solving. In particular, games provide a natural framework to study coevolutionary ...
... increasingly higher quality [16]–[20]. In coevolutionary learning, games have played an important role not only in the development of various coevolutionary algorithms, but also in understanding its approach to problem solving. In particular, games provide a natural framework to study coevolutionary ...
AAAI 2017 Conference Program
... not watchful. Whether your opinion sits on one side or the other, the fact remains; robots have already become a part of our society. In particular, with recent advances in robotics, therapeutic interventions using robots is now ideally positioned to make an impact. There are numerous challenges tho ...
... not watchful. Whether your opinion sits on one side or the other, the fact remains; robots have already become a part of our society. In particular, with recent advances in robotics, therapeutic interventions using robots is now ideally positioned to make an impact. There are numerous challenges tho ...
without teaching statement
... [57] R. Ramanujan, A. Sabharwal, and B. Selman. On adversarial search spaces and sampling-based planning, 2010. Under review. [58] B. Dilkina, C. P. Gomes, and A. Sabharwal. Backdoors in the context of dynamic sub-solvers and learning. Annals of Mathematics and Artificial Intelligence, 2010. Under r ...
... [57] R. Ramanujan, A. Sabharwal, and B. Selman. On adversarial search spaces and sampling-based planning, 2010. Under review. [58] B. Dilkina, C. P. Gomes, and A. Sabharwal. Backdoors in the context of dynamic sub-solvers and learning. Annals of Mathematics and Artificial Intelligence, 2010. Under r ...
INTELLIGENT REASONING ON NATURAL
... deal with the mind problem and to create an artificial mind; and then, understanding mind will help the researchers to create an artificial mind. These make the strong-AI field improve incrementally and slowly, just as the human mind does. Thrun (1997) used the term “lifelong machine learning” for a ...
... deal with the mind problem and to create an artificial mind; and then, understanding mind will help the researchers to create an artificial mind. These make the strong-AI field improve incrementally and slowly, just as the human mind does. Thrun (1997) used the term “lifelong machine learning” for a ...
CSE 5290: Artificial Intelligence
... distortions in the input data and their capability of learning. They are often good at solving problems that are too complex for conventional technologies (e.g., problems that do not have an algorithmic solution that are sometimes called NP complete problems or for which an algorithmic solution is t ...
... distortions in the input data and their capability of learning. They are often good at solving problems that are too complex for conventional technologies (e.g., problems that do not have an algorithmic solution that are sometimes called NP complete problems or for which an algorithmic solution is t ...
Introduction to Artificial Intelligence
... Search algorithms are core tool for decision making, especially when the domain is too complex to use alternatives like Dynamic Programming. In recent years, with the increase in computational power search methods became the method of choice for complex domains, like the game of Go, or certain POMDP ...
... Search algorithms are core tool for decision making, especially when the domain is too complex to use alternatives like Dynamic Programming. In recent years, with the increase in computational power search methods became the method of choice for complex domains, like the game of Go, or certain POMDP ...
Connectionism and Information Processing Abstractions
... S3 implies that the essential characteristic of S2 is not continuity but a radically different sense of representation and processing than S1. The connectionist models relate to the symbolic models in the same way S2 relates to S1. An adequate discussion of what makes a symbol requires more space an ...
... S3 implies that the essential characteristic of S2 is not continuity but a radically different sense of representation and processing than S1. The connectionist models relate to the symbolic models in the same way S2 relates to S1. An adequate discussion of what makes a symbol requires more space an ...
Online Full Text
... gained a great attention in the last decade. The problem’s difficulty and interestingness arises from the changing nature of spam. The high accuracy required from any useful spam filter makes the problem even more demanding. In this paper, the Genetic Fuzzy C-Mean Clustering algorithm is evaluated a ...
... gained a great attention in the last decade. The problem’s difficulty and interestingness arises from the changing nature of spam. The high accuracy required from any useful spam filter makes the problem even more demanding. In this paper, the Genetic Fuzzy C-Mean Clustering algorithm is evaluated a ...
View PDF - CiteSeerX
... Feature selection is an effective technique in dealing with dimensionality reduction. For classification, it is used to find an “optimal” subset of relevant features such that the overall accuracy of classification is increased while the data size is reduced and the comprehensibility is improved. Fe ...
... Feature selection is an effective technique in dealing with dimensionality reduction. For classification, it is used to find an “optimal” subset of relevant features such that the overall accuracy of classification is increased while the data size is reduced and the comprehensibility is improved. Fe ...
Deep learning in neural networks: An overview
... have influenced each other in complex ways. Starting from recent DL results, I tried to trace back the origins of relevant ideas through the past half century and beyond, sometimes using ‘‘local search’’ to follow citations of citations backwards in time. Since not all DL publications properly ackno ...
... have influenced each other in complex ways. Starting from recent DL results, I tried to trace back the origins of relevant ideas through the past half century and beyond, sometimes using ‘‘local search’’ to follow citations of citations backwards in time. Since not all DL publications properly ackno ...
APPLICATION OF ARTIFICIAL INTELLIGENCE METHODS IN
... The most widely used techniques include artificial neural networks, fuzzy logic, expert systems, and generic algorithms with interesting developments in hybrid. Other existing techniques include support vector machines, functional network, cased based reasonining and expert systems. Most of the arti ...
... The most widely used techniques include artificial neural networks, fuzzy logic, expert systems, and generic algorithms with interesting developments in hybrid. Other existing techniques include support vector machines, functional network, cased based reasonining and expert systems. Most of the arti ...
Introduction to The Soar Papers - Autonomous Learning Laboratory
... the latter can be found in [j:89]. Our ultimate goal for the Soar architecture is that it serve as a basis for both human and artificial cognition. There is no generally accepted term for such a combination, so how Soar is described usually varies by context. Since the construction of the first majo ...
... the latter can be found in [j:89]. Our ultimate goal for the Soar architecture is that it serve as a basis for both human and artificial cognition. There is no generally accepted term for such a combination, so how Soar is described usually varies by context. Since the construction of the first majo ...
A. Azzini "A New Genetic Approach for Neural Network Design and
... The success of an ANN application usually requires a high number of experiments. Moreover, several parameters of an ANN can affect, during the design, how easy a solution is to find. Some of these parameters are related to the architecture design of the neural network, concerning the number of layer ...
... The success of an ANN application usually requires a high number of experiments. Moreover, several parameters of an ANN can affect, during the design, how easy a solution is to find. Some of these parameters are related to the architecture design of the neural network, concerning the number of layer ...
approximate reasoning using anytime algorithms
... system; and (b) insert into the composite module the necessary code to achieve that performance. The precise definition and solution of the problem depend on the following factors: 1. Composite program structure – what type of programming operators are used to compose anytime algorithms? 2. Type of ...
... system; and (b) insert into the composite module the necessary code to achieve that performance. The precise definition and solution of the problem depend on the following factors: 1. Composite program structure – what type of programming operators are used to compose anytime algorithms? 2. Type of ...
as a PDF
... WFH+96]. In military and medical professions, these systems have evolved in recent years from ad-hoc physical mock-ups to virtual environments with a high degree of automation and standardization [De94, GFH94, WFH+96]. A common aspect of many crisis management tasks is time-critical decision making, ...
... WFH+96]. In military and medical professions, these systems have evolved in recent years from ad-hoc physical mock-ups to virtual environments with a high degree of automation and standardization [De94, GFH94, WFH+96]. A common aspect of many crisis management tasks is time-critical decision making, ...
Ontology learning from text based on multi
... In response to this problem, many new research initiatives have been set up to enrich available information with machine-processable semantics. Tim Berners-Lee, Director of the World Wide Web Consortium, referred to the future of the WWW as the semantic web - an extended web of machine-readable info ...
... In response to this problem, many new research initiatives have been set up to enrich available information with machine-processable semantics. Tim Berners-Lee, Director of the World Wide Web Consortium, referred to the future of the WWW as the semantic web - an extended web of machine-readable info ...
Machine learning
Machine learning is a subfield of computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. Such algorithms operate by building a model from example inputs in order to make data-driven predictions or decisions, rather than following strictly static program instructions.Machine learning is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction-making. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms is infeasible. Example applications include spam filtering, optical character recognition (OCR), search engines and computer vision. Machine learning is sometimes conflated with data mining, although that focuses more on exploratory data analysis. Machine learning and pattern recognition ""can be viewed as two facets ofthe same field.""When employed in industrial contexts, machine learning methods may be referred to as predictive analytics or predictive modelling.