Introduction to AI. H.Feili, 1 Introduction to Artificial Intelligence LECTURE 2: Intelligent Agents What is an intelligent agent? Structure of intelligent agents Environments Examples

Introduction to AI. H.Feili, 2 Intelligent agents: their environment and actions

Introduction to AI. H.Feili, 3 Ideal rational agents For each possible percept sequence, an ideal rational agent should take the action that is expected to maximize its performance measure, based on evidence from the percept sequence and its built-in knowledge. Key concept: mapping from perceptions to actions Different architectures to realize the mapping

Introduction to AI. H.Feili, 4 Structure of intelligent agents Agent program: a program that implements the mapping from percepts to actions Architecture: the platform to run the program (note: not necessarily the hardware!) Agent = architecture + program Examples: –medical diagnosis- part-picking robot –satellite image analysis- interactive tutor –refinery controller- flight simulator

Introduction to AI. H.Feili, 5 Illustrative example: taxi driver

Introduction to AI. H.Feili, 6 Table-Driven Agents function Table-driven-agent(percept) returns action static: percepts, a sequence, initially empty table, indexed by percept sequences (given) append percept to the end of percepts action := LOOKUP(percepts, table) return action Keeps a list of all percepts seen so far Table too large takes too long to build might not be available

Introduction to AI. H.Feili, 7 Simple Reflex Agent (1)

Introduction to AI. H.Feili, 8 Simple Reflex Agent (2) function Simple-Reflex-Agent(percept) returns action static: rules, a set of condition-action rules state := Interpret-Input (percept) rule := Rule-Match(state, rule) action := Rule-Action[rule] return action No memory, no planning

Introduction to AI. H.Feili, 9 Reflex Agents with States (1)

Introduction to AI. H.Feili, 10 Reflex Agents with States (2) function Reflex-Agent-State(percept) returns action static: rules, a set of condition-action rules state, a description of the current state state := Update-State (state, percept) rule := Rule-Match(state, rules) action := Rule-Action[rule] state := Update-State (state, action) return action still no longer-term planning

Introduction to AI. H.Feili, 11 Goal-based Agents (1)

Introduction to AI. H.Feili, 12 Goal-based Agents (2) function Goal-Based-Agent(percept, goal) returns action static: rules, a set of condition-action rules state, a description of the current state state := Update-State (state, percept) rule := Plan-Best-Move(state, rules, goal) action := Rule-Action[rule] state := Update-State (state, action) return action longer term planning, but what about cost?

Introduction to AI. H.Feili, 13 Utility-based Agents (1)

Introduction to AI. H.Feili, 14 Utility-based Agents (2) Add utility evaluation: not only how close does the action take me to the goal, but also how useful it is for the agent Note: both goal and utility-based agents can plan with constructs other than rules Other aspects to be considered: –uncertainty in perceptions and actions –incomplete knowledge –environment characteristics

Introduction to AI. H.Feili, 15 Properties of environments Accessible to inaccesible: is the state of the world fully know at each step? Deterministic to nondeterministic: how much is the next state determined by the current state? Episodic to non-episodic: how much state memory? Static to dynamic: how much independent change? Discrete to continuous: how clearly are the actions and percepts differentiated? What is the environment where the agent acts like?

Introduction to AI. H.Feili, 16 Examples of environments Chess: accessible, deterministic, nonepisodic, static, discrete. Poker: inaccessible, nondeterministic, nonepisodic, static, discrete. Satelite image analysis:accessible, deterministic nonepisodic, semi static, continuous Taxi driving: all no!

Introduction to AI. H.Feili, 17 Environment Programs (1) A program to run the individual agents and coordinate their actions -- like an operating system. Control strategies: –sequential: each agent perceives and acts once –asynchronous: let the agents communicate –blackboard: post tasks and have agents pick them Agents must not have access to the environment program state!

Introduction to AI. H.Feili, 18 Environment Programs (2) function Run-Environment (state, Update-Function, agents, Termination-Test, Performance-Function) repeat for each agent in agents do Percept[agent] := Get-Percept(agent, state) for each agent in agents do Action[agent] := Program[agent](Percept([agent]) states := Update-Function(actions, agents, state) scores := Performance-Function(scores, agents, states) until Termination-Test(states) return scores

Introduction to AI. H.Feili, 19 Environment Programs: Examples Chess –two agents, take turns to move. Electronic stock market bidding –many agents, asynchronous, blackboard-based. Robot soccer playing –in the physical world, no environment program!

Introduction to AI. H.Feili, 20 Summary Formulate problems in terms of agents, percepts, actions, states, goals, and environments Different types of problems according to the above characteristics. Key concepts: –generate and search the state space of problems –environment programs: control architectures – problem modelling is essential

Introduction to AI. H.Feili, 21 ?