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::
Intelligent
Agent
Perception
::
Molly Maymar
Columbia University
July 2015
What is Intelligent Agent Perception?
Perception is the process by which intelligent agents
sense, perceive, and interpret...
During the perceptual process, an intelligent agent
assigns meaning to incoming visual, auditory,
olfactory, and/or tactil...
Perception entails the filtering of sensory information
[2]. Through a process called attention—by which
the agent determi...
• “Perception tends to be ambiguous and noisy.” [9]
• Noisy sensors interfere with agents’ abilities to
perceive their wor...
The agent combines perceptual data; recognition
and categorization of familiar patterns to build a
model of its domain and...
Intelligent agents sense through simple, inexpensive modalities
like temperature measurement and/or complex, expensive
mod...
The most common method for acquiring sensory
data is through sensors.
Sensor data comes in two varieties [10]:
• Data that...
Model-based robotics rely on complete and accurate,
pre-programmed models of the robot and its
environment in order for th...
Behavior-based robotics rely on immediate sensor
feedback for determining a robot’s action. This method
places physical li...
Bayes’ Theorem
Bayes’ Theorem is a rule that is used to determine
how an agent filters new data, based on previous
knowled...
Bayes Theorem is as follows [8]:
P(A|B) = P(B|A) * P(A)/P(B)
P(A) is the probability of A, independent of B
P(B) is the pr...
Molly’s Example:
An agent needs to know if an object is a cat
P(cat) is the probability of an object being a cat, whether ...
Molly’s Example:
The agent would determine the probability of whether or not
the object is a cat using the following formu...
Probabilistic Algorithms
Probabilistic algorithms, also called randomized
algorithms, account for the inherent uncertainty...
Probabilistic algorithms have proven successful with
mobile robot localization—the problem of finding out the
robot’s coor...
Probabilistic algorithms are preferable to traditional
approaches to robotic perception, like methods
associated with mode...
Artificial Neural Networks
An artificial neural network (ANN) is an information
processing technique inspired by the way t...
ANNs are comprised of interconnected firing
neurons capable of being trained. ANNs consist of
three layers: an input layer...
II. How Do Intelligent Agents Perceive?
Machine learning is the science of pattern
recognition and computational learning ...
Some Examples:
Visual Sensors + ANN
Researchers working with the iCub humanoid robot
sought to show that artificial neural...
The iCub, moving autonomously in a world, obtained visual
information from two cameras. The ANN provided the robot
with th...
Haptic Sensors + Classification Algorithms
Researchers working with the ALoF robot
(autonomous legged robot on four legs) ...
Researchers bisected each of ALoF’s legs and placed
force sensor resisters at the bisection in order to
capture haptic fee...
When trained in real-world walking force and contact
force measurement data samples, a special classification
algorithm, c...
ALoF showed that robots can use haptics, or tactile
sensory information, to identify local surface properties.
This will h...
Pressure Sensors + ANN + Offline Planning
Through the integration of sensor-based
techniques, offline planning, and an ANN...
Conventional offline planning techniques—
preprogrammed automation tasks reliant on
complete knowledge of the static envir...
In this study, humans stood on mats consisting of
pressure-activated nodes. Data collected by the
nodes was clustered into...
Researchers will use online information sensed by
the mats and interpreted by the SOM to modify
robots’ predefined motion ...
References
[1] Castelli, V. (2005). Lecture 2. Retrieved from: http://www.ee.columbia.edu/~vittorio/Lecture-2.pdf
[2] Goer...
::
Thank You
::
All Images obtained
from http://sid766.tumblr.com
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Intelligent Agent Perception

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In this presentation, I address two major questions:
1. What is Intelligent Agent Perception?
2. How do Intelligent Agents Perceive?

In addressing the meaning of agent perception, I highlight impediments to the perceptual process and the process of situation assessment.

In addressing how agents perceive, I highlight traditional approaches to robotic perception and then the next step after sensor input, which is how sensor data (vision sensor, tactile, olfactory, haptic, etc.) is interpreted. Methods for interpretation include solutions based on Bayes' Theorem, the underpinning of many robotics algorithms; Probabilistic Algorithms; and Artificial Neural Networks.

I also discuss a current system for robotic perception, designed to accommodate more robust and complex robotic needs: using sensors in tandem with machine learning. This method is closer to mimicking the human perceptual process than previous methods. I discuss some examples of this: 1) a study in which researchers used visual sensors and an artificial neural network (ANN) for robotic perception; 2) a study in which researchers used haptic sensors and a classification algorithm, called a boosting algorithm for robotic perception; and 3) a study in which researchers used pressure sensors, an ANN, and intended to add pre-programmed models in order to facilitate robotic perception.

Published in: Technology
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Intelligent Agent Perception

  1. 1. :: Intelligent Agent Perception :: Molly Maymar Columbia University July 2015
  2. 2. What is Intelligent Agent Perception? Perception is the process by which intelligent agents sense, perceive, and interpret their external worlds [8], called domains [4]. Agents use built-in sensors in order to obtain sensory data [2]. Intelligent Agent Perception | Molly Maymar | 7.10.2015
  3. 3. During the perceptual process, an intelligent agent assigns meaning to incoming visual, auditory, olfactory, and/or tactile sensory data [2]. The assignment of meaning can be bottoms-up, by which data significance is drawn directly from sensation, or top-down, by which previously perceived or programmed meanings are recalled and assigned to current data [2]. Perception can be top-down within a single cycle, in that the agent relies on recent recollections of the same data—in the same domain and within the same problem space—to inform data significance in real-time [2]. I. What is Intelligent Agent Perception? Intelligent Agent Perception | Molly Maymar | 7.10.2015
  4. 4. Perception entails the filtering of sensory information [2]. Through a process called attention—by which the agent determines how to direct its perceptual resources [4] and select the most relevant, urgent, or important information in its world—the agent ignores some sensory data while processing others [2]. The output of the perceptual process is a percept— a categorical judgment or concept that the agent has created as a result of the perceptual process [11,2]. I. What is Intelligent Agent Perception? Intelligent Agent Perception | Molly Maymar | 7.10.2015
  5. 5. • “Perception tends to be ambiguous and noisy.” [9] • Noisy sensors interfere with agents’ abilities to perceive their worlds completely and accurately [4] • Dynamic worlds [4] mean that agents cannot rely on pre-programmed models or sensory information [5] and must undertake more complex data filtering processes [2] Impediments to the Perceptual Process I. What is Intelligent Agent Perception? Intelligent Agent Perception | Molly Maymar | 7.10.2015
  6. 6. The agent combines perceptual data; recognition and categorization of familiar patterns to build a model of its domain and/or the dynamics of that domain, in order that the agent can best sense and act [4,9,2] An example is an agent using vision to detect features of its world and then using these features to determine its position within the world, as well as the obstacles it will encounter [9] Situation Assessment I. What is Intelligent Agent Perception? Intelligent Agent Perception | Molly Maymar | 7.10.2015
  7. 7. Intelligent agents sense through simple, inexpensive modalities like temperature measurement and/or complex, expensive modalities [4] like stereo vision—seeing in 3D by using two cameras as “eyes” [5]. The input from these modalities can be combined and output as a single assessment [4,11]. How Do Intelligent Agents Perceive? Intelligent Agent Perception | Molly Maymar | 7.10.2015
  8. 8. The most common method for acquiring sensory data is through sensors. Sensor data comes in two varieties [10]: • Data that characterizes the momentary situation EX: camera images, laser range scans • Data related to a change in the situation EX: controls, odometer readings II. How Do Intelligent Agents Perceive? Intelligent Agent Perception | Molly Maymar | 7.10.2015
  9. 9. Model-based robotics rely on complete and accurate, pre-programmed models of the robot and its environment in order for the robot to perceive and act. This method presents challenges in dynamic or noisy environments, which do not accurately reflect the models [10]. Traditional Approaches to Robotic Perception II. How Do Intelligent Agents Perceive? Intelligent Agent Perception | Molly Maymar | 7.10.2015
  10. 10. Behavior-based robotics rely on immediate sensor feedback for determining a robot’s action. This method places physical limits on perceptual process and poses boundaries on the types of task that can be handled using this approach [10]. II. How Do Intelligent Agents Perceive? :: Traditional Approaches Intelligent Agent Perception | Molly Maymar | 7.10.2015
  11. 11. Bayes’ Theorem Bayes’ Theorem is a rule that is used to determine how an agent filters new data, based on previous knowledge [9,8]. Bayes’ rule is an equation for showing the relationship between a conditional probability (the probability of event1, given that event2 has already occurred) and its reverse form (the probability of event2, given event1) [9]. II. How Do Intelligent Agents Perceive? How is Sensor Data Interpreted? Intelligent Agent Perception | Molly Maymar | 7.10.2015
  12. 12. Bayes Theorem is as follows [8]: P(A|B) = P(B|A) * P(A)/P(B) P(A) is the probability of A, independent of B P(B) is the probability of B, independent of A P(A|B) is the probability of A, given that B is true P(B|A) is the probability of B, given that A is true II. How Do Intelligent Agents Perceive? :: Sensor Data :: Bayes’ Rule Intelligent Agent Perception | Molly Maymar | 7.10.2015
  13. 13. Molly’s Example: An agent needs to know if an object is a cat P(cat) is the probability of an object being a cat, whether or not it has pointy ears P(ears) is the probability of an object having pointy ears, whether or not it is a cat P(cat|ears) is the probability the object is a cat, given it has pointy ears P(ears|cat) is the probability an object has pointy ears, given that it is a cat II. How Do Intelligent Agents Perceive? :: Sensor Data :: Bayes’ Rule Intelligent Agent Perception | Molly Maymar | 7.10.2015
  14. 14. Molly’s Example: The agent would determine the probability of whether or not the object is a cat using the following formula: P(cat|ears) = P(ears|cat) * P(cat)/P(ears) II. How Do Intelligent Agents Perceive? :: Sensor Data :: Bayes’ Rule Intelligent Agent Perception | Molly Maymar | 7.10.2015
  15. 15. Probabilistic Algorithms Probabilistic algorithms, also called randomized algorithms, account for the inherent uncertainty in intelligent agent perception [10]. Agents use sensors to input data from their environments. As a result of sensor limitations, noise, and environmental dynamism and unpredictability, their input is uncertain. Probabilistic algorithms process sensor data in a way that computes a probability distribution over what might be the case in the agent’s world overall, versus generating a single best guess [10]. II. How Do Intelligent Agents Perceive? :: Sensor Data Intelligent Agent Perception | Molly Maymar | 7.10.2015
  16. 16. Probabilistic algorithms have proven successful with mobile robot localization—the problem of finding out the robot’s coordinates relative to its environment—and mapping—the problem of generating maps from sensor measurements [10]. II. How Do Intelligent Agents Perceive? :: Sensor Data :: Probabilistic Algorithms Intelligent Agent Perception | Molly Maymar | 7.10.2015
  17. 17. Probabilistic algorithms are preferable to traditional approaches to robotic perception, like methods associated with model- and behavior-based robotics, in that they are more robust in the face of sensor limitations, noise, and environmental dynamics. They are computationally inefficient in that they consider entire probability densities, so they generate approximate outputs [10]. II. How Do Intelligent Agents Perceive? :: Sensor Data :: Probabilistic Algorithms Intelligent Agent Perception | Molly Maymar | 7.10.2015
  18. 18. Artificial Neural Networks An artificial neural network (ANN) is an information processing technique inspired by the way that the human nervous system processes information. In human brains, neurons receive signals—electric impulses transmitted by chemical processes—from other neurons, modify the signals, and forward them to their connections. An artificial neuron is a computational unit with many inputs and one output. It forwards signals, or “fires”, based on the input pattern received [5]. II. How Do Intelligent Agents Perceive? :: Sensor Data Intelligent Agent Perception | Molly Maymar | 7.10.2015
  19. 19. ANNs are comprised of interconnected firing neurons capable of being trained. ANNs consist of three layers: an input layer, a hidden layer, and an output layer. Each sensory input arrives in an input node in the input layer. This input is then provided to every node in the hidden layer, where each connection is assigned a weight. The output value of every hidden node is then provided to the output node, where connections are again weighted and then output as a percept [5,11,2]. II. How Do Intelligent Agents Perceive? :: Sensor Data :: Artificial Neural Networks Intelligent Agent Perception | Molly Maymar | 7.10.2015
  20. 20. II. How Do Intelligent Agents Perceive? Machine learning is the science of pattern recognition and computational learning theory in artificial intelligence. Machine learning focuses on the construction and study of algorithms that learn from and make predictions on data [6]. In order to ensure more robust robot perception in light of unpredictable, dynamic, and noisy real- world scenarios, scientists pair machine learning approaches with biologically-inspired sensor systems [5]. Sensors + Machine Learning Intelligent Agent Perception | Molly Maymar | 7.10.2015
  21. 21. Some Examples: Visual Sensors + ANN Researchers working with the iCub humanoid robot sought to show that artificial neural networks can be trained to allow humanoid robot spatial perception in Cartesian (3D) coordinates. Spatial perception is the detection and localization of objects in the robot’s world [5]. II. How Do Intelligent Agents Perceive? :: Sensors + Machine Learning Intelligent Agent Perception | Molly Maymar | 7.10.2015
  22. 22. The iCub, moving autonomously in a world, obtained visual information from two cameras. The ANN provided the robot with the ability to estimate the position of camera-identified objects relative to itself in this 3D space. The ANN was comprised of three separate neural networks—one trained to predict object position and robot poses along the X axis, one along the Y, and one along the Z. Visual input regarding image coordinates and robot poses arrived in each input node and each output layer provided an estimate of the object and robot’s position along one Cartesian axis [5]. The iCub showed that by training ANNs, artificial agents can learn the skill of estimating object locations based on visual perception [5]. II. How Do Intelligent Agents Perceive? :: Sensors + Machine Learning :: Visual + ANN Intelligent Agent Perception | Molly Maymar | 7.10.2015
  23. 23. Haptic Sensors + Classification Algorithms Researchers working with the ALoF robot (autonomous legged robot on four legs) used a classification algorithm—an algorithm that selects the best possible hypothesis based on observed data [1]—to identify the geometric texture shape and surface properties of a robot’s selected footholds based on haptic information, or tactile feedback [3]. II. How Do Intelligent Agents Perceive? :: Sensors + Machine Learning Intelligent Agent Perception | Molly Maymar | 7.10.2015
  24. 24. Researchers bisected each of ALoF’s legs and placed force sensor resisters at the bisection in order to capture haptic feedback from ALoF’s knee joint movement when it was trodding on different surfaces. The resistors registered the normal force (the force supporting the weight of ALoF’s “femur” on its “tibia”, or tibia on the floor) and moments (the force necessary to rotate the tibia on the knee/axis) in the joint. ALoF “walked” and “scratched” on artificial terrain textured separately in four geometrical shapes, ranging from a Teflon-like surface to a rocky-feeling one [3]. II. How Do Intelligent Agents Perceive? :: Sensors + Machine Learning :: Haptic + Classification Intelligent Agent Perception | Molly Maymar | 7.10.2015
  25. 25. When trained in real-world walking force and contact force measurement data samples, a special classification algorithm, called a boosting algorithm—which combines a weighted ensemble of weak classifiers into a stronger one, and is slightly more accurate than a probabilistic algorithm—was able to identify surface types based on the haptic feedback [3]. II. How Do Intelligent Agents Perceive? :: Sensors + Machine Learning :: Haptic + Classification Intelligent Agent Perception | Molly Maymar | 7.10.2015
  26. 26. ALoF showed that robots can use haptics, or tactile sensory information, to identify local surface properties. This will help robots characterize their environment while walking through it, especially when detailed environmental models have not been supplied in advance [3]. II. How Do Intelligent Agents Perceive? :: Sensors + Machine Learning :: Haptic + Classification Intelligent Agent Perception | Molly Maymar | 7.10.2015
  27. 27. Pressure Sensors + ANN + Offline Planning Through the integration of sensor-based techniques, offline planning, and an ANN, researchers sought to inform the development of a new breed of safe and intelligent robots that could share a common workspace with humans [7]. II. How Do Intelligent Agents Perceive? :: Sensors + Machine Learning Intelligent Agent Perception | Molly Maymar | 7.10.2015
  28. 28. Conventional offline planning techniques— preprogrammed automation tasks reliant on complete knowledge of the static environment— require complete knowledge about the static environment. They do not account for human motion, which is unpredictable, and unstructured or time-varying interactive environments [7]. II. How Do Intelligent Agents Perceive? :: Sensors + Machine Learning :: Sensors + ANN + Offline Intelligent Agent Perception | Molly Maymar | 7.10.2015
  29. 29. In this study, humans stood on mats consisting of pressure-activated nodes. Data collected by the nodes was clustered into subsets corresponding to each foot using a self-organizing map (SOM)—a special class of ANN that discovers salient features in time-varying data sets without any prior training. Using these subsets, each human’s body orientation and location were derived and paired with average human body dimensions in order to obtain a model of each person [7]. II. How Do Intelligent Agents Perceive? :: Sensors + Machine Learning :: Sensors + ANN + Offline Intelligent Agent Perception | Molly Maymar | 7.10.2015
  30. 30. Researchers will use online information sensed by the mats and interpreted by the SOM to modify robots’ predefined motion paths, in order that the robots can reach their intended goals, or preform their automated functions, while avoiding obstacles. This will allow robots to collaborate safely and amicably with humans [7]. II. How Do Intelligent Agents Perceive? :: Sensors + Machine Learning :: Sensors + ANN + Offline Intelligent Agent Perception | Molly Maymar | 7.10.2015
  31. 31. References [1] Castelli, V. (2005). Lecture 2. Retrieved from: http://www.ee.columbia.edu/~vittorio/Lecture-2.pdf [2] Goertzel, B., & Wang, P. (2007). A foundational architecture for artificial general intelligence. Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms, 6, 36. [3] Höpflinger, M., Remy, C. D., Hutter, M., Spinello, L., & Siegwart, R. (2010, May). Haptic terrain classification for legged robots. In Robotics and Automation (ICRA), 2010 IEEE International Conference on (pp. 2828-2833). IEEE. [4] Langley, P., Laird, J. E., & Rogers, S. (2009). Cognitive architectures: Research issues and challenges. Cognitive Systems Research, 10(2), 141-160 [5] Leitner, J., Harding, S., Frank, M., Forster, A., & Schmidhuber, J. (n.d.). Artificial neural networks for spatial perception: Towards visual object localisation in humanoid robots. The 2013 International Joint Conference on Neural Networks (IJCNN). [6] Machine Learning (2015, July 3). Wikipedia. Retrieved from: https://en.wikipedia.org/wiki/Machine_learning [7] Najmaei, N., & Kermani, M. R. (2011). Applications of artificial intelligence in safe human–robot interactions. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on, 41(2) 448-459. [8] Nilsson, N. J. (2009). The quest for artificial intelligence. Cambridge University Press. [9] Poole, D. L., & Mackworth, A. K. (2010). Artificial Intelligence: foundations of computational agents. Cambridge University Press. [10] Thrun, S. (2000). Probabilistic algorithms in robotics. Ai Magazine, 21(4), 93. [11] Wang, P. (2014). Perception. Introduction to artificial intelligence [online lecture notes]. Retrieved from: http://www.cis.temple.edu/~pwang/3203-AI/Lecture/IO-2.htm
  32. 32. :: Thank You :: All Images obtained from http://sid766.tumblr.com

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