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Unifying Logical and Statistical AI - Washington
Unifying Logical and Statistical AI - Washington

... logical KB, becoming equivalent to one in the limit of all infinite weights. When the weights are positive and finite, and all formulas are simultaneously satisfiable, the satisfying solutions are the modes of the distribution represented by the ground Markov network. Most importantly, Markov logic ...
Program - Association for the Advancement of Artificial Intelligence
Program - Association for the Advancement of Artificial Intelligence

... Each AAAI-16 poster / demo session will include posters by authors who presented poster ads that day (please see schedule for detail). In addition, a total of 29 technical demos will be divided among the three evening sessions. Sunday evening will also include Doctoral Consortium and EAAI posters. M ...
The Promise and Perils of Artificial Intelligence
The Promise and Perils of Artificial Intelligence

...  Tools that exhibit human intelligence and behaviour including selflearning robots, expert systems, voice recognition, natural and automated translation. Unesco/education  The branch of computer science dealing with the reproduction or mimicking of human-level thought in computers; The essential q ...
Slides  - Neural Network Research Group
Slides - Neural Network Research Group

... • Turing Test for game bots: $10,000 prize (2007-12) • Three players in Unreal Tournament 2004: – Human confederate: tries to win – Software bot: pretends to be human – Human judge: tries to tell them apart! ...
Bootstrap Planner: an Iterative Approach to Learn Heuristic
Bootstrap Planner: an Iterative Approach to Learn Heuristic

... process on a larger set of training instances with the newly created heuristic. If the planner fails to solve enough training instances, it increases the time limit and tries to solve the training instances using the new time limit. The heuristic is learned as follows. For each state on the plan of ...
A Survey of Current Practice and Teaching of AI
A Survey of Current Practice and Teaching of AI

... factor as it is mentioned by only 15% of our respondents. Twelve percent of our colleagues cited lack of student preparation or interest as a reason for not adding desired topics. ...
Wollowski, M., Selkowitz, R., Brown, L., Goel, A
Wollowski, M., Selkowitz, R., Brown, L., Goel, A

... they deem important, there may be constraints that prevent them from following their wishes. As such, we inquired about what topics and techniques ought to be covered. Lending insight into what are considered basic topics such as mentioned in Question 3; we see that an ideal course covers search, kn ...
A Comparative Analysis of Classification with Unlabelled Data using
A Comparative Analysis of Classification with Unlabelled Data using

... distribution, they concentrate solely on using the same representation to improve classification accuracy. A method for constructing augmented Bayesian networks is a novel, more efficient search algorithm, which is SuperParent. They compare these to Friedman and Goldszmidt’s approach [11] and show t ...
Part I - Department of Computer Science and Engineering
Part I - Department of Computer Science and Engineering

... Complex data usually have multiple facets and can be meaningfully partitioned in multiple ways. Multidimensional clustering / Multi-Clustering ...
Classifier Ensembles for Detecting Concept Change in Streaming
Classifier Ensembles for Detecting Concept Change in Streaming

... • Classifier ensembles versus single classifiers. A categorisation of classifier ensemble methods for changing environment is offered in [22]. The methods are grouped with respect to how they adapt to the concept drift: by updating the combination rule for fixed classifiers (“horse racing”); by usin ...
Matching tutor to student: rules and mechanisms for
Matching tutor to student: rules and mechanisms for

... We considered a model for information transfer that is composed of three sub-circuits: a conductor, a student, and a tutor (see Fig. 1B). The conductor provides input to the student in the form of temporally precise patterns. The goal of learning is for the student to convert this input to a predefi ...
Thesauri and Formal Concept Analysis
Thesauri and Formal Concept Analysis

... Our doctoral student Sam Warfel undertook the study and concluded that if the Thesaurus hierarchy were regarded as having six levels (Figure 3), in a large number of cases it is safe to assume that words which occur in the same category at any level are more closely related to each other than to wo ...
Building a Constraint Solver that Learns. In Proceedings of the AAAI
Building a Constraint Solver that Learns. In Proceedings of the AAAI

... architecture for the rapid development of expertise (Epstein, 1992). To produce an adaptive, robust problem solver, FORR exploits many techniques observable in human learners. FORR itself is domain independent; a FORR-based application requires a set of domain-specific state representations and heur ...
PDF
PDF

... specifically designed for them and often with humans around. Thus, they should have the capability to adapt to new situations and cope with unexpected events. Such adaptation takes two rather different forms depending on whether it occurs at the sensorimotor or cognitive levels 29. Sensorimotor adap ...
Learning to Complete Sentences
Learning to Complete Sentences

... speed due to saved keystrokes and the time lost because of distractions is highly user-specific; any measurement of the actual time savings is a projection of these conflicting goals for a particular group of users. ...
On the effect of data set size on bias and variance in classification
On the effect of data set size on bias and variance in classification

... learning algorithms to be required to be used on increasingly large data sets - much larger than the size of data sets with which they were originally developed. Hence, machine learning algorithms will be required to perform well on very large data sets. This paper addresses the impact of this trend ...
PDF file
PDF file

... “imitation game,” now called the Turing Test, to test it. The Turing Test had greatly influenced the modern day AI research that followed [2]. Not until the 1980’s had the importance of embodiment received sufficient recognition in the AI community. The behavior-based approach, popularized by Rodney ...
Universal Artificial Intelligence: Practical Agents and Fundamental
Universal Artificial Intelligence: Practical Agents and Fundamental

... Computer programs. The solution to these questions come from a somewhat unexpected direction. In one of the greatest mathematical discoveries of the 20th century, Alan Turing invented the universal Turing machine (UTM). Essentially, a UTM can compute anything that can be computed at all. Today, the ...
Learning Planning Operators by Observation and Practice
Learning Planning Operators by Observation and Practice

... Figure 4: The secondobservation of the operator GOTO-DR The systeminitializes the ISGby setting its root to the virtual operator *finish*. The last operator in the episode must be achieving sometop-level goals. Since everything Refining Operators with Practice Because of the incorin the delta-state ...
Lecture Notes in Computer Science
Lecture Notes in Computer Science

... Fig. 1 illustrates what we are trying to accomplish; it depicts the distances of 13 examples, 5 of which belong to a class that is identified by a square and 8 belong to a different class that is identified by a circle. When using the initial distance function dinit we cannot observe too much cluste ...
Representations for Learning to Summarize Plots
Representations for Learning to Summarize Plots

... units and cross-cutting relational links The three subtasks form a pipeline in which the system successively builds up the knowledge required for the labeling of complex plot units. Affective state labeling of story events is needed to determine relational links, and relational link labels are requi ...
behavioral animation for crowd simulation
behavioral animation for crowd simulation

... the basic properties that comprise the characteristics of these agents. A full behavioral animation system should address these issues. These properties can be summarized as follows [57]: • Behavior: Response of an individual, group or species to the environment. • Intelligence: The ability to learn ...
Rewardguided learning beyond dopamine in the nucleus
Rewardguided learning beyond dopamine in the nucleus

... matters, ultimately, is what the animal actually learns, not what the experimenter believes that the animal learns, and what the animal actually learns can only be revealed by assays that directly probe the content of learning. The Pavlovian-instrumental distinction would have been trivial if the an ...
Concurrent Effect Search in Evolutionary Systems
Concurrent Effect Search in Evolutionary Systems

... be called a computational origins problem—while our physical origins have been worked out, our computational origins remain a mystery. Referring to this problem, Valiant speculates that future generations will wonder why it was not regarded with a greater sense of urgency [29]. If the computational ...
Nagai
Nagai

... Figure 1 (a)). At the 12th month, that is the second stage, the infant begins to track the caregiver’s gaze and watches the object that the caregiver attends to (see Figure 1 (b)). However, even at this stage, the infant exhibits the gaze following only when the object is within the field of the inf ...
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Concept learning

Concept learning, also known as category learning, concept attainment, and concept formation, is largely based on the works of the cognitive psychologist Jerome Bruner. Bruner, Goodnow, & Austin (1967) defined concept attainment (or concept learning) as ""the search for and listing of attributes that can be used to distinguish exemplars from non exemplars of various categories."" More simply put, concepts are the mental categories that help us classify objects, events, or ideas, building on the understanding that each object, event, or idea has a set of common relevant features. Thus, concept learning is a strategy which requires a learner to compare and contrast groups or categories that contain concept-relevant features with groups or categories that do not contain concept-relevant features.Concept learning also refers to a learning task in which a human or machine learner is trained to classify objects by being shown a set of example objects along with their class labels. The learner simplifies what has been observed by condensing it in the form of an example. This simplified version of what has been learned is then applied to future examples. Concept learning may be simple or complex because learning takes place over many areas. When a concept is difficult, it is less likely that the learner will be able to simplify, and therefore will be less likely to learn. Colloquially, the task is known as learning from examples. Most theories of concept learning are based on the storage of exemplars and avoid summarization or overt abstraction of any kind.
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