Artificial Intelligence Flakey A Communicating Agent

Flakey - A Communicating Agent  Flakey as Communicating Agent  Case Frame Representation  Concrete and Generic Actions  Effects of Actions  Inference / Reasoning  Two Types of Questions

Flakey as Communicating Agent "Flakey, bring this file to Karen." verb determiner noun preposition noun Noun Phrase Prepositional Phrase V NP PP agent action patiens recipient listener head direct object indirect object

Case Frames for Representing NL "Flakey, bring this file to Karen.” head direct object indirect object case frame action: bringhead-verb patiens: file-1direct object recipient: Karen indirect object

Flakey - Question Answering I agent: Flakey action: bring patiens: file1 destination: Karen Answer: “I brought the file to Karen.” Compare to stored case frames: “Flakey, where did you bring the file.” agent: Flakey action: bring patiens: file1 destination: where?

Flakey - Question Answering II Q: “Flakey, where is the file.” case frame action/status: is subject: the fileidentify with file1 location: ? refers to loc of file1 Access dynamic KB (world state) Stored from effect of bring-action or pre-stored:... at (file1, Karen),... have (Karen, file1),... A: “The file is at Karen.” or "Karen has the file."

Mapping Case Frames to Actions robot action precondition: have (Flakey, file1) action: bring (Flakey, file1, Karen) effect: not (have (Flakey, file1)) and have (Karen, file1) case frame agent:Flakey action: bringhead patiens: file-1direct object recipient: Karen indirect object

Concrete and Generic Actions concrete "bring" action (generated instance) precondition: have (Flakey, file1) action: give (Flakey, file1, Karen) effect: not (have (Flakey, file1)) and have (Karen, file1) generic "bring" action (stored concept) precondition: have (agent, object) action: give (agent, object, recipient) effect: not (have (agent, object)) and (have (recipient, object))

Effects of Actions - Change KB Preconditions and effects specify world states. World states are stored in the knowledge base (KB). concrete action: bring (Flakey, file1, Karen) precondition: have (Flakey, file1) effect: not (have (Flakey, file1)) and have (Karen, file1) effect of this action delete from KB have (Flakey, file1) add to KB have (Karen, file1)

Flakey - Reasoning, Inference Integrate General Rules (Axioms; Theory) Reasoning / Inference have (Flakey, object)  at (Flakey, here)  at (object, here) have (Karen, file1)  at (Karen, Karen's-office)  at (file1, Karen's-office) Axiom  x  y  loc: (have (x, y)  (at (x, loc)  at (y, loc)))

Conclusion Artificial Intelligence and Agents Flakey - Example Natural Language Processing Reasoning Questions?

References Christel Kemke, Artificial Intelligence, Stuart Russell and Peter Norvig, Artificial Intelligence – A Modern Approach, Prentice Hall, 1995 & 2003 SRI Video Archives, PBS Video on Flakey,