The SNe PS Research Group SNe RG Prof

The SNe. PS Research Group SNe. RG Prof. Stuart C. Shapiro, Prof. William J. Rapaport Prof. Carl Alphonce, Prof. Josephine Anstey, Prof. Debra T. Burhans, Prof. Michelle L. Gregory, Prof. Jean-Pierre A. Koenig, Prof. David R. Pierce Graduate Students: Jonathan Bona, Trupti Devdas Nayak, Albert Goldfain, Frances L. Johnson, Michael Kandefer, John F. Santore, Lunarso Sutanto Undergraduate Students: Vikranth B. Rao, Isidore Dinga. Madou Semantic Network Processing System The long-term goal of The SNe. PS Research Group is the design and construction of a natural-language-using computerized cognitive agent, and carrying out the research in artificial intelligence, computational linguistics, and cognitive science necessary for that endeavor. The three-part focus of the group is on knowledge representation, reasoning, and natural-language understanding and generation. The group is widely known for its development of the SNe. PS knowledge representation/reasoning system, and Cassie, its computerized cognitive agent. Cassie the FEVAHR Agent GLAIR Architecture A Plan for Detonating Unexploded Landmines (UXOs) Grounded Layered Architecture with Integrated Reasoning Knowledge Level NL SNe. PS Perceptuo-Motor Level Sensory-Actuator Level Natural Language Interaction with FEVAHR (Foveal Extra-Vehicular Activity Helper-Retriever) Cassie Vision Sonar Motion : Who are you? all(a)(Agent(a) => Act. Plan(Blowup(a, UXOs), Act(a, Cascade(Searchfor. Uxo(a), With. Some+(obj, Near(a, obj), With. New({ch ex}, {Charge(ch), Explosion(ex)}, Possess(a, ch), Cascade(Place(a, ch, obj), Hide(a), Waitfor(a, ex), Searchfor. Uxo(a))), goto(a, Safe. Zone)))))) My name is `Cassie' Proprioception and I am the SNe. PS cognitive agent. Example SNe. PS Ontology : Who did you talk to? I talked to Stu and I talked to Bill and I talked to Carl and I talked to David and I talked to Debbie and I talked to J. P. and I talked to Josephine and I talked to Michelle and I am talking to you. : Who did you see? The Trial The Trail is an I saw Stu interactive drama for an immersive VR environment. Its intelligent agents are SNe. PS-driven. and I saw Albert and I saw Fran and I saw John and I saw Jon and I saw Lunarso and I saw Michael and I saw Trupti Contextual Vocabulary Acquisition: From Algorithm to Curriculum and I see you. Belief Base Revision with Reconsideration UAV PIs: William J. Rapaport (CSE & SNe. RG) & Michael W. Kibby (Learning & Instruction Dept. ) • CVA = computing a meaning for unknown word from contextual clues & prior knowledge Current Belief Base Time UAV INTEL TROOPS T 1 Red in D 1, D 2 Red in D 1, D 3 T 2 Red in D 1, D 3, C 3, Bridge-D T 3 Red in D 1, D 2 Red in D 1, D 3, C 3, D 4 Always TROOPS > UAV > INTEL Always TROOPS > INTEL > UAV INTEL Red in C 3 Asserting beliefs into the belief base (or KB) = Adding them to the KB = Stating them to be true. T 1: UAV & INTEL disagree on Red troop location => contradiction. Consolidation makes a belief base consistent -- in this case by removing (or retracting) INTEL’s statement. = Contracting the KB by INTEL’s statement. (UAV > INTEL) BLUE T 2: UAV & INTEL again disagree. TROOPS At T 3, BLUE TROOPS confirm an INTEL belief over that of UAV So, we reverse the INTEL/UAV credibility order. Thus, UAV is disbelieved. Reconsideration of the KB is defined as consolidation of all base beliefs (current, or not). INTEL’s earlier beliefs are recaptured (= returned to the KB), and UAV’s are retracted. Identifying Perceptually Indistinguishable Objects: Is that the same one you saw before? Crystal Cassie’s view of the world showing two perceptually indistinguishable robots, one of whom she is following. • “There came a white hart running into the hall with a white brachet next to him, and thirty couples of black hounds came running after them. As the hart went by the sideboard, the white brachet bit him. The knight arose, took up the brachet and rode away with the brachet. A lady came in and cried aloud to King Arthur, ‘Sire, the brachet is mine’. There was the white brachet which bayed at him fast. The hart lay dead; a brachet was biting on his throat, and other hounds came behind. ” [Morte D’Arthur] • Cassie learns what “brachet” means: From above text + prior knowledge about harts, animals, King Arthur, etc. ; no info about brachets. Input: SNe. PS version of simplified English narrative. Output: Definition frame (varies with context and prior knowledge): 1. First Sentence: • A hart runs into King Arthur’s hall. 3. Full Story: – In the story, B 12 is a hart. A hart runs into King Arthur’s hall. – In the story, B 13 is a hall. A white brachet is next to the hart. – In the story, B 13 is King Arthur’s. The brachet bites the hart’s buttock. – In the story, B 12 runs into B 13 The knight picks up the brachet. • A white brachet is next to the hart. The knight carries the brachet. – In the story, B 14 is a brachet. The lady says that she wants the brachet. – In the story, B 14 has the property “white”. The brachet bays at Sir Tor. Therefore, brachets are physical objects. • + prior knowledge: only hunting dogs bay • deduced while reading, using… 4. --> (define. Noun “brachet”) • …prior knowledge: only physical objects have color Definition of brachet: 2. --> (define. Noun “brachet”) Class Inclusions: hound, dog, Definition of brachet: Possible Actions: bite buttock, bay, hunt, Class Inclusions: phys obj, Possible Properties: valuable, small, white, Possible Properties: white, 5. OED: brachet: a kind of hound which hunts by scent • Application: Development of classroom curriculum to teach CVA, based on our CVA algorithms Robots used in human subjects’ and Crystal Cassie’s tasks What Crystal Cassie can see: a table with glasses and a computer lab with two people SNe. RG website: www. cse. buffalo. edu/sneps