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ARTIFICIAL INTELLIGENCE [INTELLIGENT AGENTS PARADIGM] Professor Janis Grundspenkis Riga Technical University Faculty of Computer Science and Information.

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Presentation on theme: "ARTIFICIAL INTELLIGENCE [INTELLIGENT AGENTS PARADIGM] Professor Janis Grundspenkis Riga Technical University Faculty of Computer Science and Information."— Presentation transcript:

1 ARTIFICIAL INTELLIGENCE [INTELLIGENT AGENTS PARADIGM] Professor Janis Grundspenkis Riga Technical University Faculty of Computer Science and Information Technology Institute of Applied Computer Systems Department of Systems Theory and Design E-mail: Janis.Grundspenkis@rtu.lv SIMPLE INTELLIGENT AGENTS

2 Intelligent Agents and Their Programs An agent is just something that perceives and acts Variety of definitions Agent functions: mapping percepts to actions

3 Rational Agent One that does the right thing How and when to evaluate the agents success? What is rational depends on four factors: –Performance measure (for the how?) –Percept sequence –Knowledge about the environment –Actions Ideal rational and omniscient agents

4 Autonomous Agent One whose actions are not based completely on built-in knowledge One whose actions are based on both built-in knowledge and own experience Initial knowledge provides an ability to learn A truly autonomous agent can adapt to a wide variety of environments

5 Structures of Intelligent Agents Agent is a program and an architecture Initial phase for agent program is to understand and describe: –Percepts –Actions –Goals –Environment

6 Agent Programs (1) Skeleton-Agent > Single percept Update-Memory(memory, percept) Choose-Best-Action(memory) > Action Update-Memory(memory, action) Return: action

7 Agent Programs (2) Table-Driven-Agent > Percept sequences Look-Up(percepts, table) Return: action

8 Agent Programs (3) Rule-Based Agent (simple reflex agent) > Percept Interpretation(percept) > Rule match Interpreted percept IF pattern > Rule Firing THEN pattern > Action Return: action Applications: logical reasoning systems

9 Agent Programs (4) Model-Based Agent > Percept Update-State(state, percept) > Rule match State IF pattern > Rule Firing THEN pattern > Action Return: action Applications: logical reasoning systems, decision making agents

10 Agent Programs (5) Goal-Based Agent > Goal > Inference > Search and Planning Applications: planning agents

11 Agent Programs (6) Utility-Based Agent > Utility Applications: game playing, decision making agents


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