The SNePS Research Group

1. SNeRG The SNePS Research Group 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 Semantic Network Processing System The long-term goal of The SNePS 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 SNePS knowledge representation/reasoning system, and Cassie, its computerized cognitive agent. GLAIR Architecture Grounded Layered Architecture with Integrated Reasoning A Plan for Detonating unexploded landmines Knowledge Level all(a)(Agent(a) => ActPlan(Blowup(a, UXOs), Act(a, Cascade(SearchforUxo(a), WithSome+(obj, Near(a, obj), WithNew({ch ex}, {Charge(ch), Explosion(ex)}, Possess(a, ch), Cascade(Place(a, ch, obj), Hide(a), Waitfor(a, ex), SearchforUxo(a))), goto(a, SafeZone)))))) NL SNePS Perceptuo-Motor Level Sensory-Actuator Level Vision Interaction with Cassie the Fevahr Agent : Who are you? My name is `Cassie' and I am the SNePS cognitive agent. : 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? I saw Stu 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 and I see you. Sonar Motion Proprioception Example SNePS Ontology The Trial The Trail is an interactive drama for an immersive VR environment. The intelligent agents are SNePS-driven. Contextual Vocabulary Acquisition:From Algorithm to Curriculum Belief Base Revision with Reconsideration • PIs: William J. Rapaport (CSE & SNeRG) & Michael W. Kibby (Learning & Instruction Dept.) • CVA = computing a meaning for unknown word from contextual clues & prior knowledge • “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 brachetsInput: SNePS 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, B12 is a hart. A hart runs into King Arthur’s hall. • In the story, B13 is a hall. A white brachet is next to the hart. • In the story, B13 is King Arthur’s. The brachet bites the hart’s buttock. • In the story, B12 runs into B13 The knight picks up the brachet. • • A white brachet is next to the hart. The knight carries the brachet. • In the story, B14 is a brachet. The lady says that she wants the brachet. • In the story, B14 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.--> (defineNoun “brachet”) • …prior knowledge: only physical objects have color Definition of brachet: • 2.--> (defineNoun “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 UAV Current Belief Base TimeUAVINTELTROOPS T1 Red in D1,D2 Red in D1,D3 T2 Red in D1,D2 Red in D1,D3,C3,Bridge-D T3 Red in D1,D2 Red in D1,D3,C3,D4 Red in C3 Always TROOPS > UAV > INTEL Always TROOPS > INTEL > UAV INTEL Assertingbeliefs into the belief base (or KB) = Adding them to the KB = Stating them to be true. T1: 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) T2: UAV & INTEL again disagree. At T3, 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. BLUE TROOPS Identifying Perceptually Indistinguishable Objects: Is that the same one you saw before? Cassie’s view of the world showing two perceptually indistinguishable robots, one of whom she is following. Robots used in human subjects’ and Cassie’s tasks What Cassie can see: a table with glasses and a computer lab with two people SNeRG website: www.cse.buffalo.edu/sneps