Artificial Intelligence and Lisp #2 Introduction to Cognitive Agents and to Knowledge Representation

Software agents A piece of software that produces a behavior in terms of discrete 'actions', and which is perceived as an entity 'doing' these actions It is the instance of the software that is an agent Autonomous agent: decides itself Model-based agent: uses a model of its environment for selecting actions Cognitive agent: a model-based agent using a concept-based model of the environment

Uses of concept-based model Know procedures/ scripts/ methods … and be able to apply them Diagnose problems and resolve them Imagine what will happen Use earlier experience and adapt it (learning) Have facts and apply them Acquire facts

Additional uses of such a model Adapt earlier solutions to problems Identify relevant facts Structure a given problem and its solution Draw conclusions from selected facts Identify and apply constraints

Concept-based model supports Robustness Flexibility User-friendlyness

Scenario for this course: the Zoo Entities: animals, spaces for animals, food for them; zookeeper, guardians, veterinary,... Actions and events: move an animal, feed an animal, animal gets ill, treat illness, animal gives birth, animal dies, … Each course participant 'builds' his or her zoo and applies his/her agent to it Then (maybe) we connect the zoos together

Scenario for this lecture: Household Live illustrations These lead to the introduction of a notation which is presented in the lecture notes (part I)

Agent behavior frameworks Command-taking agent, with a certain intelligence in carrying out the commands Monitoring agent, in charge of maintaining the correct state in its environment Plan-executing agent, in charge of performing a given plan and monitoring that it proceeds as intended More complex cases?

Preparation framework Two phases: preparation phase and performance phase Example: prepare for a reception Performance phase: guests arrive and are entertained Preparation phase: think through what must be done in order to facilitate the performance phase.

Preparation framework (revised) Two phases: preparation phase and performance phase Example: prepare for a reception Performance phase: guests arrive and are entertained Preparation phase: identify situations that may arise, actions that will be required for dealing with these, and preconditions for those actions. Arrange that preconditions are satisfied.

Software architecture for AI applications Monitoring Operating system Programming language - CommonLisp Cognitive system platform - Leonardo Model-based agent - MIA Preparation Dealing w obstacles Diagnosis...Planning... Command execution

Is there an architecture for “universal artificial intelligence” ? SOAR proposal (Laird, Newell, Rosenbloom) presumes five steps performed cyclically: Input Elaboration Decision Application Output … and with possibility of recursion