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Lecture 2 of 41

Agents and Problem Solving

Lecture Outline

• Intelligent Agent Frameworks
  • Reactive
  • With state
  • Goal-based
  • Utility-based
• Thursday: Problem Solving and Search
  • Background in combinatorial algorithms
    • Asymptotic analysis
    • Essentials of graph theory (definitions)
  • State space search handout (Winston)
  • Search handout (Ginsberg)

Review: 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 )
• 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

Agent Framework:

Simple Reflex Agents [1]

Agent Sensors

Effectors

Condition-Action Rules

What action I should do now

Environment

Agent Frameworks:

(Reflex) Agents with State [1]

Agent Sensors

Effectors

Condition-Action Rules

What action I should do now

State^ Environment

How world evolves

What my actions do

What world is like now

Agent Frameworks:

(Reflex) Agents with State [2]

• Implementation and Properties
  • Instantiation of generic skeleton agent: Figure 2.
  • function ReflexAgentWithState ( percept ) returns action
    • static: state , description of current world state; rules , set of condition-action rules
    • state ← Update-State ( state , percept )
    • rule ← Rule-Match ( state, rules )
    • action ← Rule-Action { rule }
    • return action
• Advantages
  • Selection of best action based only on current state of world and rules
  • Able to reason over past states of world
  • Still efficient, somewhat more robust
• Limitations and Disadvantages
  • No way to express goals and preferences relative to goals
  • Still limited range of applicability

Agent Frameworks:

Goal-Based Agents [2]

• Implementation and Properties
  • Instantiation of generic skeleton agent: Figure 2.
  • Functional description
    • Chapter 13: classical planning
    • Requires more formal specification
• Advantages
  • Able to reason over goal, intermediate, and initial states
  • Basis: automated reasoning
    • One implementation: theorem proving (first-order logic)
    • Powerful representation language and inference mechanism
• Limitations and Disadvantages
  • Efficiency limitations: can’t feasible solve many general problems
  • No way to express preferences

Agent Frameworks:

Utility-Based Agents [1]

Agent Sensors

Effectors

Utility (^) What action I should do now

State^ Environment

How world evolves

What my actions do

What world is like now

What it will be like if I do A

How happy will I be

Looking Ahead: Search

• Thursday’s Reading: Sections 3.1-3.4, Russell and Norvig
• Thinking Exercises (Discussion in Next Class): 3.3 (a, b, e), 3.
• Solving Problems by Searching
  • Problem solving agents: design, specification, implementation
  • Specification components
    • Problems – formulating well-defined ones
    • Solutions – requirements, constraints
  • Measuring performance
• Formulating Problems as (State Space) Search
• Example Search Problems
  • Toy problems: 8-puzzle, 8-queens, cryptarithmetic, toy robot worlds, constraints
  • Real-world problems: layout, scheduling
• Data Structures Used in Search
• Next Tuesday: Uninformed Search Strategies
  • State space search handout (Winston)
  • Search handouts (Ginsberg, Rich and Knight)

Homework 1:

Machine Problem

• 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
• 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

Rational Agents

• “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
• 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
• Possible Additional Capabilities
  • Memory (internal model of state of the world)
  • Knowledge about effectors, reasoning process (reflexive reasoning)

Measuring Performance

• 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.)
• When to Evaluate Success
  • Depends on objectives (short-term efficiency, consistency, etc.)
  • Is task episodic? Are there milestones? Reinforcements? (e.g., games)

What Is Rational?

• Criteria
  • Determines what is rational at any given time
  • Varies with agent, environment, situation
• Performance Measure
  • Specified by outside observer or evaluator
  • Applied (consistently) to (one or more) IAs in given environment
• Percept Sequence
  • Definition: entire history of percepts gathered by agent
  • NB: may or may not be retained fully by agent (issue: state and memory)
• Agent Knowledge
  • Of environment – “required”
  • Of self (reflexive reasoning)
• Feasible Action
  • What can be performed
  • What agent believes it can attempt?

Problem-Solving Agents [1]:

Preliminary Design

• Justification
  • Rational IAs: act to reach environment that maximizes performance measure
  • Need to formalize, operationalize this definition
• Practical Issues
  • Hard to find appropriate sequence of states
  • Difficult to translate into IA design
• Goals
  • Chapter 2, R&N: simplifies task of translating agent specification to formal design
  • First step in problem solving: formulation of goal(s) – “accept no substitutes”
  • Chapters 3-4, R&N: goal ≡ {world states | goal test is satisfied}
• Problem Formulation
  • Given: initial state, desired goal, specification of actions
  • Find: achievable sequence of states (actions) mapping from initial to goal state
• Search
  • Actions: cause transitions between world states (e.g., applying effectors)
  • Typically specified in terms of finding sequence of states (operators)