S.C. Shapiro Symbol-Anchoring in Cassie Stuart C. Shapiro and Haythem O. Ismail Department of Computer Science and Engineering and Center for.

Slides:



Advertisements
Similar presentations
1 Knowledge Representation Introduction KR and Logic.
Advertisements

Pat Langley Computational Learning Laboratory Center for the Study of Language and Information Stanford University, Stanford, California USA
Pat Langley Computational Learning Laboratory Center for the Study of Language and Information Stanford University, Stanford, California USA
Pat Langley School of Computing and Informatics Arizona State University Tempe, Arizona USA A Unified Cognitive Architecture for Embodied Agents Thanks.
SPECIFYING MODALITIES IN THE MGLAIR ARCHITECTURE Stuart C. Shapiro and Jonathan P. Bona Department of Computer Science and Engineering And Center for Cognitive.
S.C. Shapiro Knowledge Representation and Reasoning Stuart C. Shapiro Professor, CSE Director, SNePS Research Group Member, Center for Cognitive.
Research in Knowledge Representation and Reasoning Stuart C. Shapiro Department of Computer Science & Engineering Center for MultiSource Information.
SWE Introduction to Software Engineering
CPSC 322, Lecture 19Slide 1 Propositional Logic Intro, Syntax Computer Science cpsc322, Lecture 19 (Textbook Chpt ) February, 23, 2009.
The GLAIR Architecture for Cognitive Robotics Stuart C. Shapiro Department of Computer Science & Engineering and Center for Cognitive Science.
The GLAIR Cognitive Architecture and Prospects for Consciousness Stuart C. Shapiro Department of Computer Science & Engineering and Center.
A Categorization of Contextual Constraints Michael Kandefer and Stuart C. Shapiro University at Buffalo Department of Computer Science and Engineering.
The GLAIR Cognitive Architecture Stuart C. Shapiro and Jonathan P. Bona Department of Computer Science & Engineering Center for Cognitive Science.
The GLAIR Architecture for Cognitive Robots Stuart C. Shapiro Department of Computer Science & Engineering and Center for Cognitive Science.
Knowledge Representation for Self-Aware Computer Systems Stuart C. Shapiro Department of Computer Science and Engineering, and Center for Cognitive.
S.C. Shapiro Development of a Cognitive Agent Stuart C. Shapiro Department of Computer Science and Engineering and Center for Cognitive Science.
S.C. Shapiro Endowing Agents with a Personal Sense of Time Haythem O. Ismail & Stuart C. Shapiro Department of Computer Science and Engineering.
S.C. Shapiro Knowledge Representation for Natural Language Competence Stuart C. Shapiro Department of Computer Science and Engineering and.
Cassie as a Self-Aware SNePS/GLAIR Agent Stuart C. Shapiro Department of Computer Science and Engineering, and Center for Cognitive Science.
S.C. Shapiro Knowledge Representation and Reasoning Stuart C. Shapiro Professor, CSE Director, SNePS Research Group Member, Center for Cognitive.
COMP 4640 Intelligent & Interactive Systems Cheryl Seals, Ph.D. Computer Science & Software Engineering Auburn University.
S.C. Shapiro An Intelligent Interface to a GIS Stuart C. Shapiro Professor, CSE Director, SNePS Research Group Member, Center for Cognitive.
Semantics of a Propositional Network Stuart C. Shapiro Department of Computer Science & Engineering Center for MultiSource Information Fusion.
© 2002 Franz J. Kurfess Introduction 1 CPE/CSC 481: Knowledge-Based Systems Dr. Franz J. Kurfess Computer Science Department Cal Poly.
A Logic of Arbitrary and Indefinite Objects Stuart C. Shapiro Department of Computer Science and Engineering, and Center for Cognitive Science.
Knowledge Acquisition by an Intelligent Acting Agent Michael Kandefer and Stuart Shapiro Department of Computer Science and Engineering, Center for Cognitive.
S.C. Shapiro Knowledge Representation and Reasoning Stuart C. Shapiro Professor, CSE Director, SNePS Research Group Member, Center for Cognitive.
S.C. Shapiro Symbol Anchoring in a Grounded Layered Architecture with Integrated Reasoning Stuart C. Shapiro Department of Computer Science.
Abstraction and ACT-R. Outline Motivations for the Theory –Architecture –Abstraction Production Rules ACT-R Architecture of Cognition.
SNePS 3 for Ontologies Stuart C. Shapiro Department of Computer Science and Engineering, and Center for Cognitive Science University at Buffalo,
S.C. Shapiro The SNePS Approach to Cognitive Robotics Stuart C. Shapiro Department of Computer Science and Engineering and Center for Cognitive.
Research in Knowledge Representation, Reasoning, and Acting Stuart C. Shapiro Professor, CSE Director, Center for Cognitive Science Director,
AI: Trends and Directions Stuart C. Shapiro Professor, CSE Affiliated Professor, Linguistics, Philosophy Director, SNePS Research Group ACM.
A Logic of Arbitrary and Indefinite Objects Stuart C. Shapiro Department of Computer Science and Engineering, and Center for Cognitive Science.
S.C. Shapiro An Introduction to SNePS 3 Stuart C. Shapiro Department of Computer Science and Engineering and Center for Cognitive Science State.
1 Introduction to Modeling Languages Striving for Engineering Precision in Information Systems Jim Carpenter Bureau of Labor Statistics, and President,
A Cognitive Substrate for Natural Language Understanding Nick Cassimatis Arthi Murugesan Magdalena Bugajska.
Inference Graphs: A Roadmap Daniel R. Schlegel and Stuart C. Department of Computer Science and Engineering L A – Logic of Arbitrary.
Towards Cognitive Robotics Biointelligence Laboratory School of Computer Science and Engineering Seoul National University Christian.
Dr. Matthew Iklé Department of Mathematics and Computer Science Adams State College Probabilistic Quantifier Logic for General Intelligence: An Indefinite.
Learning Science and Mathematics Concepts, Models, Representations and Talk Colleen Megowan.
Panel Discussion I: Brainstorm on Language, Embodiment and the Critical minass of Intelligence Moderator: Alexei Samsonovich Panelists: Kenneth De Jong,
Moving NGSS to KCAS and Beyond Terry Rhodes
NATURAL LANGUAGE UNDERSTANDING FOR SOFT INFORMATION FUSION Stuart C. Shapiro and Daniel R. Schlegel Department of Computer Science and Engineering Center.
Artificial Intelligence: Introduction Department of Computer Science & Engineering Indian Institute of Technology Kharagpur.
DenK and iCat Two Projects on Cooperative Electronic Assistants (CEA’s) Robbert-Jan Beun, Rogier van Eijk & Huub Prüst Department of Information and Computing.
Intelligent Robot Architecture (1-3)  Background of research  Research objectives  By recognizing and analyzing user’s utterances and actions, an intelligent.
The SNePS Research Group Semantic Network Processing System The long-term goal of The SNePS Research Group is the design and construction of a natural-language-using.
Knowledge Representation and Reasoning in SNePS for Bioinformatics Stuart C. Shapiro Department of Computer Science and Engineering, and Center.
Introduction to Artificial Intelligence CS 438 Spring 2008 Today –AIMA, Chapter 1 –Defining AI Next Tuesday –Intelligent Agents –AIMA, Chapter 2 –HW: Problem.
Chapter 9. PlayMate System (1/2) in Cognitive Systems, Henrik Iskov Chritensen et al. Course: Robots Learning from Humans Kwak, Hanock Biointelligence.
Chapter 10. The Explorer System in Cognitive Systems, Christensen et al. 2 nd Part Course: Robots Learning from Humans Park, Seong-Beom Behavioral neurophysiology.
Chapter 10. The Explorer System in Cognitive Systems, Christensen et al. Course: Robots Learning from Humans On, Kyoung-Woon Biointelligence Laboratory.
CPSC 322, Lecture 19Slide 1 (finish Planning) Propositional Logic Intro, Syntax Computer Science cpsc322, Lecture 19 (Textbook Chpt – 5.2) Oct,
Vector and symbolic processors
Bridges To Computing General Information: This document was created for use in the "Bridges to Computing" project of Brooklyn College. You are invited.
An argument-based framework to model an agent's beliefs in a dynamic environment Marcela Capobianco Carlos I. Chesñevar Guillermo R. Simari Dept. of Computer.
COMP 4640 Intelligent & Interactive Systems Cheryl Seals, Ph.D. Computer Science & Software Engineering Auburn University.
Artificial Intelligence Logical Agents Chapter 7.
Chapter 9. The PlayMate System ( 2/2 ) in Cognitive Systems Monographs. Rüdiger Dillmann et al. Course: Robots Learning from Humans Summarized by Nan Changjun.
Service-Oriented Computing: Semantics, Processes, Agents
Cognitive Language Processing for Rosie
CS 4700: Foundations of Artificial Intelligence
Artificial Intelligence and Lisp Lecture 13 Additional Topics in Artificial Intelligence LiU Course TDDC65 Autumn Semester,
Building an mglair Agent a tutorial
Service-Oriented Computing: Semantics, Processes, Agents
MGLAIR Modal Grounded Layered Architecture with Integrated Reasoning
Teaching Java with the assistance of harvester and pedagogical agents
MGLAIR Modal Grounded Layered Architecture with Integrated Reasoning
Service-Oriented Computing: Semantics, Processes, Agents
Presentation transcript:

S.C. Shapiro Symbol-Anchoring in Cassie Stuart C. Shapiro and Haythem O. Ismail Department of Computer Science and Engineering and Center for Cognitive Science University at Buffalo {shapiro | |

S.C. Shapiro Outline Introduction Perceivable entities and properties Attentional Structures Actions Time Language Examples Summary

S.C. Shapiro Embodied Cassie A computational cognitive agent –Embodied in hardware –or Software-Simulated –Based on SNePS and GLAIR.

S.C. Shapiro Cassie, the FEVAHR

S.C. Shapiro FEVAHR/Cassie in the Lab

S.C. Shapiro SNePS Knowledge Representation and Reasoning –Intensional Representation –Propositions as Terms SNIP: SNePS Inference Package –Specialized connectives and quantifiers SNeBR: SNePS Belief Revision SNeRE: SNePS Rational Engine Interface Languages –SNePSUL: Lisp-Like –SNePSLOG: Logic-Like –GATN for Fragments of English.

S.C. Shapiro Interaction with Cassie English (Statement, Question, Command) (Current) Set of Beliefs [SNePS] (Updated) Set of Beliefs [SNePS] Actions [SNeRE] (New Belief) [SNePS] English sentence expressing new belief answering question reporting actions Answer [SNIP] GATN Parser GATN Generator Reasoning Clarification Dialogue Looking in World Reasoning

S.C. Shapiro Entities, Terms, Symbols, Objects Cassie’s mental entity: a person named Stu SNePS term: B4 Object in world:

S.C. Shapiro GLAIR Architecture Knowledge Level Perceptuo-Motor Level Sensory-Actuator Level NL Vision Sonar MotionProprioception Grounded Layered Architecture with Integrated Reasoning SNePS

S.C. Shapiro Alignment KL Body (PML/SAL) World SNePS term PML structure Object/PhenomenonAction

S.C. Shapiro Outline Introduction Perceivable entities and properties Attentional Structures Actions Time Language Examples Summary

S.C. Shapiro World Objects to Feature Vectors <Height, Width, Texture,.. > WorldPML/SAL

S.C. Shapiro Feature Vectors to KL Terms <Height, Width, Texture,.. > PML/SALKL ProperName(B4, Stu) Alignment

S.C. Shapiro Incomplete Feature Vectors <Height, nil,.. > PML/SALKL Height(B4, B12)

S.C. Shapiro Unifying PML-Descriptions PML/SAL KL B20 B30 B31 B6 Isa Prop

S.C. Shapiro Outline Introduction Perceivable entities and properties Attentional Structures Actions Time Language Examples Summary

S.C. Shapiro Deictic & Modality Registers I You Now Vision. PML KL Terms denoting Cassie Addressee Current time Current state(s) of looking at x.

S.C. Shapiro Outline Introduction Perceivable entities and properties Attentional Structures Actions Time Language Examples Summary

S.C. Shapiro Primitive Actions Aligned with PML/SAL Functions PMLKL Find(B4) SAL Modality Registers World

S.C. Shapiro Outline Introduction Perceivable entities and properties Attentional Structures Actions Time Language Examples Summary

S.C. Shapiro Aligning NOW using MTF NOW Semi-lattice of times, all of which contain *NOW, any of which could be meant by “now” Finite---only conceptualized times of conceptualized states Maximal Temporal Frame based on *NOW KL PML

S.C. Shapiro Moving NOW with MTF NOW KL PML Moves when Cassie acts, newly observes a state, or is informed of a new state. Always includes times of states in modality registers.

S.C. Shapiro Providing a Feel for Time NOW COUNTn hom 0 KL PML t1 t2 q ! beforeafter time duration !

S.C. Shapiro Outline Introduction Perceivable entities and properties Attentional Structures Actions Time Language Examples Summary

S.C. Shapiro Aligning Lexemes/NL “Stu” PML/SALKL ProperName(B4, Stu) Isa(B4, person) “person” ctgy npr ctgy n num sing Grammar NL World

S.C. Shapiro Outline Introduction Perceivable entities and properties Attentional Structures Actions Time Language Examples Summary

S.C. Shapiro Acting 1

S.C. Shapiro Acting 2 I found a red robot. I am looking at a red robot. Follow a red robot.

S.C. Shapiro Acting 3 I went to a red robot. I am near a red robot. I am following a red robot. I found a red robot. I am looking at a red robot. Follow a red robot.

S.C. Shapiro Use of Indexicals 1 Come here. I found you. I am looking at you.

S.C. Shapiro Come here. I came to you, Stu. I am near you. Use of Indexicals 2 I found you. I am looking at you.

S.C. Shapiro Who am I? Your name is ‘Stu’ and you are a person. Who have you talked to? I am talking to you. Talk to Bill. I am talking to you, Bill. Come here. Use of Indexicals 3

S.C. Shapiro Come here. I found you. I am looking at you. Use of Indexicals 4

S.C. Shapiro Come here. I came to you. I am near you. I found you. I am looking at you. Use of Indexicals 5

S.C. Shapiro Who am I? I talked to Stu and I am talking to you. Your name is ‘Bill’ and you are a person. Who are you? I am the FEVAHR and my name is ‘Cassie’. Who have you talked to? Use of Indexicals 6

S.C. Shapiro Outline Introduction Perceivable entities and properties Attentional Structures Actions Time Language Examples Summary

S.C. Shapiro Summary KL terms denote mental entities. KL terms aligned to PML structures. PML variables/registers contain KL terms. PML variables/registers/structures grounded in world via sensors & effectors. PML attentional structures anchor symbols by changing only when motivated.

S.C. Shapiro For More Information Personnel Manual Tutorial Bibliography ftp’able SNePS source code etc.