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Introduction Agents Rational Agents Classes of Agents Environments Conclusion References Introduction Agents Rational Agents Classes of Agents Environments Conclusion References Agenda Artificial Intelligence 3. Intelligent Agents 1 Introduction 2 What is an Agent? 3 What is a Rational Agent? 4 Different Classes of Agents 5 Different Classes of Environments 6 Conclusion Let’s Get a Little Orientation Here Jörg Hoffmann Wolfgang Wahlster Summer Term 2017 Hoffmann and Wahlster Introduction Agents Artificial Intelligence Rational Agents Classes of Agents Chapter 3: Intelligent Agents Environments Conclusion 1/34 References Introduction Hoffmann and Wahlster Introduction Agents Artificial Intelligence Rational Agents Classes of Agents Chapter 3: Intelligent Agents Environments Conclusion 2/34 References Disclaimer → A central aspect of intelligence (and one possible way to define it) is the ability to act successfully in the world (cf. Chapter 1). This chapter provides a broad categorization of, and terminology for, talking about that: Useful for a research field to establish a common language. → The concepts and explanations in this chapter are very broad and superficial. In the remainder of the course, we will consider in detail particular problems relating to decision-making, and specify algorithms for solving these. Useful for you to get a rough overview. → We’ll think a bit about what it means to “behave rationally”, what are possible architectures for achieving such behavior with a computer, and what are relevant properties of the world we need to act in. Hoffmann and Wahlster Artificial Intelligence Chapter 3: Intelligent Agents 4/34 Hoffmann and Wahlster Artificial Intelligence Chapter 3: Intelligent Agents 5/34 Introduction Agents Rational Agents Classes of Agents Environments Conclusion References What is an “Agent” in AI? Introduction Agents Rational Agents Classes of Agents Environments Conclusion References Rational Agents . . . Agents: . . . do “the right thing”! Perceive the environment through sensors (→ percepts). Act upon the environment through actuators (→ actions). Agent Sensors → Meaning of “do the right thing”: Rational agents select their actions so as to maximize a performance measure. → What’s the performance measure of an autonomous vacuum cleaner? Percepts Environment ? Actions Actuators → Examples? Hoffmann and Wahlster Introduction Agents Artificial Intelligence Rational Agents Chapter 3: Intelligent Agents Classes of Agents Environments Conclusion 7/34 References Actually, Rational Agents . . . Introduction Agents Artificial Intelligence Rational Agents Classes of Agents Chapter 3: Intelligent Agents Environments Conclusion 9/34 References So, What Is a Rational Agent? . . . ATTEMPT to do “the right thing”! Mapping your input to the best possible output: → The hypothetical best case (“the right thing”) is often unattainable. → The agent might not be able to perceive all relevant information. (Is there dirt under this bed? Is this a hamster or a shoe?) Rationality vs. Omniscience: An omniscient agent knows everything about the enviroment, and knows the actual effects of its actions. A rational agent just makes the best of what it has at its disposal, maximizing expected performance given its percepts and knowledge. Performance measure × Percepts × Knowledge → Action An agent has a performance measure M and a set A of possible actions. Given a percept sequence P , as well as knowledge K about the world, it selects an action a ∈ A. The action a is optimal if it maximizes the expected value of M , given the evidence provided by P and K. The agent is rational if it always chooses an optimal a. → If the vacuum cleaner bumps into the Hamster, then this can be rational in case → Note: If observation actions are required, they are elements of A, i.e., the agent must perceive actively. Example: → Example? Hoffmann and Wahlster Hoffmann and Wahlster Artificial Intelligence Chapter 3: Intelligent Agents 10/34 Hoffmann and Wahlster Artificial Intelligence Chapter 3: Intelligent Agents 11/34 Introduction Agents Rational Agents Classes of Agents Environments Conclusion References A Rational Agent is an Optimal Action Choice Function? Introduction Agents Rational Agents Classes of Agents Environments Conclusion References Examples of Agents: PEAS Descriptions We also need to realize the agent, through: an agent program, executed on an architecture which also provides an interface to the environment (percepts, actions). → Agent = Architecture + Program. Practical limitations: Our definition captures limitations on percepts and knowledge. It does not capture computational limitations (often, determining an optimal choice would take too much time/memory). → In practice, we often merely approximate the rational decision. Hoffmann and Wahlster Introduction Agents Artificial Intelligence Rational Agents Chapter 3: Intelligent Agents Classes of Agents Environments Conclusion 12/34 References Questionnaire Introduction Agents Artificial Intelligence Rational Agents Classes of Agents Chapter 3: Intelligent Agents Environments Conclusion 13/34 References Questionnaire Answers Question! Which are agents? (A): James Bond. (C): Vacuum cleaner. (B): Your dog. (D): Thermometer. Question! Who is rational? (A): James Bond, crossing the street without looking. (C): Vacuum cleaner, deciding to clean under your bed. (B): Your dog, crossing the street without looking. (D): Thermostat, deciding to cool down your fridge. Hoffmann and Wahlster Hoffmann and Wahlster Artificial Intelligence Chapter 3: Intelligent Agents 14/34 Hoffmann and Wahlster Artificial Intelligence Chapter 3: Intelligent Agents 15/34 Introduction Agents Rational Agents Classes of Agents Environments Conclusion References Table-Driven Agents Introduction Agents Rational Agents Classes of Agents Environments More useful, but still very simple, method for choosing actions: Condition-Action Rules (note: raw sensor data interpreted, using a world model, prior to evaluating the rules) Sensors append percept to the end of percepts action ← L OOKUP( percepts , table) return action State What my actions do → Why is this not a good idea? What action I should do now Condition-action rules function R EFLEX -VACUUM -AGENT( [location,status ]) returns an action Agent if status = Dirty then return Suck else if location = A then return Right else if location = B then return Left The agent program for a simple reflex Chapter agent in3:the two-state vacuum environment. Artificial Intelligence Intelligent Agents 17/34 This program implements the agent function tabulated in Figure ??. Classes of Agents Environments Conclusion References function S IMPLE -R EFLEX -AGENT( percept ) returns an action Goal-Based Agents persistent: rules, a set of condition–action rules Hoffmann and Wahlster Introduction Agents Artificial Intelligence Rational Agents Chapter 3: Intelligent Agents Classes of Agents Environments Conclusion 18/34 References Learning Agents state ← I NTERPRET-I NPUT( percept ) Often, doing the right thing requires considering the future: rule ← RULE -M ATCH(state, rules) action ← rule.ACTION return action Actuators → Example? Figure 2.4 Hoffmann and Wahlster Rational Agents Environment What the world is like now How the world evolves Figure 2.3 The TABLE -D RIVEN -AGENT program is invoked for each new percept and returns an action each time. It retains the complete percept sequence in memory. Agents References Reflex Agents function TABLE -D RIVEN -AGENT( percept ) returns an action persistent: percepts , a sequence, initially empty table, a table of actions, indexed by percept sequences, initially fully specified Introduction Conclusion It is typically useful to be able to learn: (extreme case: start with 0 knowledge in an unknown environment) Sensors Performance standard What the world Critic State What it will be like if I do action A 2 Goals Agent Sensors feedback changes Learning element What action I should do now knowledge learning goals Problem generator Actuators Actuators Agent → Example? Hoffmann and Wahlster Performance element Environment What my actions do Environment Figure 2.6 A How simple reflex agent. It acts according is like now to a rule whose condition matches the current the world evolves state, as defined by the percept. → Performance element: Selects actions (exploitation). Learning element: Learns new knowledge. Critic: Measures performance. Problem generator: Suggests actions favoring informative learning experiences (exploration). Artificial Intelligence Chapter 3: Intelligent Agents 19/34 Hoffmann and Wahlster Artificial Intelligence Chapter 3: Intelligent Agents 20/34 Introduction Agents Rational Agents Classes of Agents Environments Conclusion References Domain-Specific vs. General Agents Introduction Agents Rational Agents Classes of Agents Environments Conclusion References Questionnaire Question! What kind of agent are you? (A): Table-Driven (C): Learning vs. Solver specific to a particular problem (“domain”). vs. Solver based on description in a general problem-description language (e.g., the rules of any board game). More efficient. vs. More intelligent. Hoffmann and Wahlster Introduction Agents Artificial Intelligence Rational Agents Classes of Agents Chapter 3: Intelligent Agents Environments Conclusion 21/34 References The Environment of Rational Agents Hoffmann and Wahlster Introduction Agents (B): Reflex Agent (D): Domain-Specific Artificial Intelligence Rational Agents Classes of Agents Chapter 3: Intelligent Agents Environments Conclusion 22/34 References The Environment of Rational Agents, ctd. Accessible vs. inaccessible (fully observable vs. partially observable) Static vs. dynamic Are the relevant aspects of the environment accessible to the sensors? Can the environment change while the agent is deliberating? If the environment does not change, but the agent’s performance score changes, the environment is called semi-dynamic. Deterministic vs. stochastic Is the next state of the environment completely determined by the current state and the selected action? Discrete vs. continuous Is the environment discrete or continuous? If the only non-determinism are actions of other agents, the environment is called strategic. Single agent vs. multi-agent Is there just one agent, or several of them? Episodic vs. sequential There are competitive and cooperative multi-agent scenarios. Can the quality of an action be evaluated within an episode (perception + action), or are future developments decisive? Hoffmann and Wahlster Artificial Intelligence Chapter 3: Intelligent Agents 24/34 Hoffmann and Wahlster Artificial Intelligence Chapter 3: Intelligent Agents 25/34 Introduction Agents Rational Agents Classes of Agents Environments Conclusion References Examples of Environments Introduction Agents Rational Agents Classes of Agents Environments Conclusion References Classifying AI Areas Many sub-areas of AI can be classified by: Domain-specific vs. general. The environment. (Particular agent architectures sometimes also play a role, especially in Robotics.) → The same is true of the sub-topics in this course. The focus is on general methods (a bias in much of the AI field), and simple environments (after all, it’s an introductory course only). → Up next: A rough classification of our topics, in these terms. Hoffmann and Wahlster Introduction Agents Artificial Intelligence Rational Agents Chapter 3: Intelligent Agents Classes of Agents Environments Conclusion 26/34 References Classifying AI Areas: Our Topics Hoffmann and Wahlster Introduction Agents Artificial Intelligence Rational Agents Chapter 3: Intelligent Agents Classes of Agents Environments Conclusion 27/34 References Classifying AI Areas: Our Topics Planning Environment: Classical Search Environment: Fully observable vs. partially observable. Fully observable vs. partially observable. Deterministic vs. stochastic. Deterministic vs. stochastic. Episodic vs. sequential. Episodic vs. sequential. Static vs. dynamic. Static vs. dynamic. Discrete vs. continuous. Discrete vs. continuous. Single-agent vs. multi-agent. Single-agent vs. multi-agent. Approach: Approach: Domain-specific vs. general. Domain-specific vs. general. → Planning formalisms and approaches exist also for any and all of partial observability, and stochastic/dynamic/continuous/multi-agent settings. Hoffmann and Wahlster Artificial Intelligence Chapter 3: Intelligent Agents 28/34 Hoffmann and Wahlster Artificial Intelligence Chapter 3: Intelligent Agents 28/34 Introduction Agents Rational Agents Classes of Agents Environments Conclusion References Classifying AI Areas: Our Topics Introduction Agents Rational Agents Classes of Agents Environments General Game Playing (not covered in this course) Environment: Fully observable vs. partially observable. Fully observable vs. partially observable. Deterministic vs. stochastic. Deterministic vs. stochastic. Episodic vs. sequential. Episodic vs. sequential. Static vs. dynamic. Static vs. dynamic. Discrete vs. continuous. Discrete vs. continuous. Single-agent vs. multi-agent. Single-agent vs. multi-agent. Approach: Approach: Domain-specific vs. general. Domain-specific vs. general. → Adversarial search formalisms and approaches exist also for partial observability and stochastic settings. Introduction Agents Artificial Intelligence Rational Agents Chapter 3: Intelligent Agents Classes of Agents Environments Conclusion → General game playing formalisms and approaches exist also for partial observability and stochastic settings. 28/34 References Classifying AI Areas: Our Topics Hoffmann and Wahlster Introduction Agents Artificial Intelligence Rational Agents Chapter 3: Intelligent Agents Classes of Agents Environments Conclusion 28/34 References Classifying AI Areas: Our Topics Constraint Satisfaction & Reasoning Environment: Probabilistic Reasoning (not covered in this course) Environment: Fully observable vs. partially observable. Fully observable vs. partially observable. Deterministic vs. stochastic. Deterministic vs. stochastic. Episodic vs. sequential. Episodic vs. sequential. Static vs. dynamic. Static vs. dynamic. Discrete vs. continuous. Discrete vs. continuous. Single-agent vs. multi-agent. Single-agent vs. multi-agent. Approach: Approach: Domain-specific vs. general. Hoffmann and Wahlster References Classifying AI Areas: Our Topics Adversarial Search Environment: Hoffmann and Wahlster Conclusion Artificial Intelligence Domain-specific vs. general. Chapter 3: Intelligent Agents 28/34 Hoffmann and Wahlster Artificial Intelligence Chapter 3: Intelligent Agents 28/34 Introduction Agents Rational Agents Classes of Agents Environments Conclusion References Questionnaire (B): Episodic. (D): Single-Agent. Question! Your own environment is? (A): Fully Observable. (C): Static. (B): Episodic. (D): Single-Agent. Introduction Agents Agents Rational Agents Classes of Agents Environments Conclusion References Questionnaire Answers Question! James Bond’s environment is? (A): Fully Observable. (C): Static. Hoffmann and Wahlster Introduction Artificial Intelligence Rational Agents Classes of Agents Chapter 3: Intelligent Agents Environments Conclusion 29/34 References Summary Hoffmann and Wahlster Introduction Agents Artificial Intelligence Rational Agents Classes of Agents Chapter 3: Intelligent Agents Environments Conclusion 30/34 References Reading An agent is something that perceives and acts. It consists of an architecture and an agent program. A rational agent always takes the action that maximizes its expected performance, subject to the percept sequence and its environment knowledge. There are a variety of agent designs: Reflex agents respond to percepts by condition-action rules. Goal-based agents work towards goals. Utility-based agents make trade-offs using a utility function. Learning agents improve their behavior over time. Chapter 2: Intelligent Agents [Russell and Norvig (2010)]. Content: A much more detailed description of the subjects I overviewed here (agents, agent architectures, environments). Just as broad and superficial, though. Some environments are more demanding than others . . . . . . your own, and that of James Bond, are the most difficult. Hoffmann and Wahlster Artificial Intelligence Chapter 3: Intelligent Agents 32/34 Hoffmann and Wahlster Artificial Intelligence Chapter 3: Intelligent Agents 33/34 Introduction Agents Rational Agents Classes of Agents Environments Conclusion References References I Stuart Russell and Peter Norvig. Artificial Intelligence: A Modern Approach (Third Edition). Prentice-Hall, Englewood Cliffs, NJ, 2010. Hoffmann and Wahlster Artificial Intelligence Chapter 3: Intelligent Agents 34/34