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Agents Agents Intelligent Agents – Types of Intelligent Agents – Types of agents agents
Laurea
Laurea MAGISTRALE in MAGISTRALE in COMPUTER SCIENCE COMPUTER SCIENCE
Corso di
ARTIFICIAL INTELLIGENCE Stefano Ferilli
Definitions
●
Agent: Who acts
●
Software Agents: Automated systems that carry out useful tasks
●
Characterizing Features
– Autonomous: action is driven by “internal” directives
– Reactive: perceive aspects of the environment and react appropriately
– Proactive: may take initiative and make goal-driven actions
– Social: communicate with other agents
Agents
●
How should agents act?
●
They should interact with the environment
– Sensors
– Effectors
Kinds of agents
●
A traditional distinction
●
Simple-Reflex agents
●
Agents who take into account the world
●
Goal-driven agents
●
Utility-driven agents
●
Our focus
●
NO interaction with the environment
– Making suitable sensors and effectors is the job of engineering
●
YES reasoning process that the agent must carry out
Agent Schemes
●
First schema : Simple-Reflex (S-R) Agents
●
Machines that have no internal states and react to stimuli coming from the environment
– Interpret input – find rule
– Stimulus-Reaction
●
Next schema
●
The agent takes into account the world and the acquired experience
– Memory and Internal states
Simple-Reflex Agent
E nv iro nm en t
AGENT
Sensors
How the world Is now
Effectors
Which action to take now
Condition-Action Rules
Simple Reflex Agents
●
Operates on the basis of simple condition- action rules
●
function ARS(perceptions) return action
– static: rules // a set of condition-action rules
– state interpret_input(perceptions)
– rule find_rule(state, rules)
– action rule_action(rule)
– return action
Simple-Reflex Agent with Internal State
E nv iro nm en t
AGENT
Sensors
How the world Is now
Effectors
Which action to take now
Condition-Action Rules What actions do How the world evolves
State
Simple-Reflex Agent with Internal State
●
Operate on the basis of
– simple condition-action rules
– an internal state that represents a simple past experience of the agent
●
function ARCS(perceptions) return action
– static: rules // a set of condition-action rules state /* a description of the current
state of the world */
– state update_state(state, perceptions)
– rule find_rule(state, rules)
– action rule_action(rule)
– state update_state(state, action)
– return action
Agents with explicit goals
●
When the agent has explicit information about its goal, the sequence of actions, based on the stimuli that “produce” the consequent action, is defined as a function of the goal to reach
●
Search & Planning
Agent with explicit goals
E nv iro nm en t
AGENT
Sensors
How the world Is now
Effectors
Which action to take now
Goal What actions do How the world evolves
State
How it becomes if I carry out action A
Agent with explicit goals
●
Variant of the schema
●
The agent knows how to operate when
– there are many goals, possibly contrasting with each other, or
– none of the goals can be attained with certainty
●
The agent must be able to assess the
utility/convenience of making each choice
Complete utility-driven agent
E nv iro nm en t
AGENT
Sensors
How the world Is now
Effectors
Which action to take now
Utility What actions do How the world evolves
State
How it becomes if I carry out action A
How happy will I be in such a state
14
Interactive
English tutor Typed words Print exercises, suggestions,
corrections
Maximize student scores
in tests
Sets of students Refinery
controller
Temperature and pressure readings
Open and close valves; adjust
temperature
Maximize purity, product,
safety
Refinery Robot that
collects parts
Pixels with variable intensity
Collect parts and sort them in containers
Place parts in proper containers
Conveyor belt with parts Satellite images
analysis system
Pixels with variable intensity
and color
Print a categorization
of the scene Fix categorization
Images from orbiting satellite Medical
diagnosis system
Symtoms, responses, patient’s answers
Questions, tests, treatments
Healty patient, minimize costs
Patient, hospital Agent Types Perceptions Actions Goals Environment
Examples of Agents
Rational Agent
●
Does the right thing,
●
the action that will bring most success by maximizing the expected performance measure
●
Acting rationally = acting to reach goals, given own knowledge, based on the incoming perceptions, given the actions he can carry out
●
Performance measure = Evaluate – how and when – the expected success, considering what has been perceived
●
New incoming perceptions do not affect its representation/model of the world
Describing/Building an agent
●
Correspondence:
●
Sequence of perceptions Actions
●
An agent may be described through
●
A complete listing of actions it may carry out in response to any possible sequence of perceptions
– Project/program of an ideal rational agent
●
The definition of the correspondences ( function of correspondence) without providing an exhaustive listing
Describing/Building a real and autonomous rational agent
●
Computational limitations prevent reaching perfect rationality
●
Autonomous agent: has learning abilities
●
Allow it to update its initial knowledge
●
In real rational agents there are many ways for modeling
●
Autonomy and learning capacity
●
Evaluation of performance
●
Aim: building agents with best performance
●
As for any system
Problem solving agents and Planning agents
●
Problem solving agent
●
Decides the sequence of actions before acting
– Accessible environment
●
Knowledge-based agent
●
Chooses actions based on an explicit representation of states and of actions’ effect
– Complex and inaccessible environment
●
Planning agent
●
Plans but using explicit knowledge about actions and their effect
– A special case of a KB agent
Problem-solving Agent
●
To solve a problem, an AI system or
Knowledge-based system generally considers a large number of possibilities and dynamically builds a solution
Problem Solving
●
“Problem” is a concept that cannot be defined, only exemplified. (Nilsson, 1982)
●
Some examples
– Board puzzles -> usually NP
– “Traveling Salesman”
– Puzzles such as Rubik’s Cube
– SAT, Theorem Proving
– Games (Checkers, Chess, ...)
– VLSI
A General “Problem Solver”
●
How to build programs that, through
●
symbolic computation
●
specialized knowledge about a domain of interest are able to solve problems “automatically”?
●
I.e., without an algorithm defined and translated into a pre-defined sequence of operations to be executed
●
Initial focus of AI
●
Before focusing on methods and techniques o represent, manipulate, process knowledge
●
Problem Solving
●
Methods to attain (often indeterminate or uncertain) goals
●
Search Methods
●
Methods to generate all possible solutions and test them until an appropriate one is found
Problem Solving
●
Available are
●
1. A (possibly partial) description of a current situation and of a desired situation
– Situations represented using schemes (or languages) rich enough to allow describing entities, events, cases or objects (situations) and differences between pairs of situations
●
2. A list of operators that can be applied to situation in order to transform them into new situations
– Operators available in a language referred to the solution process
– Any sequence of operators in the process language is itself an operator
Problem Solving
●
Problem solution
●
3. A (composite) operator in the process language
that transforms the object describing the initial
situation into the object describing the desired
situation
Turing Machine
●
Theoretical computational model
●
Sequence of applicable operators known a priori
●
Imperative solution methods corresponding to algorithms that are general but valid for specific problems
●
Universal Turing Machine
●
Using the imitation algorithm, can execute any algorithm for a specific problem (specialized Turing Machine) it takes as input along with the data on which it is to be applied
●
Problems for which the algorithm is lacking
●
Are to be solved tentatively
●
Must exploit a general search mechanism
– Solution is to be searched in the space of possible
“problem states”
– To reach a solution must allow to choose, at any moment, among the applicable operators, those that can be appropriately applied to a situation/state and transform it into a situation that is closer to the final expected situation (goal state)
●
A possible computational model:
Pattern Directed Inference Systems
Pattern Directed Inference Systems (PDIS)
●
Programs that directly and dinamically respond to a range of (unanticipated) data or events
●
rather than working on expected data, in known format, using a pre-defined and rigid strategy (control structure)
●
So-called “Pattern-Directed organization”
●
Patterns underlying data help to choose the code to be applied
– Pattern matching operator is crucial
Pattern Directed Inference Systems
●
3 components
●
A collection of modules (PDM)
– Substructures that can be activated by patterns in the data
●
Global Data Base, or Working memory
– One or more data structures that can be examined and modified by the PDMs
●
An interpreter
– Controls the selection and activation of the PDM modules
Problem Solver
●
Organized as 3 specialized modules with specific objectives
●
Describe operator
●
Match applicable conditions
●
Choose operator
Problem Solving
●
Problem setup
– Define the “problem environment”
●Identify all possible configurations of the elments in the domain (state space)
●Distinguish “legal” or “admissible” states – Define the initial state
– Define the goal states
●Corresponding to the desired situations of the problem – Define a set of operators (rules)
●Each explicitly expressing the conditions that are to be satisfied in order to apply it
– Generate solution
●Search process, in the space of states, of those operators that allow to reach the goal states
Problem State Space
●
Problem space =
●
A set of problem states (possible configurations)
– Symbolic structures representing single problem configurations in sufficient detail to allow devising a solution procedure
+
●
A set of operators that can change the states
– Functions that take a state and map/transform it into another
– Not all applicable to any state
●Operator preconditions : Applicability conditions –The conditions that must be true for an operator to be
applicable to a state
Problem State Space
●
Examples
●
8-Puzzle:
– States: the different permutations of the tiles
– Operators: move a numbered tile up, down, left, right
●
Chess:
– States: the different dispositions of pieces on the chessboard
– Operators: valid moves for each piece according to the game rules
8-puzzle
●
Problem
●
Given a frame of numbered square tiles in random order with one tile missing, place the tiles in order by making sliding moves that use the empty space
– E.g.:
– Possible moves: tile 4 down; tile 1 right, tile 8 up
●
9! = 362.880 configurations
2
8 7
6 5 4
3 1 7
2
5 8
1 6
4 3
How Many States?
●
15-puzzle
●
10
13states
●
24-puzzle
●
10
24states
●
Rubik’s Cube
●
10
19states
1 2 3 4 5 6 7 8 9 1011 12131415
2021222324 1 2 3 4 5 6 7 8 9 1011121314 1516171819
How Many States?
●
Knight’s Tour ...
●
“The first real program I tried to write was called the Knight’s Tour. You jump a knight piece around the chessboard, only in valid moves for a knight, in a pattern so that it hits every one of the sixty-four squares on the board exactly once. [...] I wrote my program [...] to try all the moves until you can’t move again. And if it didn’t hit all the squares by the time it got stuck, the program would back up and change a move and try again from there. It would keep backtracking as far as it needed and then kept going. That computer could calculate instructions a million times a second, so I figured it would be a cinch and would solve this problem quickly.
... Knight’s Tour ...
●
... Knight’s Tour ...
●
“[...] The computer doesn’t spit out anything. The
lights on the computer flickered, and then the lights
just stayed the same. Nothing was happening. My
engineer friend let it run a while longer and then
said, “Well, probably it’s in a loop”. [...] Anyway, the
next week I went back and I wrote my program so
that I could flip a switch in order to get printouts of
whatever chess arrangement it was working on. I
remember pulling the printouts out and studying
them that very day and realizing something. The
program was in fact working the way it was
supposed to. I hadn’t done anything wrong. It just
wasn’t going to come up with a solution for 10
25years. That’s a lot longer than the universe has
even been around.”
... Knight’s Tour
●
... Knight’s Tour
●
“That made me realize that a million times a second didn’t solve everything. Raw speed isn’t always the solution. Many understandable problems need an insightful, well-thought-out approach to succeed.”
... Knight’s Tour
●
... Knight’s Tour
●
“That made me realize that a million times a second didn’t solve everything. Raw speed isn’t always the solution. Many understandable problems need an insightful, well-thought-out approach to succeed.”
– Steve Wozniak
... Knight’s Tour
●
... Knight’s Tour
●
“That made me realize that a million times a second didn’t solve everything. Raw speed isn’t always the solution. Many understandable problems need an insightful, well-thought-out approach to succeed.”
– Steve Wozniak
Problem-space Graph
●
A mathematical abstraction often used to represent the space of a problem
– Directed or undirected graph
●States = Nodes
●Operators = Arcs
●
State space formally represented as a 4-tuple
<N,A,S,G>
– N : set of states, represented as nodes in a graph
– A : set of arcs connecting nodes, representing steps of a problem-solving process
– S : a non-empty subset of N that includes the initial state
– G : non-empty subset of N that includes the goal states of the problem
●
Solution = path in the graph, from an initial node in S to a node in G
Problem Solving
●
4 general steps
●
Goal definition
– Which are the successful states of the world
●
Problem definition
– What actions and states are to be considered depending on a specific objective
●
Search
– Determining the possible sequence of actions that lead to known/legal states
– (possibly) Choosing the best sequence
●
Execution
– Carrying out actions
Problem Solving
●
Example: Route finding
Problem Solving
●
Example: Planning a vacation
●
We are in Romania
– We are currently in Arad
– Tomorrow there is a flight from Bucharest
●
Goal
– Being in Bucharest
●
Problem formulation
– States: different towns
– Actions: driving from one town to another
●
Solution
– A sequence of towns: Arad, Sibiu, Fagaras, Bucharest
Problem Solving
●
Example: Route finding
Goal Actions
Problem Solving
●
Example: Route finding
Goal Start
States Actions
Solution
Choosing the space of states
●
The real world is, in general, complex
●
State space : an abstraction of the real world useful for problem solving
●
States = set of real states
– Admissible, legal, etc.
●
Actions = complex combinations of real actions
●E.g., the journey Arad Zerind represents a complex set of possible roads, paths, journeys
– Abstraction is valid if the path connecting two states reflects what one can do in the real world
●
Solution(s) = the set of possible real paths that are soutions in the real world
– Each abstract solution should be simpler than in the real world
General model of a solver agent
function SIMPLE-PROBLEM-SOLVING-AGENT( percept) return an action
static: seq, an action sequence
state, some description of the current world state goal, a goal
problem, a problem formulation state UPDATE-STATE(state, percept) if seq is empty then
goal FORMULATE-GOAL(state)
problem FORMULATE-PROBLEM(state,goal) seq SEARCH(problem)
action FIRST(seq) seq REST(seq) return action
Hypotheses so far
●
Environment
●
Static
●
Discretizable
●
Observable
●
Actions
●
Deterministic
Planning vs Problem Solving
●
PS Agents
●
Can generate successors of a state
●
Implicit goal representation, test to check goal attainment (goal test)
●
Planning problem
– Obtaining, through a heuristic search process, a sequence of actions that lead from the initial state to the goal state
●
Planning Agents
●
Has an explicit representation of the goal, of the actions and of their effect
●
Can decompose the goal into independent sub-goals
●
Has freedom in building the plan
●
Can/must be more efficient
Planning agents
●
Considerations
●
A goal must be given
●
Knowing the current status of the environment is not sufficient to decide what to do
●
Searching a solution to the problem requires planning the sequence of actions
Planning agents
●
When the state of the world is accessible, an agent may use its perceptions of the
environment to build a “complete and correct”
model of the current state of the world
●
Given a goal, can exploit a planning algorithm to generate an action plan that it will put in action step by step
●
Ideal planner
Logic, or Knowledge-based agent
●
Starts with a general knowledge of the world and of its actions
●
Uses logical reasoning to
●
Maintain a description of the world that is consistent with new incoming perceptions
●
Infer a sequence of actions that will lead to attain its goals
Knowledge-based agent
●
Agents with knowledge expressed explicitly and declarative (not hard-wired)
●
To improve their rational capabilities, artificial agents must be endowed with more complex representations of the world, that cannot be described simply
– The world is typically complex: need for a partial and incomplete representation of an abstraction of the world useful for the agent’s goals
– Partially observable environments need for more expressive knowledge representation languages and inferential capabilities
– Most problems in AI are “knowledge intensive”
●Knowledge-based Systems is almost a synonym of AI
Knowledge
●
Definitions
●
Awareness and understanding of facts, information, truth
●
The fact or condition of being aware of something
●
Knowledge is experience
●
Self-consciousness of owning valuable information
items if connected among them and of little utility if
taken singularly
Are facts and knowledge the same?
●
Through knowledge, we can understand the world around us and make inferences
●
E.g., we know that sun is warm and sky is blue.
These facts are knowledge about the world.
●
But we also know that
– if sun is high, then there is visibility
– an automatic dispenser dispenses products if we put in coins
We make these inferences based on the availability of facts
Data, Information, Knowledge
●
Data
●
Numbers and words related to property of reality that may be synthesized and processed
●
Information
●
Data in context and from an objective perspective...
interpreted
●
Knowledge
●
Dynamic set of concrete experiences, values, information and intuitions that allow to evaluate and include new experiences and information
– Information made subjecive
●
[Experience]
Knowledge in practice
●
Knowledge = information available for action
●
Declarative: knowing that
●E.g., knowing Roman history
●Knowing Wikipedia = being aware of the website
●
Procedural: knowing how
●E.g., “I can swim”
●Knowing Wikipedia = being able to write a page using the Wikipedia language
●
Focus
●
Traditional Computer Science : on knowing how
– Procedural knowledge “hidden” in the algorithm
●
AI : also on knowing that
– Programming with declarative knowledge
3 Kinds of declarative knowledge
●
Terminological
●
About the lexicon of a language
– E.g., “mother” means “woman with at least one child”
●
Nomologic
●
About regularities, general laws that rule the world
– E.g., mothers are always older than their children, usually mothers love their children, etc.
●
Factual
●
About particular facts
– E.g., Steve is a child of Anna
...Kinds of knowledge
●
What about knowledge about individuals?
●
Specific, concrete or abstract, living or non-living, animate or inanimate objects
– E.g., “I know Barbara”, “I know Beethoven’s Symphony No. 9”
●
Only seemingly different case
●
Again, factual knowledge
●
More precisely an (often very wide) set of factual knowledge related to a specific individual
– Barbara, Beethoven’s Symphony No. 9
How to Represent Knowledge?
●
Need to establish a set of conventions about how to describe a situation, some objects, some events, a reality
●
Using a “computable” knowledge representation means adopting these
conventions to conceptualize an abstraction of the world, having available tools to create it, modify it, reason with it, etc.
●
But what does it mean to “reason”?
A Perspective on Human-Level Reasoning
●
“It appears that there are two kinds of reasoning that people do. I will characterize them roughly as rule-based and associative. When we’re doing the former, we’re aware of it, and it has steps that we can describe. We know we’re doing something, and if it’s complicated enough, we know that we’re doing work and that we might make mistakes.
When we do associative reasoning, however, it’s largely below the level of consciousness, and it appears effortless.
●
...
A Perspective on Human-Level Reasoning
●
It’s probably not an accident, therefore, that the structure of computer programs and the characteristics of logical formalisms tend to resemble the rule-based reasoning that we’re aware of, and we’ve been relatively successful at getting computers to do this kind of reasoning.
●
On the associative side, however, it is much harder even to understand what people do, much less figure out how to get computers to do something equivalent to it. When it comes to dealing with large amounts of knowledge, when a person has more knowledge, the person generally thinks better and is more effective at understanding the environment and functioning in it.”
– W. A. Woods “Meaning and Links” 2007
Knowledge-based agent
●
Has knowledge about:
●
The objects in the domain
●
The events that are to happen
●
How to accomplish a specific task
●
Knowledge must be represented explicitly
●
Knowledge representation = a combination of data structures and interpretive procedures that, if used appropriately, make the system pursue a
reasonable behavior, aware of the world in which it acts
– Note: not just the definition of suitable data structures to represent information,
but also development of procedures that can be applied on them to make inferences
Knowledge-based agent
●
Architectural Principles
●
Any knowledge-based system must be able to express 2 kinds of knowledge in a separate and modular way
– About the application domain (what)
– About how to use knowledge about the application domain to solve problems
●
Problems
– Representation
●Expressing knowledge about the problem – Strategy
●What control strategy to use
Knowledge Base
Inference Engine
Knowledge-based agent
●
Must own a Knowledge Base (KB)
– Often described in a formal language
●
Adopts a declarative approach
– I.e., everything it needs to know is explicit
●
Includes an inference engine to ask itself (Ask) what to do or answers that can be deduced from what is stored in the Knowledge Base
●
2 possible perspectives on such agents
●
formal (knowledge level) : for what they know, independently of their implementation
●
operational (implementation level) : for how data structures are implemented in the KB and for the algorithms devised to process such data structures
A simple knowledge-based agent
●
A knowledge-based agent
– maintains a knowledge base (KB): a set of propositions expressed in a representation language
– Interacts with the KB through a functional Tell-Ask interface
●Tell: to add new facts to the KB
●Ask: to query the KB… maybe Retract –Answers must logically follow from the KB
(being logical consequence of the KB)
A simple knowledge-based agent
●
The agent must be capable of
●
Representing states, actions, etc.
●
Incorporate new perceptions of the world
●
Update its internal representation of the world
●
Reason about the world
●
Deduce the most appropriate actions
Knowledge-based & Intelligent agents
●
Is a knowledge-based agent an Intelligent Agent?
●
“The ability of an agent, natural or artificial, of exhibiting an intelligent behavior can be described in terms of knowledge owned by a subject”
– A. Newell & H. Simon: “Human Problem Solving”, Prentice-Hall, 1972
Knowledge-based Systems Intelligent Systems (Agents)
●
A. Newell & H. Simon: “Human Problem Solving”, Prentice-Hall, 1972
69 Affect the
world
Change representation Apply
method
Choose solution method Recognize
input Internal Representation
General
Knowledge Methods
Repository
●
A “human” intelligent agent is immersed in an environment in which it must carry out a task
●
The task must be intially acquired, recognized and encoded in an initial internal representation
●
Need for an ability to recognize
●
First, the task and
●
Then, the method to use for solving the problem
– selected from a “Methods repository”
●
The methods repository draws from a repository of “general knowledge about the world”
●
Agent’s activity cycle
●
Recognize the task to be carried out
●
Select the method from a methods repository
●
If the internal representation is satisfactory, apply the method
– Application of the method translates into an action that affects the environment
●
During several activity cycles, both methods and representations may be changed
●
Representation :
– The set of data structures that describe the problem and that, once processed, will allow to solve the problem, &
– The set of ways to interpret them
The Knowledge Level
●
“The knowledge level provides the means to
‘rationalise’ the behaviour of a system from the standpoint of an external observer. This observer treats the system as a ‘black box’ but maintains that it acts ‘as if’ it possesses certain knowledge about the world and uses this knowledge in a perfectly rational way toward reaching its goals. The behaviour of the agent is explained and predicted in terms of the reasons that the agent is assumed to have to take certain actions in order to reach ascribed goals.”
●
[A. Newell]
The Knowledge Level
●
[A. Newell]
The Knowledge Level
●
“In more detail a knowledge level description is based on the following model of the behaviour of an agent:
●
The intelligent agent possesses knowledge
●
Some of this knowledge constitutes the goals of the agent
●
The agent has the ability to perform a set of actions
●
The agent chooses actions according to the principle of rationality
●
The agent will select an action to perform next which according to its knowledge leads to the achievement of one of its goals.”
– [A. Newell]
The Knowledge Level
●
[A. Newell]
75
K’
Knowledge Symbol System S’
Goals G
Knowledge K Symbol System S
Observer Agent
Actions
The artificial agent (observer) simulates the behavior of an intelligent agent (human)
The Knowledge Level
●
The observer
– Considers the agent as a knowledge system, and knows its goals (G) and knowledge (K)
– Knows that it owns a set of actions:
by direct observation also knows the environment.
Knows that the agent determines which actions to take based on a symbol system
– May itself be considered as a knowledge system
●It has knowledge (about the agent, the environment, etc.)
●It has available a symbol system which selects the actions that the agent would take
– Through its processing mechanism, produces predictions about the agent’s behavior and can simulate it
The Knowledge Level
●
The observer is a
Knowledge-based System
that can emulate the behavior of an intelligent agent that is able to predict and understand, without having an operational model of the mechanisms, methods and processing enacted by the agent
●
The artificial system uses only
●
the knowledge that the agent has about its external environment
●
the knowledge about the goals
●
the system of symbols
●
What are the minimum features for an artificial system to be capable of taking actions in an intelligent way?
●
Primary need: being able to handle symbols
– Read
– Interpretate
– Process
Physical Symbol System
●
Physical Symbol System
●
A system that produces a collection of symbol structures that evolves in time
●
Operates in a wider world than that of the symbolic expressions themselves
●
Physical Symbol System Hypothesis
●
A physical symbol system has the necessary and sufficient means for generalized intelligent action
– [Newell & Simon]
●
Computers can be programmed to simulate any physical symbol system
Learning Programs/Agents
●
A program A is said to learn from experience E with respect to a given set of tasks T and with a performance measure P
if its performance on tasks T, measured according to P, improves with experience E
●
Any specific program that learns must identify and define:
●
The class of tasks
●
The measure of performance to be improved
●
The source of experience
Learning Agent
●
Architecture
Learning Agent
●
Components
●
Learning Element
– Learns and improves behavior
●
Performance Element
– The agent itself knows what to do and can evaluate and improve what it does
●
Problem Generator
– Suggests alternative actions to explore and carry out
●
Critical evaluation element
– Provides feedback about how the agent is behaving
Further Readings
●
A. Newell & H. Simon: “Human Problem Solving”, Prentice-Hall, 1972
●
