Intelligent Robot Based on Synaptic Plasticity Web Site: www.ijaiem.org Email:
... Similarly, in our work we first show that shining light in front of and or behind the robot elicits no response but pressing the push button causes the robot to move forward or backward. We then press the button while shining the light on the robot and the neural network programmed into the robot ca ...
... Similarly, in our work we first show that shining light in front of and or behind the robot elicits no response but pressing the push button causes the robot to move forward or backward. We then press the button while shining the light on the robot and the neural network programmed into the robot ca ...
Resources - CSE, IIT Bombay
... on the left side of the river and only one boat is available for crossing over to the right side. At any time the boat can carry at most 2 persons and under no circumstance the number of cannibals can be more than the number of missionaries on any bank ...
... on the left side of the river and only one boat is available for crossing over to the right side. At any time the boat can carry at most 2 persons and under no circumstance the number of cannibals can be more than the number of missionaries on any bank ...
Artificial Intelligence and Neural Networks The
... of the first electronic computers. Therefore it seems to be time again to compare todays state-of-the art with thoughts and proposals at the very beginning of the computer age. I have chosen Alan Turing and John von Neumann as the most important representatives of the first concepts of machine intel ...
... of the first electronic computers. Therefore it seems to be time again to compare todays state-of-the art with thoughts and proposals at the very beginning of the computer age. I have chosen Alan Turing and John von Neumann as the most important representatives of the first concepts of machine intel ...
chapter 18a slides
... Learning is needed for unknown environments, or for lazy designers Learning agent = performance element + learning element Learning method depends on type of performance element, available feedback, type of component to be improved, and its representation For supervised learning, the aim is to find ...
... Learning is needed for unknown environments, or for lazy designers Learning agent = performance element + learning element Learning method depends on type of performance element, available feedback, type of component to be improved, and its representation For supervised learning, the aim is to find ...
While most ids systems reported in the literature use training data
... that should be satisfied by a function that quantifies an agent’s beliefs, which introduced the derivation of Dempster's rule of conditioning. The TBM is based on the assumption that beliefs manifest themselves at two mental levels: the credal level where beliefs are fitted and the pignistic level w ...
... that should be satisfied by a function that quantifies an agent’s beliefs, which introduced the derivation of Dempster's rule of conditioning. The TBM is based on the assumption that beliefs manifest themselves at two mental levels: the credal level where beliefs are fitted and the pignistic level w ...
Neural Networks and Evolutionary Computation
... evolution, on the application of evolutionary operators like mutation, recombination and selection. Like no other computational method, EAs have been applied to a very broad range of problems [2]. In the recent years the idea of combining ANNs and EAs has received much attention [1, 32, 50, 58, 59, ...
... evolution, on the application of evolutionary operators like mutation, recombination and selection. Like no other computational method, EAs have been applied to a very broad range of problems [2]. In the recent years the idea of combining ANNs and EAs has received much attention [1, 32, 50, 58, 59, ...
An Evolutionary Artificial Neural Network Time Series Forecasting
... Artificial Neural Networks (ANNs) have the ability of learning and to adapt to new situations by recognizing patterns in previous data. Time Series (TS) (observations ordered in time) often present a high degree of noise which difficults forecasting. Using ANNs for Time Series Forecasting (TSF) may ...
... Artificial Neural Networks (ANNs) have the ability of learning and to adapt to new situations by recognizing patterns in previous data. Time Series (TS) (observations ordered in time) often present a high degree of noise which difficults forecasting. Using ANNs for Time Series Forecasting (TSF) may ...
Machines that dream: A brief introduction into developing artificial
... are mathematical systems consisting of only a few equations that are too complex for a human to understand the behavior of the equations. The only way to know how equations will behave is to run ...
... are mathematical systems consisting of only a few equations that are too complex for a human to understand the behavior of the equations. The only way to know how equations will behave is to run ...
Approaches to Artificial Intelligence
... One's approach to research in AI seems to depend to a large extent on what propert.ies of int.elligent behaviour one is most. impressed by. For some, it might be the evolut.ionary ant.ecedents of this behaviour in other animals; for others, its biological underpinnings in the central nervous systemj ...
... One's approach to research in AI seems to depend to a large extent on what propert.ies of int.elligent behaviour one is most. impressed by. For some, it might be the evolut.ionary ant.ecedents of this behaviour in other animals; for others, its biological underpinnings in the central nervous systemj ...
Chapter 5
... ID3 Program = to classify a particular input, we start at the top of the tree and answer questions until we reach a leaf, where the classification is stored. See Figure 17.13 Decision tree p. 470 1. Choose window = random subset of training examples to train 2. Outside window = use to test the decis ...
... ID3 Program = to classify a particular input, we start at the top of the tree and answer questions until we reach a leaf, where the classification is stored. See Figure 17.13 Decision tree p. 470 1. Choose window = random subset of training examples to train 2. Outside window = use to test the decis ...
Motivated Learning for Machine Intelligence_ Nov
... He called this the “motivated complexity” principle. Chicken and egg problem? An agent must have a motivation to develop while his motivation comes from development? ...
... He called this the “motivated complexity” principle. Chicken and egg problem? An agent must have a motivation to develop while his motivation comes from development? ...
Advances in Artificial Intelligence Require Progress Across all of
... Historically, special purpose hardware architectures for specific tasks e.g., computer vision, have been in and out of fashion. However, as we approach the end of the Moore’s law, performance gains that are required for successful deployment of AI systems in real-world applications are likely to ...
... Historically, special purpose hardware architectures for specific tasks e.g., computer vision, have been in and out of fashion. However, as we approach the end of the Moore’s law, performance gains that are required for successful deployment of AI systems in real-world applications are likely to ...
Motivated_Learning_BARCELONA
... a primitive pain is directly sensed thresholded curiosity based pain ...
... a primitive pain is directly sensed thresholded curiosity based pain ...
Improving Control-Knowledge Acquisition for Planning by Active
... problem; – it retrieves the literal l in which the chosen instance appear in the state/goal. For instance, suppose that it has selected objecti , such that (in objecti airplanej ) is true in the initial state; – it randomly selects a predicate p from the predicates in which the instances of the chos ...
... problem; – it retrieves the literal l in which the chosen instance appear in the state/goal. For instance, suppose that it has selected objecti , such that (in objecti airplanej ) is true in the initial state; – it randomly selects a predicate p from the predicates in which the instances of the chos ...
Logical and Probabilistic Knowledge Representation and Reasoning
... [23] Feng Niu, Christopher Ré, AnHai Doan, Jude Shavlik. Tuffy: Scaling up Statistical Inference in Markov Logic Networks using an RDBMS. Proc. VLDB, 2011. [24] Jeff B. Paris. The Uncertain Reasoner’s Companion: A Mathematical Perspective. Cambridge U.P., 1994. [25] Jeff B. Paris. Common Sense and ...
... [23] Feng Niu, Christopher Ré, AnHai Doan, Jude Shavlik. Tuffy: Scaling up Statistical Inference in Markov Logic Networks using an RDBMS. Proc. VLDB, 2011. [24] Jeff B. Paris. The Uncertain Reasoner’s Companion: A Mathematical Perspective. Cambridge U.P., 1994. [25] Jeff B. Paris. Common Sense and ...
Evolving Connectionist and Fuzzy-Connectionist Systems for
... 1. Introduction: On-line, Adaptive Decision Making and Control The complexity and the dynamics of many real-world problems, especially in engineering and manufacturing, require using sophisticated methods and tools for building on-line, adaptive decision making and control systems (OLADECS). Such sy ...
... 1. Introduction: On-line, Adaptive Decision Making and Control The complexity and the dynamics of many real-world problems, especially in engineering and manufacturing, require using sophisticated methods and tools for building on-line, adaptive decision making and control systems (OLADECS). Such sy ...
Kære kollegaer,
... until you reach a leaf. The leaf stores the classification (Sunburnt or None). In the present case the decision tree agrees with our intuition about factors that are decisive for getting surnburnt. For example, neither a person’s weight nor height plays a role. It is often possible to construct more ...
... until you reach a leaf. The leaf stores the classification (Sunburnt or None). In the present case the decision tree agrees with our intuition about factors that are decisive for getting surnburnt. For example, neither a person’s weight nor height plays a role. It is often possible to construct more ...
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.