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C463 / B551 Artificial Intelligence

C463 / B551 Artificial Intelligence Dana Vrajitoru Intelligent Agents Intelligent Agent Agent : entity in a program or environment capable of generating action. An agent uses perception of the environment to make decisions about actions to take.

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C463 / B551 Artificial Intelligence

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  1. C463 / B551Artificial Intelligence Dana Vrajitoru Intelligent Agents

  2. Intelligent Agent • Agent: entity in a program or environment capable of generating action. • An agent uses perception of the environment to make decisions about actions to take. • The perception capability is usually called a sensor. • The actions can depend on the most recent perception or on the entire history (percept sequence). Artificial Intelligence – D. Vrajitoru

  3. Agent Function • The agent function is a mathematical function that maps a sequence of perceptions into action. • The function is implemented as the agent program. • The part of the agent taking an action is called an actuator. • environment  sensors  agent function  actuators  environment Artificial Intelligence – D. Vrajitoru

  4. Environment Environment Sensors Percept (Observations) Agent Function Agent Actuator Environment Action Environment Artificial Intelligence – D. Vrajitoru

  5. Rational Agent • A rational agent is one that can take the right decision in every situation. • Performance measure: a set of criteria/test bed for the success of the agent's behavior. • The performance measures should be based on the desired effect of the agent on the environment. Artificial Intelligence – D. Vrajitoru

  6. Rationality • The agent's rational behavior depends on: • the performance measure that defines success • the agent's knowledge of the environment • the action that it is capable of performing • the current sequence of perceptions. • Definition: for every possible percept sequence, the agent is expected to take an action that will maximize its performance measure. Artificial Intelligence – D. Vrajitoru

  7. Agent Autonomy • An agent is omniscient if it knows the actual outcome of its actions. Not possible in practice. • An environment can sometimes be completely known in advance. • Exploration: sometimes an agent must perform an action to gather information (to increase perception). • Autonomy: the capacity to compensate for partial or incorrect prior knowledge (usually by learning). Artificial Intelligence – D. Vrajitoru

  8. Environment • Task environment – the problem that the agent is a solution to. • Properties: • Observable - fully or partiallyA fully observable environment needs less representation. • Deterministic or stochasticStrategic –deterministic except for the actions of other agents. Artificial Intelligence – D. Vrajitoru

  9. Environment • Episodic or sequentialSequential – future actions depend on the previous ones. Episodic – individual unrelated tasks for the agent to solve. • Static – dynamic • Discrete – continuous • Single agent – multi agentMultiple agents can be competitive or cooperative. Artificial Intelligence – D. Vrajitoru

  10. More Definitions of Agents • "An agent is a persistent software entity dedicated to a specific purpose. " (Smith, Cypher, and Spohrer 94 ) • "Intelligent agents are software entities that carry out some set of operations on behalf of a user or another program with some degree of independence or autonomy, and in so doing, employ some knowledge or representation of the user's goals or desires." (IBM) • "Intelligent agents continuously perform three functions: perception of dynamic conditions in the environment; action to affect conditions in the environment; and reasoning to interpret perceptions, solve problems, draw inferences, and determine actions. "(Hayes-Roth 94) Artificial Intelligence – D. Vrajitoru

  11. Agent vs. Program • Size – an agent is usually smaller than a program. • Purpose – an agent has a specific purpose while programs are multi-functional. • Persistence – an agent's life span is not entirely dependent on a user launching and quitting it. • Autonomy – an agent doesn't need the user's input to function. Artificial Intelligence – D. Vrajitoru

  12. Table-driven agents: the function consists in a lookup table of actions to be taken for every possible state of the environment. If the environment has n variables, each with t possible states, then the table size is tn. Only works for a small number of possible states for the environment. Simple reflex agents: deciding on the action to take based only on the current perception and not on the history of perceptions. Based on the condition-action rule: (if (condition) action) Works if the environment is fully observable Simple Agents Artificial Intelligence – D. Vrajitoru

  13. (defuntable_agent (percept) (let ((action t)) (push percept percepts) (setq action (lookup percepts table)) action)) (defunreflex_agent (percept) (let ((rule t) (state t) (action t)) (setq state (interpret percept)) (setq rule (match state)) (setq action (decision rule)) action)) Artificial Intelligence – D. Vrajitoru

  14. percepts = [] table = {} def table_agent (percept): action = True percepts.append(percept) action = lookup(percepts, table) return action def reflex_agent (percept): state = interpret(percept) rule = match(state) action = decision(rule) return action Artificial Intelligence – D. Vrajitoru

  15. Model-Based Reflex Agents • If the world is not fully observable, the agent must remember observations about the parts of the environment it cannot currently observe. • This usually requires an internal representation of the world (or internal state). • Since this representation is a model of the world, we call this model-based agent. Artificial Intelligence – D. Vrajitoru

  16. (setq state t) ; the world model (setq action nil) ; latest action (defunmodel_reflex_agent (percept) (let ((rule t)) (setq state (update_state state action percept)) (setq rule (match state)) (setq action (decision rule)) action)) Artificial Intelligence – D. Vrajitoru

  17. state = True # the world model action = False # latest action def model_reflex_agent (percept) state = update_state(state, action, percept) rule = match(state) action = decision(rule) return action Artificial Intelligence – D. Vrajitoru

  18. Goal-Driven Agents • The agent has a purpose and the action to be taken depends on the current state and on what it tries to accomplish (the goal). • In some cases the goal is easy to achieve. In others it involves planning, sifting through a search space for possible solutions, developing a strategy. • Utility-based agents: the agent is aware of a utility function that estimates how close the current state is to the agent's goal. Artificial Intelligence – D. Vrajitoru

  19. Learning Agents • Agents capable of acquiring new competence through observations and actions. • Components: • learning element (modifies the performance element) • performance element (selects actions) • feedback element (critic) • exploration element (problem generator). Artificial Intelligence – D. Vrajitoru

  20. Other Types of Agents • Temporarily continuous – a continuously running process, • Communicative agent – exchanging information with other agents to complete its task. • Mobile agent – capable of moving from one machine to another one (or from one environment to another). • Flexible agent – whose actions are not scripted. • Character – an agent with conversation skills, personality, and even emotional state. Artificial Intelligence – D. Vrajitoru

  21. Agent Classification Artificial Intelligence – D. Vrajitoru

  22. Agent Example • A file manager agent. • Sensors: commands like ls, du, pwd. • Actuators: commands like tar, gzip, cd, rm, cp, etc. • Purpose: compress and archive files that have not been used in a while. • Environment: fully observable (but partially observed), deterministic (strategic), episodic, dynamic, discrete. Artificial Intelligence – D. Vrajitoru

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