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Lecture 1
The Intelligent Agent Framework
Friday 22 August 2003
William H. Hsu
Department of Computing and Information Sciences, KSU
http://www.kddresearch.org
http://www.cis.ksu.edu/~bhsu
Reading for Next Class:
Chapter 2, Russell and Norvig
CIS 730: Introduction to Artificial Intelligence
Kansas State University
Department of Computing and Information Sciences
Lecture Outline
•
Today’s Reading: Chapter 2, Russell and Norvig
•
Intelligent Agent (IA) Design
– Shared requirements, characteristics of IAs
– Methodologies
• Software agents
• Reactivity vs. state
• Knowledge, inference, and uncertainty
•
Intelligent Agent Frameworks
– Reactive
– With state
– Goal-based
– Utility-based
•
Thursday: Problem Solving and Search
– State space search handout (Winston)
– Search handout (Ginsberg)
CIS 730: Introduction to Artificial Intelligence
Kansas State University
Department of Computing and Information Sciences
Why Study Artificial Intelligence?
•
New Computational Capabilities
– Advances in uncertain reasoning, knowledge representations
– Learning to act: robot planning, control optimization, decision support
– Database mining: converting (technical) records into knowledge
– Self-customizing programs: learning news filters, adaptive monitors
– Applications that are hard to program: automated driving, speech recognition
•
Better Understanding of Human Cognition
– Cognitive science: theories of knowledge acquisition (e.g., through practice)
– Performance elements: reasoning (inference) and recommender systems
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Time is Right
– Recent progress in algorithms and theory
– Rapidly growing volume of online data from various sources
– Available computational power
– Growth and interest of AI-based industries (e.g., data mining/KDD, planning)
CIS 730: Introduction to Artificial Intelligence
Kansas State University
Department of Computing and Information Sciences
Relevant Disciplines
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Machine Learning
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Bayesian Methods
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Cognitive Science
•
Computational Complexity Theory
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Control Theory
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Economics
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Neuroscience
•
Philosophy
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Psychology
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Statistics
Game Theory
Utility Theory
Decision Models
Planning, Design
Optimization
Meta-Learning
PAC Formalism
Mistake Bounds
Inference
NLP / Learning
Bayes’s Theorem
Missing Data Estimators
Artificial
Intelligence
Symbolic Representation
Planning/Problem Solving
Knowledge-Guided Learning
Bias/Variance Formalism
Confidence Intervals
Hypothesis Testing
Power Law of Practice
Heuristics
Logical Foundations
Consciousness
CIS 730: Introduction to Artificial Intelligence
ANN Models
Learning
Kansas State University
Department of Computing and Information Sciences
Application:
Knowledge Discovery in Databases
CIS 730: Introduction to Artificial Intelligence
Kansas State University
Department of Computing and Information Sciences
Text Mining:
Information Retrieval and Filtering
•
20 USENET Newsgroups
–
comp.graphics
misc.forsale
soc.religion.christian sci.space
–
comp.os.ms-windows.misc
rec.autos
talk.politics.guns
–
comp.sys.ibm.pc.hardware
rec.motorcycles
talk.politics.mideast sci.electronics
–
comp.sys.mac.hardware
rec.sports.baseball
talk.politics.misc
–
comp.windows.x
rec.sports.hockey
talk.religion.misc
–
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sci.crypt
sci.med
alt.atheism
Problem Definition [Joachims, 1996]
– Given: 1000 training documents (posts) from each group
– Return: classifier for new documents that identifies the group it belongs to
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Example: Recent Article from comp.graphics.algorithms
Hi all
I'm writing an adaptive marching cube algorithm, which must deal with cracks. I got the vertices of the
cracks in a list (one list per crack).
Does there exist an algorithm to triangulate a concave polygon ? Or how can I bisect the polygon so, that I
get a set of connected convex polygons.
The cases of occuring polygons are these:
...
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Performance of Newsweeder (Naïve Bayes): 89% Accuracy
CIS 730: Introduction to Artificial Intelligence
Kansas State University
Department of Computing and Information Sciences
Artificial Intelligence:
Some Problems and Methodologies
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Problem Solving
– Classical search and planning
– Game-theoretic models
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Making Decisions under Uncertainty
– Uncertain reasoning, decision support, decision-theoretic planning
– Probabilistic and logical knowledge representations
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Pattern Classification and Analysis
– Pattern recognition and machine vision
– Connectionist models: artificial neural networks (ANNs), other graphical models
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Data Mining and Knowledge Discovery in Databases (KDD)
– Framework for optimization and machine learning
– Soft computing: evolutionary algorithms, ANNs, probabilistic reasoning
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Combining Symbolic and Numerical AI
– Role of knowledge and automated deduction
– Ramifications for cognitive science and computational sciences
CIS 730: Introduction to Artificial Intelligence
Kansas State University
Department of Computing and Information Sciences
A Generic
Intelligent Agent Model
Agent
Sensors
Observations
Knowledge about World
Predictions
Knowledge about Actions
Expected
Rewards
Preferences
Environment
Internal Model (if any)
Action
Effectors
CIS 730: Introduction to Artificial Intelligence
Kansas State University
Department of Computing and Information Sciences
Term Project Guidelines
•
Due: 08 Dec 2004
– Submit using new script (procedure to be announced on class web board)
– Writeup must be turned in on (for peer review)
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Team Projects
– Work in pairs (preferred) or individually
– Topic selection and proposal due 17 Sep 2004
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Grading: 200 points (out of 1000)
– Proposal: 15 points
– Originality and significance: 25 points
– Completeness: 50 points
• Functionality (20 points)
• Quality of code (20 points)
• Documentation (10 points)
– Individual or team contribution: 50 points
– Writeup: 40 points
– Peer review: 20 points
CIS 730: Introduction to Artificial Intelligence
Kansas State University
Department of Computing and Information Sciences
Term Project Topics
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Intelligent Agents
– Game-playing: rogue-like (Nethack, Angband, etc.); reinforcement learning
– Multi-Agent Systems and simulations; robotic soccer (e.g., Teambots)
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Probabilistic Reasoning and Expert Systems
– Learning structure of graphical models (Bayesian networks)
– Application of Bayesian network inference
• Plan recognition, user modeling
• Medical diagnosis
– Decision networks or other utility models
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Probabilistic Reasoning and Expert Systems
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Constraint Satisfaction Problems (CSP)
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Soft Computing for Optimization
– Evolutionary computation, genetic programming, evolvable hardware
– Probabilistic and fuzzy approaches
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Game Theory
CIS 730: Introduction to Artificial Intelligence
Kansas State University
Department of Computing and Information Sciences
Homework 1:
Machine Problem
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Due: 10 Sep 2004
– Submit using new script (procedure to be announced on class web board)
– HW page: http://www.kddresearch.org/Courses/Fall-2004/CIS730/Homework
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Machine Problem: Uninformed (Blind) vs. Informed (Heuristic) Search
– Problem specification (see HW page for MP document)
• Description: load, search graph
• Algorithms: depth-first, breadth-first, branch-and-bound, A* search
• Extra credit: hill-climbing, beam search
– Languages: options
• Imperative programming language of your choice (C/C++, Java preferred)
• Functional PL or style (Haskell, Scheme, LISP, Standard ML)
• Logic program (Prolog)
– MP guidelines
• Work individually
• Generate standard output files and test against partial standard solution
– See also: state space, constraint satisfaction problems
CIS 730: Introduction to Artificial Intelligence
Kansas State University
Department of Computing and Information Sciences
Intelligent Agents:
Overview
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Agent: Definition
– Any entity that perceives its environment through sensors and acts upon that
environment through effectors
– Examples (class discussion): human, robotic, software agents
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Perception
– Signal from environment
– May exceed sensory capacity
•
Sensors
– Acquires percepts
– Possible limitations
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Action
– Attempts to affect environment
– Usually exceeds effector capacity
•
Effectors
– Transmits actions
– Possible limitations
CIS 730: Introduction to Artificial Intelligence
Kansas State University
Department of Computing and Information Sciences
How Agents Should Act
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Rational Agent: Definition
– Informal: “does the right thing, given what it believes from what it perceives”
– What is “the right thing”?
• First approximation: action that maximizes success of agent
• Limitations to this definition?
– Issues to be addressed now
• How to evaluate success
• When to evaluate success
– Issues to be addressed later in this course
• Uncertainty (in environment, in actions)
• How to express beliefs, knowledge
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Why Study Rationality?
– Recall: aspects of intelligent behavior (last lecture)
• Engineering objectives: optimization, problem solving, decision support
• Scientific objectives: modeling correct inference, learning, planning
– Rational cognition: formulating plausible beliefs, conclusions
– Rational action: “doing the right thing” given beliefs
CIS 730: Introduction to Artificial Intelligence
Kansas State University
Department of Computing and Information Sciences
Rational Agents
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“Doing the Right Thing”
– Committing actions
• Limited to set of effectors
• In context of what agent knows
– Specification (cf. software specification)
• Preconditions, postconditions of operators
• Caveat: not always perfectly known (for given environment)
• Agent may also have limited knowledge of specification
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Agent Capabilities: Requirements
– Choice: select actions (and carry them out)
– Knowledge: represent knowledge about environment
– Perception: capability to sense environment
– Criterion: performance measure to define degree of success
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Possible Additional Capabilities
– Memory (internal model of state of the world)
– Knowledge about effectors, reasoning process (reflexive reasoning)
CIS 730: Introduction to Artificial Intelligence
Kansas State University
Department of Computing and Information Sciences
Measuring Performance
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Performance Measure: How to Determine Degree of Sucesss
– Definition: criteria that determine how successful agent is
– Clearly, varies over
• Agents
• Environments
– Possible measures?
• Subjective (agent may not have capability to give accurate answer!)
• Objective: outside observation
– Example: web crawling agent
• Number of hits
• Number of relevant hits
• Ratio of relevant hits to pages explored, resources expended
• Caveat: “you get what you ask for” (issues: redundancy, etc.)
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When to Evaluate Success
– Depends on objectives (short-term efficiency, consistency, etc.)
– Is task episodic? Are there milestones? Reinforcements? (e.g., games)
CIS 730: Introduction to Artificial Intelligence
Kansas State University
Department of Computing and Information Sciences
Knowledge in Agents
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Rationality versus Omniscience
– Nota Bene (NB): not the same
– Distinction
• Omniscience: knowing actual outcome of all actions
• Rationality: knowing plausible outcome of all actions
• Example: is crossing the street to greet a friend too risky?
– Key question in AI
• What is a plausible outcome?
• Especially important in knowledge-based expert systems
• Of practical important in planning, machine learning
– Related questions
• How can an agent make rational decisions given beliefs about outcomes of
actions?
• Specifically, what does it mean (algorithmically) to “choose the best”?
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Limitations of Rationality
– Based only on what agent can perceive and do
– Based on what is “likely” to be right, not what “turns out” to be right
CIS 730: Introduction to Artificial Intelligence
Kansas State University
Department of Computing and Information Sciences
What Is Rational?
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Criteria
– Determines what is rational at any given time
– Varies with agent, environment, situation
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Performance Measure
– Specified by outside observer or evaluator
– Applied (consistently) to (one or more) IAs in given environment
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Percept Sequence
– Definition: entire history of percepts gathered by agent
– NB: may or may not be retained fully by agent (issue: state and memory)
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Agent Knowledge
– Of environment – “required”
– Of self (reflexive reasoning)
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Feasible Action
– What can be performed
– What agent believes it can attempt?
CIS 730: Introduction to Artificial Intelligence
Kansas State University
Department of Computing and Information Sciences
Ideal Rationality
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Ideal Rational Agent
– Given: any possible percept sequence
– Do: ideal rational behavior
• Whatever action is expected to maximize performance measure
• NB: expectation – informal sense (for now); mathematical foundation soon
– Basis for action
• Evidence provided by percept sequence
• Built-in knowledge possessed by the agent
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Ideal Mapping from Percepts to Actions
– Figure 2.2, R&N
– Mapping p: percept sequence  action
• Representing p as list of pairs: infinite (unless explicitly bounded)
• Using p: specifies ideal mapping from percepts to actions (i.e., ideal agent)
• Finding explicit p: in principle, could use trial and error
• Other (implicit) representations may be easier to acquire!
CIS 730: Introduction to Artificial Intelligence
Kansas State University
Department of Computing and Information Sciences
Structure of Intelligent Agents
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Agent Behavior
– Given: sequence of percepts
– Return: IA’s actions
• Simulator: description of results of actions
• Real-world system: committed action
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Agent Programs
– Functions that implement p
– Assumed to run in computing environment (architecture)
• Hardware architecture: computer organization
• Software architecture: programming languages, operating systems
– Agent = architecture + program
• This course (CIS730): primarily concerned with p
• CIS540, 740, 748: concerned with architecture
• See also: Chapter 24 (Vision), 25 (Robotics), R&N
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Discussion: “Real” versus “Artificial” Environments
CIS 730: Introduction to Artificial Intelligence
Kansas State University
Department of Computing and Information Sciences
Agent Programs
•
Software Agents
– Also known as (aka) software robots, softbots
– Typically exist in very detailed, unlimited domains
– Example
• (Real-time) critiquing, automation of avionics, shipboard damage control
• Indexing (spider), information retrieval (IR; e.g., web crawlers) agents
• Plan recognition systems (computer security, fraud detection monitors)
– See: Bradshaw (Software Agents)
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Focus of This Course: Building IAs
– Generic skeleton agent: Figure 2.4, R&N
– function SkeletonAgent (percept) returns action
• static: memory, agent’s memory of the world
• memory  Update-Memory (memory, percept)
• action  Choose-Best-Action (memory)
• memory  Update-Memory (memory, action)
• return action
CIS 730: Introduction to Artificial Intelligence
Kansas State University
Department of Computing and Information Sciences
Example:
Automated Taxi Driver
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Agent Type: Taxi Driver
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Percepts
– Visual: cameras
– Profilometer: speedometer, tachometer, odometer
– Other: GPS, sonar, interactive (microphone)
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Actions
– Steer, accelerate, brake
– Talk to passenger
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Goals
– Safe, legal, fast, comfortable
– Maximize profits
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Environment
– Roads
– Other traffic, pedestrians
– Customers
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Discussion: Performance Requirements for Open Ended Task
CIS 730: Introduction to Artificial Intelligence
Kansas State University
Department of Computing and Information Sciences
Review: Course Topics
•
Overview: Intelligent Systems and Applications
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Artificial Intelligence (AI) Software Development Topics
– Knowledge representation
• Logical
• Probabilistic
– Search
• Problem solving by (heuristic) state space search
• Game tree search
– Planning: classical, universal
– Machine learning
• Models (decision trees, version spaces, ANNs, genetic programming)
• Applications: pattern recognition, planning, data mining and decision support
– Topics in applied AI
• Computer vision fundamentals
• Natural language processing (NLP) and language learning survey
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Implementation Practicum – 1-2 Students per Team
CIS 730: Introduction to Artificial Intelligence
Kansas State University
Department of Computing and Information Sciences
Terminology
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Artificial Intelligence (AI)
– Operational definition: study / development of systems capable of “thought
processes” (reasoning, learning, problem solving)
– Constructive definition: expressed in artifacts (design and implementation)
•
•
Intelligent Agents
Topics and Methodologies
– Knowledge representation
• Logical
• Uncertain (probabilistic)
• Other (rule-based, fuzzy, neural, genetic)
– Search
– Machine learning
– Planning
•
Applications
–
–
–
–
Problem solving, optimization, scheduling, design
Decision support, data mining
Natural language processing, conversational and information retrieval agents
Pattern recognition and robot vision
CIS 730: Introduction to Artificial Intelligence
Kansas State University
Department of Computing and Information Sciences
Summary Points
•
Artificial Intelligence: Conceptual Definitions and Dichotomies
– Human cognitive modelling vs. rational inference
– Cognition (thought processes) vs. behavior (performance)
– Intelligent agent framework
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Roles of Knowledge Representation, Search, Learning, Inference in AI
– Necessity of KR, problem solving capabilities in intelligent agents
– Ability to reason, learn
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Applications and Automation Case Studies
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–
–
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Search: game-playing systems, problem solvers
Planning, design, scheduling systems
Control and optimization systems
Machine learning: pattern recognition, data mining (business decision support)
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Course Group: http://groups.yahoo.com/group/ksu-cis730-fall2004
•
More Resources Online
– Home page for AIMA (R&N) textbook: http://aima.cs.berekeley.edu
– CMU AI repository
– Comp.ai newsgroup (now moderated): http://groups.google.com
CIS 730: Introduction to Artificial Intelligence
Kansas State University
Department of Computing and Information Sciences