Agen Cerdas: Artificial Intelligence Agents PDF

Agen Cerdas Pertemuan 02 Pra-S2 Pengantar Kecerdasan Artifisial What is an Agent? in general, an entity that interacts with its environment perception through sensors actions through effectors or actuators Percepts...

Agen Cerdas Pertemuan 02 Pra-S2 Pengantar Kecerdasan Artifisial What is an Agent? in general, an entity that interacts with its environment perception through sensors actions through effectors or actuators Percepts sensors Environment Agent Actuators 2 Actions Examples of Agents human agent eyes, ears, skin, taste buds, etc. for sensors hands, fingers, legs, mouth, etc. for actuators powered by muscles robot camera, infrared, bumper, etc. for sensors grippers, wheels, lights, speakers, etc. for actuators often powered by motors software agent functions as sensors information provided as input to functions in the form of encoded bit strings or symbols functions as actuators results deliver the output 3 Agents and Environments an agent perceives its environment through sensors the complete set of inputs at a given time is called a percept the current percept, or a sequence of percepts may influence the actions of an agent it can change the environment through actuators an operation involving an actuator is called an action actions can be grouped into action sequences 4 Agents and Their Actions a rational agent does “the right thing” the action that leads to the best outcome under the given circumstances an agent function maps percept sequences to actions abstract mathematical description an agent program is a concrete implementation of the respective function it runs on a specific agent architecture (“platform”) problems: what is “ the right thing” how do you measure the “best outcome” 5 Performance of Agents criteria for measuring the outcome and the expenses of the agent often subjective, but should be objective task dependent time may be important 6 Performance Evaluation Examples vacuum agent number of tiles cleaned during a certain period based on the agent’s report, or validated by an objective authority doesn’t consider expenses of the agent, side effects energy, noise, loss of useful objects, damaged furniture, scratched floor might lead to unwanted activities agent re-cleans clean tiles, covers only part of the room, drops dirt on tiles to have more tiles to clean, etc. 7 Rational Agent 8 Rational Agent selects the action that is expected to maximize its performance based on a performance measure depends on the percept sequence, background knowledge, and feasible actions 9 Rational Agent Considerations performance measure for the successful completion of a task complete perceptual history (percept sequence) background knowledge especially about the environment dimensions, structure, basic “laws” task, user, other agents feasible actions capabilities of the agent 10 Omniscience a rational agent is not omniscient it doesn’t know the actual outcome of its actions it may not know certain aspects of its environment Limited Rationality rationality takes into account the limitations of the agent percept sequence, background knowledge, feasible actions it deals with the expected outcome of actions 12 PEAS 13  PEAS Merancang PEAS: Agent - Performance/Pengukuran performansi: aman, cepat, tidak melanggar aturan lalu lintas, kenyamanan penumpang Percept Action - Environment/Lingkungan: jalan, rambu-rambu lalu lintas, kendaraan lain, penumpang Environ- ment - Actuator: kemudi, gas, rem, klakson - Sensor: kamera, sonar, speedometer, GPS 14 PEAS Description Template Performance How well does the agent solve the task at hand? Measures How is this measured? Environment Important aspects of theurroundings beyond the control of the agent: Actuators Determine the actions the agent can perform. Sensors Provide information about the current state of the environment. Contoh: Designing an automated taxi driver P : safe, fast, legal, comfortable trip, maximize profits. E: Roads, other traffic, pedestrians, customers A: steering wheel, accelerator, brake, signal, horn S: cameras, sonar, speedometer, GPS, odometer, engine sensors, keyboard. 16 Latihan Rancanglah agen cerdas berdasarkan prinsip PEAS 17 Jenis-jenis Lingkungan 18  Environment Type Fully observable (vs. An agent's sensors give it access to the complete partially observable) state of the environment at each point in time. Deterministic(vs. The next state of the environment is completely stochastic) determined by the current state and the action executed by the agent. (If the environment is deterministic except for the actions of other agents, then the environment is strategic) Episodic (vs. sequential) The agent's experience is divided into atomic "episodes" (each episode consists of the agent perceiving and then performing a single action), and the choice of action in each episode depends only on the episode itself 19  Environment Type (2) Static (vs. dynamic) The environment is unchanged while an agent is deliberating. Discrete(vs. A limited number of distinct, clearly defined continuous) percepts and actions. Single agent(vs. An agent operating by itself in an multiagent) environment Known(vs Unknown) This distinction refers not to the environment itself but to the agent’s (or designer’s) state of knowledge about the “laws of physics” of the environment 20  Example on Environment Type 21 Agent Program Types different ways of achieving the mapping from percepts to actions different levels of complexity simple reflex agents model-based agents keep track of the world goal-based agents Buatlah ringkasan untuk kelima jenis agent work towards a goal program berdasarkan buku referensi karangan Russel. utility-based agents Upload file Anda pada folder Tugas1 di gdrive learning agents kelas Format filename: Tugas1_nama Terima kasih