New Cognitive Robotics Lab Tests Theories of Human Thought

"The real world has a lot of inconsistency that humans handle almost without noticing -- for example, we walk on uneven terrain, we see in shifting light," said Professor Vladislav Daniel Veksler, who is currently teaching Cognitive Robotics."With robots, we can see the problems humans face when navigating their environment."

Cognitive Robotics marries the study of cognitive science -- how the brain represents and transforms information -- with the challenges of a physical environment. Advances in cognitive robotics transfer to artificial intelligence, which seeks to develop more efficient computer systems patterned on the versatility of human thought.

Professor Bram Van Heuveln, who organized the lab, said cognitive scientists have developed a suite of elements -- perception/action, planning, reasoning, memory, decision-making -- that are believed to constitute human thought. When properly modeled and connected, those elements are capable of solving complex problems without the raw power required by precise mathematical computations.

"Suppose we wanted to build a robot to catch fly balls in an outfield. There are two approaches: one uses a lot of calculations -- Newton's law, mechanics, trigonometry, calculus -- to get the robot to be in the right spot at the right time," said Van Heuveln."But that's not the way humans do it. We just keep moving toward the ball. It's a very simple solution that doesn't involve a lot of computation but it gets the job done."

Robotics are an ideal testing ground for that principle because robots act in the real world, and a correct cognitive solution will withstand the unexpected variables presented by the real world.

"The physical world can help us to drive science because it's different from any simulated world we could come up with -- the camera shakes, the motors slip, there's friction, the light changes," Veksler said."This platform -- robotics -- allows us to see that you can't rely on calculations. You have to be adaptive."

The lab is open to all students at Rensselaer. In its first semester, the lab has largely attracted computer science and cognitive science students enrolled in a Cognitive Robotics course taught by Veksler, but Veksler and Van Heuveln hope it will attract more engineering and art students as word of the facility spreads.

"We want different students together in one space -- a place where we can bring the different disciplines and perspectives together," said Van Heuveln."I would like students to use this space for independent research: they come up with the research project, they say 'let's look at this.'"

The lab is equipped with five"Create" robots -- essentially a Roomba robotic vacuum cleaner paired with a laptop; three hand-eye systems; one Chiara (which looks like a large metal crab); and 10 LEGO robots paired with the Sony Handy Board robotic controller.

On a recent day, Jacqui Brunelli and Benno Lee were working on their robot"cat" and"mouse" pair, which try to chase and evade each other respectively; Shane Reilly was improving the computer"vision" of his robotic arm; and Ben Ball was programming his robot to maintain a fixed distance from a pink object waved in front of its"eye."

"The thing that I've learned is that the sensor data isn't exact -- what it 'sees' constantly changes by a few pixels -- and to try to go by that isn't going to work," said Ball, a junior and student of computer science and physics.

Ball said he is trying to pattern his robot on a more human approach.

"We don't just look at an object and walk toward it. We check our position, adjusting our course," Ball said."I need to devise an iterative approach where the robot looks at something, then moves, then looks again to check its results."

The work of the students, who program their robots with the Tekkotsu open-source software, could be applied in future projects, said Van Heuveln.

"As a cognitive scientist, I want this to be built on elements that are cognitively plausible and that are recyclable -- parts of cognition that I can apply to other solutions as well," said Van Heuveln."To me, that's a heck of a lot more interesting than the computational solution."

In a generic domain, their early investigations clearly show how a more cognitive approach employing limited resources can easily outpace more powerful computers using a brute force approach, said Veksler.

"We look to humans not just because we want to simulate what we do, which is an interesting problem in itself, but also because we're smart," said Veksler."Some of the things we have, like limited working memory -- which may seem like a bad thing -- are actually optimal for solving problems in our environment. If you remembered everything, how would you know what's important?"

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Using Chaos to Model Geophysical Phenomena

"Geophysical phenomena are still not fully understood, especially in turbulent regimes," explains Gary Froyland at the School of Mathematics and Statistics and the Australian Research Council Centre of Excellence for Mathematics and Statistics of Complex Systems (MASCOS) at the University of New South Wales in Australia.

"Nevertheless, it is very important that scientists can quantify the 'transport' properties of these geophysical systems: Put very simply, how does a packet of air or water get from A to B, and how large are these packets? An example of one of these packets is the Antarctic polar vortex, a rotating mass of air in the stratosphere above Antarctica that traps chemicals such as ozone and chlorofluorocarbons (CFCs), exacerbating the effect of the CFCs on the ozone hole," Froyland says.

In the American Institute of Physics' journalCHAOS, Froyland and his research team, including colleague Adam Monahan from the School of Earth and Ocean Sciences at the University of Victoria in Canada, describe how they developed the first direct approach for identifying these packets, called"coherent sets" due to their nondispersive properties.

This technique is based on so-called"transfer operators," which represent a complete description of the ensemble evolution of the fluid. The transfer operator approach is very simple to implement, they say, requiring only singular vector computations of a matrix of transitions induced by the dynamics.

When tested using European Centre for Medium Range Weather Forecasting (ECMWF) data, they found that their new methodology was significantly better than existing technologies for identifying the location and transport properties of the vortex.

The transport operator methodology has myriad applications in atmospheric science and physical oceanography to discover the main transport pathways in the atmosphere and oceans, and to quantify the transport."As atmosphere-ocean models continue to increase in resolution with improved computing power, the analysis and understanding of these models with techniques such as transfer operators must be undertaken beyond pure simulation," says Froyland.

Their next application will be the Agulhas rings off the South African coast, because the rings are responsible for a significant amount of transport of warm water and salt between the Indian and Atlantic Oceans.

Disclaimer: Views expressed in this article do not necessarily reflect those of ScienceDaily or its staff.

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New Psychology Theory Enables Computers to Mimic Human Creativity

Solving this"insight problem" requires creativity, a skill at which humans excel (the coin is a fake --"B.C." and Arabic numerals did not exist at the time) and computers do not. Now, a new explanation of how humans solve problems creatively -- including the mathematical formulations for facilitating the incorporation of the theory in artificial intelligence programs -- provides a roadmap to building systems that perform like humans at the task.

Ron Sun, Rensselaer Polytechnic Institute professor of cognitive science, said the new"Explicit-Implicit Interaction Theory," recently introduced in an article inPsychological Review, could be used for future artificial intelligence.

"As a psychological theory, this theory pushes forward the field of research on creative problem solving and offers an explanation of the human mind and how we solve problems creatively," Sun said."But this model can also be used as the basis for creating future artificial intelligence programs that are good at solving problems creatively."

The paper, titled"Incubation, Insight, and Creative Problem Solving: A Unified Theory and a Connectionist Model," by Sun and Sèbastien Hèlie of University of California, Santa Barbara, appeared in the July edition ofPsychological Review. Discussion of the theory is accompanied by mathematical specifications for the"CLARION" cognitive architecture -- a computer program developed by Sun's research group to act like a cognitive system -- as well as successful computer simulations of the theory.

In the paper, Sun and Hèlie compare the performance of the CLARION model using"Explicit-Implicit Interaction" theory with results from previous human trials -- including tests involving the coin question -- and found results to be nearly identical in several aspects of problem solving.

In the tests involving the coin question, human subjects were given a chance to respond after being interrupted either to discuss their thought process or to work on an unrelated task. In that experiment, 35.6 percent of participants answered correctly after discussing their thinking, while 45.8 percent of participants answered correctly after working on another task.

In 5,000 runs of the CLARION program set for similar interruptions, CLARION answered correctly 35.3 percent of the time in the first instance, and 45.3 percent of the time in the second instance.

"The simulation data matches the human data very well," said Sun.

Explicit-Implicit Interaction theory is the most recent advance on a well-regarded outline of creative problem solving known as"Stage Decomposition," developed by Graham Wallas in his seminal 1926 book"The Art of Thought." According to stage decomposition, humans go through four stages -- preparation, incubation, insight (illumination), and verification -- in solving problems creatively.

Building on Wallas' work, several disparate theories have since been advanced to explain the specific processes used by the human mind during the stages of incubation and insight. Competing theories propose that incubation -- a period away from deliberative work -- is a time of recovery from fatigue of deliberative work, an opportunity for the mind to work unconsciously on the problem, a time during which the mind discards false assumptions, or a time in which solutions to similar problems are retrieved from memory, among other ideas.

Each theory can be represented mathematically in artificial intelligence models. However, most models choose between theories rather than seeking to incorporate multiple theories and therefore they are fragmentary at best.

Sun and Hèlie's Explicit-Implicit Interaction (EII) theory integrates several of the competing theories into a larger equation.

"EII unifies a lot of fragmentary pre-existing theories," Sun said."These pre-existing theories only account for some aspects of creative problem solving, but not in a unified way. EII unifies those fragments and provides a more coherent, more complete theory."

The basic principles of EII propose the coexistence of two different types of knowledge and processing: explicit and implicit. Explicit knowledge is easier to access and verbalize, can be rendered symbolically, and requires more attention to process. Implicit knowledge is relatively inaccessible, harder to verbalize, and is more vague and requires less attention to process.

In solving a problem, explicit knowledge could be the knowledge used in reasoning, deliberately thinking through different options, while implicit knowledge is the intuition that gives rise to a solution suddenly. Both types of knowledge are involved simultaneously to solve a problem and reinforce each other in the process. By including this principle in each step, Sun was able to achieve a successful system.

"This tells us how creative problem solving may emerge from the interaction of explicit and implicit cognitive processes; why both types of processes are necessary for creative problem solving, as well as in many other psychological domains and functionalities," said Sun.

Disclaimer: This article is not intended to provide medical advice, diagnosis or treatment. Views expressed here do not necessarily reflect those of ScienceDaily or its staff.

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Genomic Fault Zones Come and Go: Fragile Regions in Mammalian Genomes Go Through 'Birth and Death' Process

"The genomic architecture of every species on Earth changes on the evolutionary time scale and humans are not an exception. What will be the next big change in the human genome remains unknown, but our approach could be useful in determining where in the human genome those changes may occur," said Pavel Pevzner, a UC San Diego computer science professor and an author on the new study. Pevzner studies genomes and genome evolution from a computational perspective in the Department of Computer Science and Engineering at the UC San Diego Jacobs School of Engineering.

The fragile regions of genomes are prone to"genomic earthquakes" that can trigger chromosome rearrangements, disrupt genes, alter gene regulation and otherwise play an important role in genome evolution and the emergence of new species. For example, humans have 23 chromosomes while some other apes have 24 chromosomes, a consequence of a genome rearrangement that fused two chromosomes in our ape ancestor into human chromosome 2.

This work was performed by Pevzner and Max Alekseyev -- a computer scientist who recently finished his Ph.D. in the Department of Computer Science and Engineering at the UC San Diego Jacobs School of Engineering. Alekseyev is now a computer science professor at the University of South Carolina.

Turnover Fragile Breakage Model

"The main conclusion of the new paper is that these fragile regions are moving," said Pevzner.

In 2003, Pevzner and UC San Diego mathematics professor Glen Tesler published results claiming that genomes have"fault zones" or genomic regions that are more prone to rearrangements than other regions. Their"Fragile Breakage Model" countered the then largely accepted"Random Breakage Model" -- which implies that there are no rearrangement hotspots in mammalian genomes. While the Fragile Breakage Model has been supported by many studies in the last seven years, the precise locations of fragile regions in the human genome remain elusive.

The new work published inGenome Biologyoffers an update to the Fragile Breakage Model called the"Turnover Fragile Breakage Model." The findings demonstrate that the fragile regions undergo a birth and death process over evolutionary timescales and provide a clue to where the fragile regions in the human genome are located.

Do the Math: Find Fragile Regions

Finding the fragile regions within genomes is akin to looking at a mixed up deck of cards and trying to determine how many times it has been shuffled.

Looking at a genome, you may identify breaks, but to say it is a fragile region, you have to know that breaks occurred more than once at the same genomic position."We are figuring out which regions underwent multiple genome earthquakes by analyzing the present-day genomes that survived these earthquakes that happened millions of years ago. The notion of rearrangements cannot be applied to a single genome at a single point in time. It's relevant when looking at more than one genome," said Pevzner, explaining the comparative genomics approach they took.

"It was noticed that while fragile regions may be shared across different genomes, most often such shared fragile regions are found in evolutionarily close genomes. This observation led us to a conclusion that fragility of any particular genomic position may appear only for a limited amount of time. The newly proposed Turnover Fragile Breakage Model postulates that fragile regions are subject to a 'birth and death' process and thus have limited lifespan," explained Alekseyev.

The Turnover Fragile Breakage Model suggests that genome rearrangements are more likely to occur at the sites where rearrangements have recently occurred -- and that these rearrangement sites change over tens of millions of years. Thus, the best clue to the current locations of fragile regions in the human genome is offered by rearrangements that happened in our closest ancestors -- chimpanzee and other primates.

Pevzner is eagerly awaiting sequenced primate genomes from the Genome 10K Project. Sequencing the genomes of 10,000 vertebrate species -- including 100s of primates -- is bound to provide new insights on human evolutionary history and possibly even the future rearrangements in the human genome.

"The most likely future rearrangements in human genome will happen at the sites that were recently disrupted in primates," said Pevzner.

Work tied to the new Turnover Fragile Breakage Model may also be useful for understanding genome rearrangements at the level of individuals, rather than entire species. In the future, the computer scientists hope to use similar tools to look at the chromosomal rearrangements that occur within the cells of individual cancer patients over and over again in order to develop new cancer diagnostics and drugs.

Pavel Pevzner is the Ronald R. Taylor Professor of Computer Science at UC San Diego; Director of the NIH Center for Computational Mass Spectrometry; and a Howard Hughes Medical Institute (HHMI) Professor.

Disclaimer: Views expressed in this article do not necessarily reflect those of ScienceDaily or its staff.

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