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CHAPTER THIRTEEN
The Artificial Intelligence II: Embodiments and Robotics
The IA Paradigm
Intelligent Systems (and agents) are built on three
basic ideas.

modeling aspects of biological systems (including)
human reasoning
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abstracting general principles of intelligent behavior
application of concepts to the design of
“intelligent” systems.
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Modeling Aspects of Biological Systems

Biology is the study of living organisms and
provides direct inspiration to automatically find
solutions to optimization problems.
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We can: improve mechanical design of the products
that we use; discover innovative computer software;
design electronic circuits.

Evolutionary computation models follow directly from
natural evolution (diversity, survival, inheritance, and a
selection process).
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Abstracting General Principles of IAs
Embodiment : in regard to IAs this refers to the
interaction with the environment through a physical
body (robot).
Situated. existence of an agent in a dynamic,
rapidly changing environment which the entity can
manipulate.
(The distinction:
being situated emphasizes the view that intelligent
behavior follows from the environment and the
agent’s interactions with it :
the embodiment is defined by the interactions with
the environment itself. )
Applying the principles to
the design of IAs
IAs include components that are considered critical in
mainstream psychology. They are also well thoughtout elements of human intelligence and summarized in
the table on the next slide – Behavioral Elements of
Human Intelligence.
Element of
Intelligence
Trial-and-error
learning
Rote learning
Notes
Responses to external error that lead to satisfactory results will be repeated
when the same stimuli are encountered: chess moves
Direct association between stimulus and response: memorizing a list of items.
Operant conditioning
Highly developed form of learning involving positive and negative
reinforcements of behaviors: gradually slowing a car at a red light. See Chapter
3 (Psychological Approach).
Reasoning
The ability to draw inferences appropriate to the facts and the situation. It is
embodied in logical thinking and includes deductive, inductive, and abductive
reasoning: Expert Systems – given the symptoms of computer failure, determine
what needs to be repaired (or replaced).
Problem solving
Perception
Language
Special-purpose solution relies on the circumstances that pertain to the task;
General-purpose solutions: can be applied to a broad variety of problems:
Means-ends analysis: the machine assesses the current state of the
system and chooses an action that will reduce the difference between the
current state and the goal state.
The “intelligence” scans the environment using the sensory equipment it has at its
disposal; the information is processed by internal processing mechanisms and
converted to a set of abstractions that is made up of some combination of objects,
features, and relationships.
Communication between human and machine.
The Cognitive IA Model After
Russell/Norvig
Performance standard
percepts
SENSORS
CRITIC
Learning and
knowledge
Feedback
Environment
Learning goals
LEARNING
COGNITIVE PERFORMANCE
ELEMENT
actions
RECOMMENDATIONS
ACTUATORS
How would the “GPS” develop a travel plan?
Using the street map below, develop a plan to go
from the library to the university.
(Arrows indicate one-way street directions.)
An IA includes Facts, Rules, Algorithms.
The Rules consist of IF-THEN assertions; If the antecedent (IF) is satisfied,
the consequent (THEN) is invoked. For the problem on the previous slide, the
IF-THEN rules follow:
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If (at “library”) Then (follow one-way street to “Hospital”)
If (at “library”) Then (continue in same direction to next intersection)
If (at “intersection”) Then (turn right and continue towards “school”)
If (at “school”) then (continue in same direction to “factory”)
If (at “hospital”) Then (continue in same direction to “newsstand”)
If (at “hospital”) Then (turn right and continue to “Park”)
If (at “newsstand”) Then (turn right and proceed to “university”)
If (at “newsstand”) Then (continue in same direction to “church”)
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“Blind” searches can determine the directions.
Blind searches contain no ancillary information about the problem.
Searches simply follow the IF-THEN rules. These may be accomplished in
what are called “depth first” or “breadth-first” methods.

Software samples are readily available on the internet. A depth-first
solution is shown on the next slide with the successful search shown in red.
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A limitation with depth-first solutions is that an optimum solution may not be
found – the search is over once the first solution is available.
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Depth-first simulated search for finding directions
A sample TSP problem
A sequence for the cities is
bold (i.e., A .> C > B > E >
G > F > D > A)
A
B
C
E
D
F
G
The TSP Solution
Use the Evolutionary Computing Concept; a highly simplified diagram
for a solution follows:
Arbitrary
sequence
Calculate distance
Perturb sequence
Calculate distance
New < old
Perturb the city sequence by interchanging cities; if the
cost is reduced the perturbation is the now the route;
continue this until no lower cost is achieved. Keep a list of
routes so that old routes are not repeated.
Expert Systems
Employs knowledge (facts); logic ( rules, primarily in the form of an
IF-THEN calculus ); processes (algorithms for combining facts and
rules)
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WebMD is a popular Expert System that is freely available on the
internet.
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Challenges to Expert Systems: equivocation of facts; testing of the
knowledge base may be difficult and unrealistic; “experts” disagree
regarding what is important or may overlook information that they
take for granted.
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To overcome some limitations, Expert Systems may include statistical
information (“there is an 80% chance that the bacteria is chicken pox”)
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The CYC project seeks to expand knowledge to include
metaknowledge with commonplace facts known to all.
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Why not build a brain?
Blue Brain Project: started after 15 years of systematically dissecting the microanatomical,
genetic and electrical properties of the elementary unit of the neocortex .
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15,000 experiments in rat somatosensory cortex
first milestone (2006): creating, validating, and researching the neocortical column of a rat
[brain]. This is the smallest functional unit of the neocortex which was described in connection with
Hawkins’ work and is posited to be the part of the brain responsible for higher functions such as
conscious thought.
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accomplished using a supercomputer that was able to represent ANNs but with an added
advantage of being able to simulate biologically realistic neuronal models.
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a more challenging phase aimed to: simplify the column simulation leading to simulation of
connected columns. This will culminate in being able to simulate a whole neocortex; in humans this is
about 1 million such columns.
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Completing this simulation has the potential to lead to an understanding of the very nature of
consciousness itself.
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Robotic Embodiments – Ultimate IAs
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robot – mechanical embodiment of an IA that can function autonomously.
“autonomously” function with help from a human operator; is able to adapt
to changing circumstances in the world that it inhabits; continue to work if a
part breaks just as a human can continue to function if we only break a
finger; is able to navigate and interact with the changes in world
circumstances.
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Robotic Models
Siegwart and Nourbakhsh (2004): traditional aspects of the AI associated with IAs
such as: an internal model of the world that does robotic planning and execution of
locomotion based on that model.
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Rodney Brooks (1999): subsumptive approach to IA design with its development of
complex IAs based on integration of simple, “non-intelligent” components
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Cynthia Breazeal: “social robots” particularly those imbued with “emotion”.
Ronald Arkin (Arkin, 1998): behavior-based reactive control and action-oriented
perception for mobile robots and unmanned aerial vehicles. In this regard consider the
“moral issues” that robotic development might engender (i.e., moral dilemmas faced
by an automata on the battlefield regarding the taking of human life).
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Hierarchical Architecture
Includes
Sensors and
Feature
extraction
SENSE
Creates a
model, a plan to
complete a task:
Produces
commands for
the actuators
PLAN
Controls
actuators
ACT
The environment is
modified.
Reactive Architecture after Brooks
Third primitive behavior
sensor 1
Second primitive behavior
environment
sense
act
sensor 2
Primitive behavior
Simulating Robotic Planning
Goal
Obstacles (walls)
Robot track
Start
The robot senses and avoids the obstacles and arrives at the goal
The robot is trapped in a box
box
Robot track
Start
goal
Challenges and Prospects for Implementing IAs
Computers are not close to achieving the perceptive, finely-honed
reasoning and manipulative capabilities of adult humans
.
 Machines can currently demonstrate the intellect of a low order insect.
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The more we try to replicate human intelligence the more we may learn
to understand human intelligence.
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Designing computer-based machines that are intelligent is not the same
as building computers that simulate intelligence.
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We should not be obsessed with mimicking human intelligence.
Neural interfacing is an emerging technology that will permit us to
directly control actuators using electromyography.
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