Logical and Probabilistic Knowledge Representation and Reasoning
... • MLNs combine FO logic and Markov Networks (MNs) in the same representation ...
... • MLNs combine FO logic and Markov Networks (MNs) in the same representation ...
The Learning Potentials of Number Blocks
... Our iterative design process included several 2 h sessions with our target group. The themes for the sessions were: (1) Getting to know each other and the technology; (2) Brainstorming and decision making; (3) Recording sound; (4) Testing the “Pronounce number Function”; (5) Testing the “Compare Num ...
... Our iterative design process included several 2 h sessions with our target group. The themes for the sessions were: (1) Getting to know each other and the technology; (2) Brainstorming and decision making; (3) Recording sound; (4) Testing the “Pronounce number Function”; (5) Testing the “Compare Num ...
An Introduction to Reinforcement Learning
... and R, how can we find an optimal policy π ∗ ? Various classes of learning methods exist. We will consider a simple one called Q-learning, which is a temporal difference learning algorithm. Let Q be our “guess” of Q ∗ : for every state s and action a, initialise Q(s, a) arbitrarily. We will start ...
... and R, how can we find an optimal policy π ∗ ? Various classes of learning methods exist. We will consider a simple one called Q-learning, which is a temporal difference learning algorithm. Let Q be our “guess” of Q ∗ : for every state s and action a, initialise Q(s, a) arbitrarily. We will start ...
Observational Versus Trial and Error Effects in a - FORTH-ICS
... that code for an action and an object (i.e grasp-ball/push-ball). In the brain, these are coded in separate areas, but we simplify here. Object representation. This module picks out object features to identify objects. In our simple simulation, the only possible object to be recognised is a ball. Th ...
... that code for an action and an object (i.e grasp-ball/push-ball). In the brain, these are coded in separate areas, but we simplify here. Object representation. This module picks out object features to identify objects. In our simple simulation, the only possible object to be recognised is a ball. Th ...
ACO Explorer and PMPM Explorer Application
... • Compare and contrast machine learning and artificial intelligence. • Discuss techniques that offer feedback into the system and when it’s necessary to retrain a model. • Give advice on how to avoid common pitfalls in machine learning implementation. ...
... • Compare and contrast machine learning and artificial intelligence. • Discuss techniques that offer feedback into the system and when it’s necessary to retrain a model. • Give advice on how to avoid common pitfalls in machine learning implementation. ...
An algorithm for inducing least generalization under relative
... Conclusion and further work The presented algorithm is a step towards investigating the potential of constructive generalization with respect to background knowledge in the context of Inductive Logic Programming. The problem of using logical implication in a constructive way is known to be hard. Thi ...
... Conclusion and further work The presented algorithm is a step towards investigating the potential of constructive generalization with respect to background knowledge in the context of Inductive Logic Programming. The problem of using logical implication in a constructive way is known to be hard. Thi ...
Automated Bidding Strategy Adaption using Learning Agents in
... as a structured way of designing electronic markets. As one component of this process multi-agent based simulation for market evaluation is described. The central aspect of this paper is to focus different machine learning algorithms in order to identify algorithms for future market mechanism evalua ...
... as a structured way of designing electronic markets. As one component of this process multi-agent based simulation for market evaluation is described. The central aspect of this paper is to focus different machine learning algorithms in order to identify algorithms for future market mechanism evalua ...
Course Learning Outcomes
... Course Description This course is a broad graduate level introduction to the field of artificial intelligence (AI). Topics covered will include state-based problem solving, heuristic (informed) search, constraint satisfaction algorithms, game playing algorithms, propositional and first-order logic, ...
... Course Description This course is a broad graduate level introduction to the field of artificial intelligence (AI). Topics covered will include state-based problem solving, heuristic (informed) search, constraint satisfaction algorithms, game playing algorithms, propositional and first-order logic, ...
Knowledge Acquisition Via Incremental Conceptual Clustering
... examples or conceptual clustering, are nonincremental - all objects must be present at the outset of system execution. In contrast, incremental methods accept a stream of objects that are assimilated one at a time. A primary motivation for using incremental systems is that knowledge may be rapidly u ...
... examples or conceptual clustering, are nonincremental - all objects must be present at the outset of system execution. In contrast, incremental methods accept a stream of objects that are assimilated one at a time. A primary motivation for using incremental systems is that knowledge may be rapidly u ...
Towards a DNA sequencing theory (learning a string)
... past, researchers are concentrated on the “learnabil- ...
... past, researchers are concentrated on the “learnabil- ...
lecture 2 not ready - Villanova Department of Computing Sciences
... • Compression: The rule is simpler than the data it explains • Outlier detection: Exceptions that are not covered by the rule, e.g., fraud CSC 4510 - M.A. Papalaskari - Villanova University ...
... • Compression: The rule is simpler than the data it explains • Outlier detection: Exceptions that are not covered by the rule, e.g., fraud CSC 4510 - M.A. Papalaskari - Villanova University ...
Table 1 shows the statistics based on all questions answered,... some students answered four questions. Averages are fairly consistent across
... b) Some answers failed to identify appropriate aspects of human intelligence that set apart KBS from conventional systems, e.g. “being able to solve a problem.” A4.ii a) Answers mostly identified creativity as being hard to emulate in KBS, but restricted definition to “producing something new”. KBS ...
... b) Some answers failed to identify appropriate aspects of human intelligence that set apart KBS from conventional systems, e.g. “being able to solve a problem.” A4.ii a) Answers mostly identified creativity as being hard to emulate in KBS, but restricted definition to “producing something new”. KBS ...
Impaired associative learning in schizophrenia: behavioral and
... Complex glutamate–dopamine interactions may also be implicated (Castner and Williams 2007). The induction of the NMDA antagonist MK801 produces a dose-dependent increase in irregularly discharged single spikes with a concomitant decreases in burst discharges in the prefrontal cortex of freely moving ...
... Complex glutamate–dopamine interactions may also be implicated (Castner and Williams 2007). The induction of the NMDA antagonist MK801 produces a dose-dependent increase in irregularly discharged single spikes with a concomitant decreases in burst discharges in the prefrontal cortex of freely moving ...
CV - Olivier Georgeon
... algorithms, and methods to replicate situated cognition (i.e., in which, “knowledge develops as a means of coordinating activity within activity itself”, Clancey 1997). My colleagues and I proposed the Enactive Cognitive Architecture (ECA, Georgeon, Marshall, & Manzotti, 2013). ECA avoids making ...
... algorithms, and methods to replicate situated cognition (i.e., in which, “knowledge develops as a means of coordinating activity within activity itself”, Clancey 1997). My colleagues and I proposed the Enactive Cognitive Architecture (ECA, Georgeon, Marshall, & Manzotti, 2013). ECA avoids making ...
Reasoning and learning by analogy: Introduction.
... of some combination of structural information about the form of the analogs and pragmatic information about the goals that triggered the reasoning episode. Theories of analogy have been instantiated in computer simulations. The output of a simulation can be compared to human performance with analogi ...
... of some combination of structural information about the form of the analogs and pragmatic information about the goals that triggered the reasoning episode. Theories of analogy have been instantiated in computer simulations. The output of a simulation can be compared to human performance with analogi ...
Multi-Conditional Learning: Generative/Discriminative Training for
... a latent space projection that captures not only the cooccurrence of features in input (as in generative models), but also provides the ability to accurately predict designated outputs (as in discriminative models). We find that MCL is more robust than the conditional criterion alone, while also bei ...
... a latent space projection that captures not only the cooccurrence of features in input (as in generative models), but also provides the ability to accurately predict designated outputs (as in discriminative models). We find that MCL is more robust than the conditional criterion alone, while also bei ...
Computational Intelligence
... introduced and the substantial differences will be explained. An expanded model of association in neural structures will be introduced to model a kind of semantic and episodic memories. On this background, a few kinds of associative neural networks, their advanced associative features and concluding ...
... introduced and the substantial differences will be explained. An expanded model of association in neural structures will be introduced to model a kind of semantic and episodic memories. On this background, a few kinds of associative neural networks, their advanced associative features and concluding ...
Learning Visual Representations for Perception
... how to act. However, despite decades of research, current deliberative artificial agents are still severely limited in the environmental complexity they can handle. Since it is difficult to anticipate which world features turn out to be important to the agent, creating internal representations is a ...
... how to act. However, despite decades of research, current deliberative artificial agents are still severely limited in the environmental complexity they can handle. Since it is difficult to anticipate which world features turn out to be important to the agent, creating internal representations is a ...
Making Music with AI: Some examples
... with the instructions given in well known commented editions of “The Well-Tempered Clavier”. The main limitation of this system is its lack of generality because it only works well for fugues written on a 4/4 meter. For different meters, the rules should be different. Another obvious consequence of ...
... with the instructions given in well known commented editions of “The Well-Tempered Clavier”. The main limitation of this system is its lack of generality because it only works well for fugues written on a 4/4 meter. For different meters, the rules should be different. Another obvious consequence of ...
2017 Trends to Watch: Artificial Intelligence - Ovum
... Also worth mentioning is that IBM Watson has a number of services available on IBM Bluemix and has a new business division devoted to Watson consulting projects. These will be bespoke solutions that IBM will work on with the client, and models created from these projects will usually trickle down in ...
... Also worth mentioning is that IBM Watson has a number of services available on IBM Bluemix and has a new business division devoted to Watson consulting projects. These will be bespoke solutions that IBM will work on with the client, and models created from these projects will usually trickle down in ...
CH08_withFigures
... unsupervised mode – Kohonen’s algorithm forms “feature maps,” where neighborhoods of neurons are constructed – These neighborhoods are organized such that topologically close neurons are sensitive to similar inputs into the model – Self-organizing maps, or self organizing feature maps, can sometimes ...
... unsupervised mode – Kohonen’s algorithm forms “feature maps,” where neighborhoods of neurons are constructed – These neighborhoods are organized such that topologically close neurons are sensitive to similar inputs into the model – Self-organizing maps, or self organizing feature maps, can sometimes ...
Online Adaptable Learning Rates for the Game Connect-4
... Tesauro’s seminal success with TD-Gammon in 1994, many successful agents use temporal difference learning today. But in order to be successful with temporal difference learning on game tasks, often a careful selection of features and a large number of training games is necessary. Even for board game ...
... Tesauro’s seminal success with TD-Gammon in 1994, many successful agents use temporal difference learning today. But in order to be successful with temporal difference learning on game tasks, often a careful selection of features and a large number of training games is necessary. Even for board game ...
Introduction to AI (COMP-424) - McGill School Of Computer Science
... course in just 6 hours and 53 minutes without human intervention and guided only by global positioning satellite waypoints. The feat, which won a $2 million prize from the Pentagon Defense Advanced Research Project Agency, was compared by exuberant Darpa officials to the Wright brothers’ accomplishmen ...
... course in just 6 hours and 53 minutes without human intervention and guided only by global positioning satellite waypoints. The feat, which won a $2 million prize from the Pentagon Defense Advanced Research Project Agency, was compared by exuberant Darpa officials to the Wright brothers’ accomplishmen ...
Graph-Based Relational Learning: Current and Future Directions
... grammars offer the ability to represent recursive graphical hypotheses [5]. Graph grammars are similar to string grammars except that terminals can be arbitrary graphs rather than symbols from an alphabet. Graph grammars can be divided into two types: node-replacement grammars and hyperedge-replacem ...
... grammars offer the ability to represent recursive graphical hypotheses [5]. Graph grammars are similar to string grammars except that terminals can be arbitrary graphs rather than symbols from an alphabet. Graph grammars can be divided into two types: node-replacement grammars and hyperedge-replacem ...