Algorithm selection by rational metareasoning as
... Most previous theories of how humans learn when to use which cognitive strategy assume basic model-free reinforcement learning [18–20]. The REinforcement Learning among Cognitive Strategies model (RELACS [19]) and the Strategy Selection Learning model (SSL [20]) each postulate that people learn just ...
... Most previous theories of how humans learn when to use which cognitive strategy assume basic model-free reinforcement learning [18–20]. The REinforcement Learning among Cognitive Strategies model (RELACS [19]) and the Strategy Selection Learning model (SSL [20]) each postulate that people learn just ...
Resolution Based Explanations for Reasoning in the Description Logic
... 3. At last, the resolution proof is transformed into its corresponding refutation graph [6]. Our algorithm traverses the graph and “reads” the proof to generate explanations. Later, the clauses involved in each traversal step are traced back to the contributing axioms/assertions in the original DL k ...
... 3. At last, the resolution proof is transformed into its corresponding refutation graph [6]. Our algorithm traverses the graph and “reads” the proof to generate explanations. Later, the clauses involved in each traversal step are traced back to the contributing axioms/assertions in the original DL k ...
Assumptions of Decision-Making Models in AGI
... The assumption on budget: The system can afford the computational resources demanded by the selection algorithm. There are many situations where the above assumptions can be reasonably accepted, and the corresponding models have been successfully applied [11, 9]. However, there are reasons to argue ...
... The assumption on budget: The system can afford the computational resources demanded by the selection algorithm. There are many situations where the above assumptions can be reasonably accepted, and the corresponding models have been successfully applied [11, 9]. However, there are reasons to argue ...
A Comprehensive Overview of Clustering
... input value to given set of data labels. Based on the learning method used to generate the output we have the following classification, supervised and unsupervised learning. Unsupervised learning involves clustering and blind signal separation. Supervised learning is also known as classification. Th ...
... input value to given set of data labels. Based on the learning method used to generate the output we have the following classification, supervised and unsupervised learning. Unsupervised learning involves clustering and blind signal separation. Supervised learning is also known as classification. Th ...
An Artificial Intelligence Approach Towards Sensorial
... pending, the evaluation presented in this paper uses completely noise-free strain values from the FEM simulation step (cf. Figure 1) to gain a first set of reference success rates for the model of a 200 × 300 × 1 mm St37 steel plate. For each examined machine learning method, the obtained results re ...
... pending, the evaluation presented in this paper uses completely noise-free strain values from the FEM simulation step (cf. Figure 1) to gain a first set of reference success rates for the model of a 200 × 300 × 1 mm St37 steel plate. For each examined machine learning method, the obtained results re ...
Financial time series forecasting with machine learning techniques
... In order to determine the effectiveness of a machine learning technique, a benchmark model is needed. A variety of evaluation methods is used in the literature. This survey categorises the evaluation models into the categories buy & hold, random walk, statistical techniques, other machines learning ...
... In order to determine the effectiveness of a machine learning technique, a benchmark model is needed. A variety of evaluation methods is used in the literature. This survey categorises the evaluation models into the categories buy & hold, random walk, statistical techniques, other machines learning ...
Deep Learning for Artificial General Intelligence
... It is expanding to encompass a wide range of methods, including: memory, unsupervised learning, learning to act, program learning and attention. ...
... It is expanding to encompass a wide range of methods, including: memory, unsupervised learning, learning to act, program learning and attention. ...
A Case-Based Approach To Imitation Learning in Robotic Agents
... raises the difficult issues of defining the robot’s background knowledge, representation language, memory, abstraction, and transfer processes. In this paper, we describe our long-term goals for a case-based learning agent, as well as present preliminary work [9, 10] toward developing such an agent. ...
... raises the difficult issues of defining the robot’s background knowledge, representation language, memory, abstraction, and transfer processes. In this paper, we describe our long-term goals for a case-based learning agent, as well as present preliminary work [9, 10] toward developing such an agent. ...
Introduction to Cognitive Science
... Analog models: Molecules – Billiard-balls, Mechanical model: DNA molecule - Metal-made helix ...
... Analog models: Molecules – Billiard-balls, Mechanical model: DNA molecule - Metal-made helix ...
Topic 4
... its neighbours are adjusted to make them resemble the input pattern even more closely At the completion of training, the best matching node ends up with its input weight values aligned with the input pattern and produces the strongest output whenever that particular pattern is presented The node ...
... its neighbours are adjusted to make them resemble the input pattern even more closely At the completion of training, the best matching node ends up with its input weight values aligned with the input pattern and produces the strongest output whenever that particular pattern is presented The node ...
Towards a robotic model of the mirror neuron system
... self-organized formation of special codes (parametric biases) that are able to trigger specific behavior of the network without an actual sensory input. These self-organized parametric biases can be considered analogous to mirror neurons. Our model roughly models brain areas at certain level of abst ...
... self-organized formation of special codes (parametric biases) that are able to trigger specific behavior of the network without an actual sensory input. These self-organized parametric biases can be considered analogous to mirror neurons. Our model roughly models brain areas at certain level of abst ...
The return of the machinery question
... MetaMind, an AI startup recently acquired by Salesforce, is pursuing a related approach called multitask learning, where the same neural-network architecture is used to solve several different kinds of problems in such a way that experience of one thing makes it better at another. Like DeepMind, it ...
... MetaMind, an AI startup recently acquired by Salesforce, is pursuing a related approach called multitask learning, where the same neural-network architecture is used to solve several different kinds of problems in such a way that experience of one thing makes it better at another. Like DeepMind, it ...
An Integrated Approach of Learning, Planning, and Execution
... Simmons and Mitchell, 1989; Stone and Veloso, 1998; Matellán et al., 1998; Ashish et al., 1997), ranging from work on autonomous robotic agents to Web-based software agents. It integrates many areas, such as robotics, planning, and machine learning. This integration opens many questions that arise w ...
... Simmons and Mitchell, 1989; Stone and Veloso, 1998; Matellán et al., 1998; Ashish et al., 1997), ranging from work on autonomous robotic agents to Web-based software agents. It integrates many areas, such as robotics, planning, and machine learning. This integration opens many questions that arise w ...
Semantic Outlier Detection for Affective Common-Sense Reasoning and Concept-Level Sentiment Analysis Erik Cambria
... associated with each concept into a set of values of uncorrelated variables (the principal components of the SVD). By using Lanczos’ method (Lanczos 1950), moreover, the generalization process is relatively fast (a few seconds), despite the size and the sparseness of AffectNet. Applying TSVD on Affe ...
... associated with each concept into a set of values of uncorrelated variables (the principal components of the SVD). By using Lanczos’ method (Lanczos 1950), moreover, the generalization process is relatively fast (a few seconds), despite the size and the sparseness of AffectNet. Applying TSVD on Affe ...
(2005). Integrating Language and Cognition
... What in the mind help us do this? Representations, models, ontologies? – What are the nature of representations in the mind? – There are wooden chairs in the world, but no wood in the brain ...
... What in the mind help us do this? Representations, models, ontologies? – What are the nature of representations in the mind? – There are wooden chairs in the world, but no wood in the brain ...
Continuous transformation learning of translation
... requires different weight vectors to be learned by neurons than are typically learned by competitive networks in which the patterns within a cluster overlap with each other. We show here how translation invariant representations can be learned in continuous transformation learning by the associative ...
... requires different weight vectors to be learned by neurons than are typically learned by competitive networks in which the patterns within a cluster overlap with each other. We show here how translation invariant representations can be learned in continuous transformation learning by the associative ...
Time representation in reinforcement learning models of
... Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Room 46-4053 77 Massachusetts Ave. Cambridge, MA, 02139, USA ...
... Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Room 46-4053 77 Massachusetts Ave. Cambridge, MA, 02139, USA ...
A Unified Cognitive Architecture for Physical Agents
... Determining whether a given skill clause is applicable relies on the same match process utilized in conceptual inference and goal satisfaction. Matching the percepts, start conditions, and requirements fields of a skill involves comparing the generalized structures to elements in the perceptual buff ...
... Determining whether a given skill clause is applicable relies on the same match process utilized in conceptual inference and goal satisfaction. Matching the percepts, start conditions, and requirements fields of a skill involves comparing the generalized structures to elements in the perceptual buff ...
PDF file
... Then, why does the cortex require a layer of unsupervised feature layer? Developmentally, an unsupervised layer is complete in that the representation does not neglect components that may be needed for discrimination for some future tasks, given the limited resource (the number of neurons) in the la ...
... Then, why does the cortex require a layer of unsupervised feature layer? Developmentally, an unsupervised layer is complete in that the representation does not neglect components that may be needed for discrimination for some future tasks, given the limited resource (the number of neurons) in the la ...
Behavioral and Neural Properties of Social Reinforcement Learning
... 1Sackler Institute for Developmental Psychobiology, Weill Cornell Medical College, New York, New York 10065, 2Department of Psychology, New York University, New York, New York 10003, 3Lucas Center for Imaging, Department of Radiology, Stanford University, Stanford, California 94305, and 4Citigroup B ...
... 1Sackler Institute for Developmental Psychobiology, Weill Cornell Medical College, New York, New York 10065, 2Department of Psychology, New York University, New York, New York 10003, 3Lucas Center for Imaging, Department of Radiology, Stanford University, Stanford, California 94305, and 4Citigroup B ...
Proximal Gradient Temporal Difference Learning Algorithms
... formulation not only gives us the opportunity to use the techniques for the analysis of SG methods to derive finite-sample performance bounds for the GTD algorithms, but also it allows us to use the powerful algorithms that have been recently developed to solve the SG problems and derive more effici ...
... formulation not only gives us the opportunity to use the techniques for the analysis of SG methods to derive finite-sample performance bounds for the GTD algorithms, but also it allows us to use the powerful algorithms that have been recently developed to solve the SG problems and derive more effici ...
Computational models of reinforcement learning
... may not be contingent upon actions taken by the agent. In most models, the output of this function is computed as the Temporal Difference (TD) error between estimated and actual rewards. (3) A policy function (also known as actor) which maps the agent states to possible actions, using the output of ...
... may not be contingent upon actions taken by the agent. In most models, the output of this function is computed as the Temporal Difference (TD) error between estimated and actual rewards. (3) A policy function (also known as actor) which maps the agent states to possible actions, using the output of ...
Goal-direction and top-down control
... come in many different forms. They can range from short-term, such as finding a snack when hungry, to long-term, such as working towards tenure. Goals can also vary from concrete, such as searching for your keys, to abstract, such as wanting to exercise more. Regardless of their form, all goals shar ...
... come in many different forms. They can range from short-term, such as finding a snack when hungry, to long-term, such as working towards tenure. Goals can also vary from concrete, such as searching for your keys, to abstract, such as wanting to exercise more. Regardless of their form, all goals shar ...
AAAI-08 / IAAI-08 - Association for the Advancement of Artificial
... Eric Horvitz is a principal researcher and research area manager at Microsoft Research. He has had a lifelong interest in perception, reasoning, and action under uncertainty. He has pursued insights about intelligence via studies of inference and decision making under limited and varying computation ...
... Eric Horvitz is a principal researcher and research area manager at Microsoft Research. He has had a lifelong interest in perception, reasoning, and action under uncertainty. He has pursued insights about intelligence via studies of inference and decision making under limited and varying computation ...
Cortical and basal ganglia contributions to habit learning and
... and speed to asymptote. Although this criterion is commonly used, other more formal tests have been proposed. In cognitive science, the most widely used criteria come from Schneider and Shiffrin [83], who proposed that a behavior should be considered automatic if the triggering sensory events almost ...
... and speed to asymptote. Although this criterion is commonly used, other more formal tests have been proposed. In cognitive science, the most widely used criteria come from Schneider and Shiffrin [83], who proposed that a behavior should be considered automatic if the triggering sensory events almost ...