Transcript CIS 690 (Implementation of High
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
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Today’s Reading: Chapter 2, Russell and Norvig Intelligent Agent (IA) Design
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Shared requirements, characteristics of IAs Methodologies
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Software agents Reactivity vs. state Knowledge, inference, and uncertainty Intelligent Agent Frameworks
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Reactive With state Goal-based Utility-based Thursday: Problem Solving and Search
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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?
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New Computational Capabilities
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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
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Cognitive science: theories of knowledge acquisition (e.g., through practice) Performance elements: reasoning (inference) and recommender systems Time is Right
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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 Bayesian Methods PAC Formalism Mistake Bounds Cognitive Science Computational Complexity Theory Control Theory Inference NLP / Learning Economics Neuroscience Philosophy Bayes’s Theorem Missing Data Estimators Psychology Statistics Symbolic Representation Planning/Problem Solving Knowledge-Guided Learning Planning, Design Optimization Meta-Learning
Artificial Intelligence
Bias/Variance Formalism Confidence Intervals Hypothesis Testing Power Law of Practice Heuristics Logical Foundations Consciousness CIS 730: Introduction to Artificial Intelligence Game Theory Utility Theory Decision Models 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
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Text Mining: Information Retrieval and Filtering
20 USENET Newsgroups
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comp.graphics
comp.os.ms-windows.misc
comp.sys.ibm.pc.hardware
comp.sys.mac.hardware
comp.windows.x
misc.forsale
rec.autos rec.motorcycles
rec.sports.baseball
rec.sports.hockey
soc.religion.christian sci.space
talk.politics.guns
sci.crypt
talk.politics.mideast sci.electronics
talk.politics.misc
talk.religion.misc
sci.med
alt.atheism
Problem Definition [Joachims, 1996]
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Given: 1000 training documents (posts) from each group Return: classifier for new documents that identifies the group it belongs to 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: ...
Performance of Newsweeder (Naïve Bayes): 89% Accuracy CIS 730: Introduction to Artificial Intelligence Kansas State University Department of Computing and Information Sciences
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Artificial Intelligence: Some Problems and Methodologies
Problem Solving
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Classical search and planning Game-theoretic models Making Decisions under Uncertainty
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Uncertain reasoning, decision support, decision-theoretic planning Probabilistic and logical knowledge representations Pattern Classification and Analysis
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Pattern recognition and machine vision Connectionist models: artificial neural networks (ANNs), other graphical models Data Mining and Knowledge Discovery in Databases (KDD)
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Framework for optimization and machine learning Soft computing: evolutionary algorithms, ANNs, probabilistic reasoning Combining Symbolic and Numerical AI
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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
Internal Model (if any) Knowledge about World Knowledge about Actions Preferences Sensors Observations Predictions Expected Rewards Action Effectors CIS 730: Introduction to Artificial Intelligence Kansas State University Department of Computing and Information Sciences
Term Project Guidelines
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Due: 08 Dec 2004
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Submit using new script (procedure to be announced on class web board) Writeup must be turned in on (for peer review) Team Projects
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Work in pairs (preferred) or individually Topic selection and proposal due 17 Sep 2004 Grading: 200 points (out of 1000)
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Proposal: 15 points Originality and significance: 25 points Completeness: 50 points
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Functionality (20 points)
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Quality of code (20 points) Documentation (10 points)
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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
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Game-playing: rogue-like (Nethack, Angband, etc.); reinforcement learning Multi-Agent Systems and simulations; robotic soccer (e.g., Teambots) Probabilistic Reasoning and Expert Systems
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Learning structure of graphical models (Bayesian networks) Application of Bayesian network inference
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Plan recognition, user modeling Medical diagnosis
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Decision networks or other utility models Probabilistic Reasoning and Expert Systems Constraint Satisfaction Problems (CSP) Soft Computing for Optimization
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Evolutionary computation, genetic programming, evolvable hardware Probabilistic and fuzzy approaches Game Theory CIS 730: Introduction to Artificial Intelligence Kansas State University Department of Computing and Information Sciences
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Homework 1: Machine Problem
Due: 10 Sep 2004
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Submit using new script (procedure to be announced on class web board) HW page: http://www.kddresearch.org/Courses/Fall-2004/CIS730/Homework Machine Problem: Uninformed (Blind) vs. Informed (Heuristic) Search
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Problem specification (see HW page for MP document)
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Description: load, search graph
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Algorithms: depth-first, breadth-first, branch-and-bound, A* search Extra credit: hill-climbing, beam search
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Languages: options
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Imperative programming language of your choice (C/C++, Java preferred)
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Functional PL or style (Haskell, Scheme, LISP, Standard ML) Logic program (Prolog)
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MP guidelines
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Work individually Generate standard output files and test against partial standard solution
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See also: state space, constraint satisfaction problems CIS 730: Introduction to Artificial Intelligence Kansas State University Department of Computing and Information Sciences
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Intelligent Agents: Overview
Agent: Definition
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Any entity that perceives its environment through sensors and acts upon that environment through effectors
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Examples (class discussion): human, robotic, software agents Perception
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Signal from environment May exceed sensory capacity Sensors
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Acquires percepts Possible limitations Action
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Attempts to affect environment Usually exceeds effector capacity Effectors
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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
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Informal: “does the right thing, given what it believes from what it perceives” What is “the right thing”?
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First approximation: action that maximizes success of agent Limitations to this definition?
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Issues to be addressed now
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How to evaluate success
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When to evaluate success
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Issues to be addressed later in this course
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Uncertainty (in environment, in actions) How to express beliefs, knowledge Why Study Rationality?
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Recall: aspects of intelligent behavior (last lecture)
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Engineering objectives: optimization, problem solving, decision support Scientific objectives: modeling correct inference, learning, planning
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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”
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Committing actions
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Limited to set of effectors
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In context of what agent knows
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Specification (cf. software specification)
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Preconditions, postconditions of operators Caveat: not always perfectly known (for given environment) Agent may also have limited knowledge of specification Agent Capabilities: Requirements
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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 Possible Additional Capabilities
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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
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Definition: criteria that determine how successful agent is Clearly, varies over
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Agents Environments
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Possible measures?
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Subjective (agent may not have capability to give accurate answer!)
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Objective: outside observation
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Example: web crawling agent
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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.) When to Evaluate Success
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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
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Nota Bene (NB): not the same Distinction
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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?
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Key question in AI
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What is a plausible outcome?
Especially important in knowledge-based expert systems Of practical important in planning, machine learning
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Related questions
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How can an agent make rational decisions given beliefs about outcomes of actions?
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Specifically, what does it mean (algorithmically) to “choose the best”?
Limitations of Rationality
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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
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Determines what is rational at any given time Varies with agent, environment, situation Performance Measure
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Specified by outside observer or evaluator Applied (consistently) to (one or more) IAs in given environment Percept Sequence
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Definition: entire history of percepts gathered by agent NB: may or may not be retained fully by agent (issue: state and memory) Agent Knowledge
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Of environment – “required” Of self (reflexive reasoning) Feasible Action
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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
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Given: any possible percept sequence Do: ideal rational behavior
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Whatever action is expected to maximize performance measure NB: expectation – informal sense (for now); mathematical foundation soon
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Basis for action
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Evidence provided by percept sequence Built-in knowledge possessed by the agent Ideal Mapping from Percepts to Actions
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Figure 2.2, R&N Mapping p: percept sequence
action
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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
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Given: sequence of percepts Return: IA’s actions
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Simulator: description of results of actions
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Real-world system: committed action Agent Programs
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Functions that implement p Assumed to run in computing environment (architecture)
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Hardware architecture: computer organization Software architecture: programming languages, operating systems
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Agent = architecture + program
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This course (CIS730): primarily concerned with p CIS540, 740, 748: concerned with architecture See also: Chapter 24 (Vision), 25 (Robotics), R&N Discussion : “Real” versus “Artificial” Environments CIS 730: Introduction to Artificial Intelligence Kansas State University Department of Computing and Information Sciences
Agent Programs
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Software Agents
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Also known as (aka) software robots, softbots Typically exist in very detailed, unlimited domains Example
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(Real-time) critiquing, automation of avionics, shipboard damage control
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Indexing (spider), information retrieval (IR; e.g., web crawlers) agents Plan recognition systems (computer security, fraud detection monitors)
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See: Bradshaw (Software Agents) Focus of This Course: Building IAs
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Generic skeleton agent: Figure 2.4, R&N function SkeletonAgent (percept) returns action
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static: memory , agent’s memory of the world
memory
Update-Memory (memory, percept)
action
Choose-Best-Action (memory)
memory
Update-Memory (memory, action)
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return action CIS 730: Introduction to Artificial Intelligence Kansas State University Department of Computing and Information Sciences
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Example: Automated Taxi Driver
Agent Type: Taxi Driver Percepts
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Visual: cameras Profilometer: speedometer, tachometer, odometer Other: GPS, sonar, interactive (microphone) Actions
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Steer, accelerate, brake Talk to passenger Goals
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Safe, legal, fast, comfortable Maximize profits Environment
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Roads Other traffic, pedestrians Customers 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
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Overview: Intelligent Systems and Applications Artificial Intelligence (AI) Software Development Topics
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Knowledge representation
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Logical Probabilistic
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Search
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Problem solving by (heuristic) state space search Game tree search
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Planning: classical, universal Machine learning
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Models (decision trees, version spaces, ANNs, genetic programming) Applications: pattern recognition, planning, data mining and decision support
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Topics in applied AI
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Computer vision fundamentals Natural language processing (NLP) and language learning survey 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)
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Operational definition : study / development of systems capable of “thought processes” (reasoning, learning, problem solving)
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Constructive definition: expressed in artifacts (design and implementation) Intelligent Agents Topics and Methodologies
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Knowledge representation
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Logical Uncertain (probabilistic) Other (rule-based, fuzzy, neural, genetic)
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Search Machine learning Planning Applications
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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
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Artificial Intelligence: Conceptual Definitions and Dichotomies
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Human cognitive modelling vs. rational inference Cognition (thought processes) vs. behavior (performance) Intelligent agent framework Roles of Knowledge Representation, Search, Learning, Inference in AI
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Necessity of KR, problem solving capabilities in intelligent agents Ability to reason, learn Applications and Automation Case Studies
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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) Course Group: http://groups.yahoo.com/group/ksu-cis730-fall2004 More Resources Online
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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