Mapping Between Agent Architectures and Brain Organization
... point out that even though CAA skill modules aren’t typically analogous to cortical regions, this is for reasons of practicality. Within the agent discipline, modularity in CAA is primarily to support an orderly decomposition of intelligence into manageable, constructible units. But if one is more i ...
... point out that even though CAA skill modules aren’t typically analogous to cortical regions, this is for reasons of practicality. Within the agent discipline, modularity in CAA is primarily to support an orderly decomposition of intelligence into manageable, constructible units. But if one is more i ...
K-NN
... – When many irrelevant attributes, similarity/distance measure becomes less reliable Remedy – Try to remove irrelevant attributes in pre-processing step – Weight attributes differently – Increase k (but not too much) ...
... – When many irrelevant attributes, similarity/distance measure becomes less reliable Remedy – Try to remove irrelevant attributes in pre-processing step – Weight attributes differently – Increase k (but not too much) ...
Towards comprehensive foundations of computational intelligence.
... free lunch’ theorem [63, 174] shows that there is no single learning algorithm that is inherently superior to all other algorithms. In real world applications there may be many ...
... free lunch’ theorem [63, 174] shows that there is no single learning algorithm that is inherently superior to all other algorithms. In real world applications there may be many ...
A Review of Machine Learning for Automated Plan- ning
... (ICAPS). Even if this review includes classic systems for planning and learning, mainly it focuses on covering the last approaches on ML for AP. The review is organized according to AP’s two knowledge acquisition problems: (1) learning planning action models and (2) learning search control. Figure 1 ...
... (ICAPS). Even if this review includes classic systems for planning and learning, mainly it focuses on covering the last approaches on ML for AP. The review is organized according to AP’s two knowledge acquisition problems: (1) learning planning action models and (2) learning search control. Figure 1 ...
Case-based Reasoning in Agent-based Decision Support System
... developers to take the system's context into account through the set of defined variables that are linked to the application domain. With these extensions the focus in decision support systems development shifts from task ontology towards domain ontology. Most AI systems operate on a first-principle ...
... developers to take the system's context into account through the set of defined variables that are linked to the application domain. With these extensions the focus in decision support systems development shifts from task ontology towards domain ontology. Most AI systems operate on a first-principle ...
Acta polytechnica Hungarica - Volume 4, Issue No. 1 (2007.)
... developers to take the system's context into account through the set of defined variables that are linked to the application domain. With these extensions the focus in decision support systems development shifts from task ontology towards domain ontology. Most AI systems operate on a first-principle ...
... developers to take the system's context into account through the set of defined variables that are linked to the application domain. With these extensions the focus in decision support systems development shifts from task ontology towards domain ontology. Most AI systems operate on a first-principle ...
Operational Rationality through Compilation of Anytime Algorithms
... control the cost of base-level reasoning. The model that I developed for this dissertation belongs to this class of solutions. In particular, its metalevel reasoning component optimizes resource allocation to the base-level performance components. The resulting agent is said to be an operationally r ...
... control the cost of base-level reasoning. The model that I developed for this dissertation belongs to this class of solutions. In particular, its metalevel reasoning component optimizes resource allocation to the base-level performance components. The resulting agent is said to be an operationally r ...
Research Trends in Technology-based Learning from 2000 to 2009
... The prevalent use of computing and communication technologies has increased in education and thus, learning is no longer limited to the traditional environment. Communication technologies such as the Internet, digital programs and systems, Personal Digital Assistants (PDA), and simulation games have ...
... The prevalent use of computing and communication technologies has increased in education and thus, learning is no longer limited to the traditional environment. Communication technologies such as the Internet, digital programs and systems, Personal Digital Assistants (PDA), and simulation games have ...
Main Areas of AI
... • If computers are intelligent what civil rights should be given to computers? • If computers can perform most of our work; what should the human beings do? • Only those things that can be represented in computers are important. • It is fun to play with computers. ...
... • If computers are intelligent what civil rights should be given to computers? • If computers can perform most of our work; what should the human beings do? • Only those things that can be represented in computers are important. • It is fun to play with computers. ...
Real-Time Credit-Card Fraud Detection using Artificial Neural
... good or not in comparison with the current one, a very basic one is exp((currentSol-nextSol)/currentTemp), (5) and the last one is stopping criteria, there are many stopping criteria’s, in this paper we have used an threshold value of objective function as an stopping criteria. IV. TRAINING OF ANN A ...
... good or not in comparison with the current one, a very basic one is exp((currentSol-nextSol)/currentTemp), (5) and the last one is stopping criteria, there are many stopping criteria’s, in this paper we have used an threshold value of objective function as an stopping criteria. IV. TRAINING OF ANN A ...
Analogical Reasoning: A Core of Cognition
... expressions. For example, a statement like Gills are the lungs of fish can be analyzed as the solution to the following analogy: mammals : lungs :: fish : X. Here the word gills refers to whatever the analogical relation constructs for X. What is mentioned in the surface structure of the metaphoric ...
... expressions. For example, a statement like Gills are the lungs of fish can be analyzed as the solution to the following analogy: mammals : lungs :: fish : X. Here the word gills refers to whatever the analogical relation constructs for X. What is mentioned in the surface structure of the metaphoric ...
as PDF - The ORCHID Project
... et al., 2005], a special class of DEC-POMDPs where agents are organized in a network structure. Banerjee et al. [2012] ...
... et al., 2005], a special class of DEC-POMDPs where agents are organized in a network structure. Banerjee et al. [2012] ...
Reports of the AAAI 2010 Conference Workshops
... from one field to problems from the other, and to identify the key issues to be addressed in increasing the convergence between security and AI. This is a fertile area for research, and has been attracting an increasing amount of interest in both communities. Prior to this workshop there was a 2009 ...
... from one field to problems from the other, and to identify the key issues to be addressed in increasing the convergence between security and AI. This is a fertile area for research, and has been attracting an increasing amount of interest in both communities. Prior to this workshop there was a 2009 ...
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.