1 / 13

Advanced AI

Advanced AI. Prof. Sarit Kraus Bar-Ilan University Slides adjusted from David Parkes from Harvard Univ. Different Goals of AI. sensors. percepts. ?. environment. agent. actions. actuators. An agent and its environment.

cfrawley
Download Presentation

Advanced AI

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Advanced AI Prof. Sarit Kraus Bar-Ilan University Slides adjusted from David Parkes from Harvard Univ. 89-950 Lecture 1

  2. Different Goals of AI 89-950 Lecture 1

  3. sensors percepts ? environment agent actions actuators An agent and its environment agent :Something that takes input (percepts) from its environment through sensors and takes actions upon its environment, using actuators. agent function: mapping from percept sequence to an action 89-950 Lecture 1

  4. What is an Agent? PROPERTY MEANING • Situated Sense and act in dynamic/uncertain environments • Flexible Reactive (responds to changes in the environment) • Autonomous Exercises control over its own actions • Goal-oriented Purposeful • Persistent Continuously running process • Social Interacts with other agents/people • Learning Adaptive • Mobile Able to transport itself 89-950 Lecture 1

  5. Examples • Medical diagnosis system • Foreign-language tutor • Web shopping program • Virtual humans for training, entertainment 89-950 Lecture 1

  6. Examples of how the agent function can be implemented • Table-driven agent • Simple reflex agent • Reflex agent with internal state • Agent with explicit goals • Utility-based agent • Learning agent More sophisticated 89-950 Lecture 1

  7. Consider a Taxi Driving Agent • Goal: correct destination, manage fuel consumption, minimize driving violations • Environment: roads, people, potholes, etc. • Actuators: gas pedal, breaks, etc. • Sensors: video camera, speed, etc. 89-950 Lecture 1

  8. 1. Table-driven agent An agent based on a pre-specified lookup table. It keeps track of percept sequence and just looks up the best action • Disadvantage: • Huge number of possible percepts • Takes long time to build the table • Not adaptive e.g. Taxi.  |P|t states, for |P| percepts, and lifetime T T t=1 27 MB/sec, 1 hour, 10^150 entries (c.f. 10^80 atoms in observable universe) 89-950 Lecture 1

  9. Simple Reflex Agent sensors What the world is like now Environment What action I should do now Condition - action rules actuators 2. Simple reflex agent Use a simple rule, and just the current percept, perform the action associated with that rule. table still too large, and current percept may not be enough, and no goal. 89-950 Lecture 1

  10. 3. Model-Based Reflex Agent sensors State How the world evolves What the world is like now What my actions do Environment What action I should do now Condition - action rules actuators maintain model of world to make-up for lack of percepts, table still too large, and still no goals. note flexible. 89-950 Lecture 1

  11. sensors State How the world evolves What the world is like now What my actions do Environment What it will be like if I do action A What action I should do now Goals actuators 4. Model Based, Goal Based deliberative, goal-based, choosing actions with search and Planning. more flexible (not pre-specified). 89-950 Lecture 1

  12. sensors State How the world evolves What the world is like now What my actions do What it will be like if I do action A Environment How happy I will be in such as a state Utility What action I should do now effectors 5. Utility-based agent refined measure of ``good’’ and ``bad’’, utility= happiness 89-950 Lecture 1

  13. Properties of environments Observable vs. Partially-observable (complete state of world is available to agent) Deterministic vs. no-deterministic (Stochastic) (no uncertainty about effects of actions) Static vs. Dynamic (do not need to observe while deliberate) Discrete vs. Continuous (state/percepts/actions/time) Single vs. Multiagent (cooperative vs. competitive) 89-950 Lecture 1

More Related