REVIEW: How this course will define AI • Artificial Intelligence is a science that has defined its goal as giving machines the ability to perform tasks that, when performed by humans, require intelligence. These include the ability to solve problems, make decisions, to learn and to understand.
Systems that think like humans Systems that think rationally Systems that act like humans Systems that act rationally Review humanly vs. rationally thinking vs. acting
Rationality • Rational agent definition:“For each possible percept sequence, a rational agent should select an action that is expected to maximize its performance measure, given the evidence provided by the percept sequence and whatever built-in knowledge the agent has.”
Environment Types • To understand which types of agents will work where, we have to understand the environment • We often describe the environment based on six attributes. • Fully/partially observable • Deterministic/stochastic • Episodic/sequential • Static/dynamic • Discrete/continuous • Single agent/multiagent
Environment Types • Categorization of environment tasks: • Fully/partially observableextent to which an agent’s sensors give it access to the complete state of the environment • Deterministic/stochastic (also strategic)extent to which the next state of the environment is determined by the current state and the current action
Environment Types • Categorization of environment tasks: • Episodic/sequentialextent to which the agent’s experience is divided into atomic episodes • Static/dynamicextent to which the environment can change while the agent is deliberating
Environment Types • Categorization of environment tasks: • Discrete/continuousextent to which state of the environment, time, percepts and actions of the agent are expressed as a set of discrete values • Single agent/multiagent
Environment Types • The environment type largely determines the agent design • The real world is (of course) partially observable, stochastic, sequential, dynamic, continuous, multi-agent
Chapter 2 :Problem Solving by Searching • In which we see how an agent can find a sequence of actions that achieves its goals when no single action will do. • Such agents must be able to: • Formulate a goal • Formulate the overall problem • Find a solution
Observable?? Deterministic?? Episodic?? Static?? Discrete?? Agents?? Yes Yes Either Yes Yes Either Appropriate environment for Searching Agents
Problem Types • Deterministic, fully observable single-state problem • Agent knows exactly which state it will be in • Solution is a sequence • Non-observable conformant problem • Agent may have no idea where it is • Solution (if any) is a sequence • Nondeterministic and/or partially observable contingency problem • percepts provide new information about current state • solution is a tree or policy • often interleave search, execution • Unknown state space exploration problem • “online” search
Problem Types • Deterministic, fully observable single-state problem • Non-observable conformant problem • Nondeterministic and/or partially observable contingency problem • Unknown state space exploration problem
Single-State Problem Formulation • A problem is defined by four items: • initial state • successor function (which actually defines all reachable states) • goal test • path cost (additive)e.g., sum of distances, number of actions executed, etc.C(x,a,y) is the step cost, assumed to be 0
Recently I gave you this problem • Three missionaries and three cannibals • Want to cross a river using one canoe. • Canoe can hold up to two people. • Can never be more cannibals than missionaries on either side of the river. • Aim: To get all safely across the river without any missionaries being eaten.