S.C. Shapiro Endowing Agents with a Personal Sense of Time Haythem O. Ismail & Stuart C. Shapiro Department of Computer Science and Engineering.

Slides:



Advertisements
Similar presentations
Pat Langley School of Computing and Informatics Arizona State University Tempe, Arizona USA Modeling Social Cognition in a Unified Cognitive Architecture.
Advertisements

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.
A high-school teachers perspective of first-year engineering By Rod Paton.
Assessment Photo Album
An F-Measure for Context-Based Information Retrieval Michael Kandefer and Stuart C. Shapiro University at Buffalo Department of Computer Science and Engineering.
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.
Revision Revolution This booklet suggests ways to help your brain remember things…….. It still needs your help to make it happen before exams!
Research in Knowledge Representation and Reasoning Stuart C. Shapiro Department of Computer Science & Engineering Center for MultiSource Information.
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 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.
12/2/98 Prof. Richard Fikes Representing Time Computer Science Department Stanford University CS222 Fall 1998.
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.
S.C. Shapiro Symbol-Anchoring in Cassie Stuart C. Shapiro and Haythem O. Ismail Department of Computer Science and Engineering and Center for.
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.
SNePS 3 for Ontologies Stuart C. Shapiro Department of Computer Science and Engineering, and Center for Cognitive Science University at Buffalo,
Summer 2011 Wednesday, 8/3. Biological Approaches to Understanding the Mind Connectionism is not the only approach to understanding the mind that draws.
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,
Improving Recovery for Belief Bases Frances L. Johnson & Stuart C. Shapiro Department of Computer Science and Engineering, Center for Multisource.
How Bodies Matter to Minds Michael L. Anderson University of Maryland
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.
CSCI 200 Introduction To Programming with Visual Basic Bob Bradley.
BERKELEY’S CASE FOR IDEALISM (Part 2 of 2)
Teacher: Mr. Silver I AM CANADIAN Website:
Artificial Intelligence Dr. Paul Wagner Department of Computer Science University of Wisconsin – Eau Claire.
Introduction to AI Robotics Chapter 2. The Hierarchical Paradigm Hyeokjae Kwon.
Human-Computer Interaction Introduction © Brian Whitworth.
Panel Discussion I: Brainstorm on Language, Embodiment and the Critical minass of Intelligence Moderator: Alexei Samsonovich Panelists: Kenneth De Jong,
1 Artificial Intelligence Introduction. 2 What is AI? Various definitions: Building intelligent entities. Getting computers to do tasks which require.
UNIVERSITY OF SOUTH CAROLINA Department of Computer Science and Engineering Programs with Common Sense Mingzhe Du and Hongying Du April, 2011 This paper.
listening David: What’s it like living in England, Terry? Terry: well, I’m having a great time. But I sometimes have difficulty understanding what people.
Artificial Intelligence: Introduction Department of Computer Science & Engineering Indian Institute of Technology Kharagpur.
Social Game Code Plan Overview Josh McCoy. Goals for the Code Plan Use of social games. –Representation –Contextually correct application –Goal oriented.
New Bulgarian University MindRACES, First Review Meeting, Lund, 11/01/2006 Anticipation by Analogy An Attempt to Integrate Analogical Reasoning with Perception,
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.
Cognitive ability: Challenge: How to recognize objects in a scene; where are the object’s boundaries? This problem is known as ‘image segmentation’ in.
Healthy Fear The Life God Blesses Part 2. Ah, Ah Choo!
MAX This is MAX. He is a brain in a vat. (and this is a new take on an old thought experiment) Unlike other envatted brains however, the Physical Reality.
Lecture 3 Cognizing Space 1: Nonconceptual content and the impression of space 1Introduction: Where we stand 2The problem of the nonconceptual experience.
Chapter Ten How Does the Acquisition of Skill Affect Performance?
Philosophy 1050: Introduction to Philosophy Week 8: Augustine and Self-Consciousness.
Bridges To Computing General Information: This document was created for use in the "Bridges to Computing" project of Brooklyn College. You are invited.
Blindsight, Zombies & Consciousness Jim Fahey Department of Cognitive Science Rensselaer Polytechnic Institute 10/4/2007.
© Janice Regan, CMPT 128, Jan CMPT 128: Introduction to Computing Science for Engineering Students, continue; and break; statements.
Feel the beat: using cross-modal rhythm to integrate perception of objects, others, and self Paul Fitzpatrick and Artur M. Arsenio CSAIL, MIT.
Complex Sentences.
Lecture #1 Introduction
2 The Matrix (2) What is Reality?.
MGLAIR Modal Grounded Layered Architecture with Integrated Reasoning
What is Psychology?.
Symbolic cognitive architectures
TA : Mubarakah Otbi, Duaa al Ofi , Huda al Hakami
MGLAIR Modal Grounded Layered Architecture with Integrated Reasoning
2 The Matrix What is Reality (2).
2 The Matrix (2) What is Reality?.
How Bodies Matter to Minds
Presentation transcript:

S.C. Shapiro Endowing Agents with a Personal Sense of Time Haythem O. Ismail & Stuart C. Shapiro Department of Computer Science and Engineering and Center for Cognitive Science State University of New York at Buffalo {hismail |

S.C. Shapiro Outline Introduction Subjective Time Time and Bodily Knowledge Time and External States Summary

S.C. Shapiro Uses of Time by a Cognitive Agent Reason about time Talk about time Reason and act in time Decide to act in timely fashion Remember acts and when done Sense current states Be informed of current states Be informed of past/future states Understand & generate NL with correct tense & aspect.

S.C. Shapiro Cassie A Computational Cognitive Agent Based on SNePS –Logic-based –Network-based –Knowledge representation, reasoning, and acting.

S.C. Shapiro Embodied Cassie A Cognitive Robot –Hardware –or Software-Simulated Separate, but aligned –Body –Mind.

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

S.C. Shapiro Symbol Grounding: Alignment robotgreen ! lex classmod classhead member class find lex action object “Find the green robot.” B6

S.C. Shapiro Outline Introduction Subjective Time Time and Bodily Knowledge Time and External States Summary

S.C. Shapiro Deictic Center Variables whose values are SNePS terms Aspects of embodiedness *I : SNePS term representing Cassie *YOU: person Cassie is talking with *NOW: current time.

S.C. Shapiro Subjective Time NOW contains SNePS term representing current time. NOW moves when Cassie acts or perceives a change of state.

S.C. Shapiro B6 Representation of Time find lex action object B1 ! agent act state time NOW !! beforeafterbeforeafter ????????????? I

S.C. Shapiro Movement of Time v.1 t1 t2! beforeafter t3! beforeafter NOW

S.C. Shapiro The Pacemaker PML process periodically increments variable COUNT. *COUNT = some PML integer. Reset to 0 when NOW moves. Provides bodily “feel” of passing time.

S.C. Shapiro Quantizing Time Cannot conceptualize fine distinctions in time intervals. So quantize, e.g. into half orders of magnitude (Hobbs, 2000).

S.C. Shapiro Movement of Time with Pacemaker NOW COUNTn hom 0 KL PML t1 t2 q ! beforeafter time duration !

S.C. Shapiro Outline Introduction Subjective Time Time and Bodily Knowledge Time and External States Summary

S.C. Shapiro Modality Variables Similar to Deictic Center. E.g.: VISION, AUDITION, WHEELS, ARMS *VISION = Holds(Lookat(Cassie, Stu), t3) –if vision currently occupied by looking at Stu –t3 denotes the time during which Cassie will be looking at Stu –*NOW is during t3 Set at PML when bodily state starts/ceases. One state may occupy multiple modalities.

S.C. Shapiro Knowing What You’re Doing When NOW moves –For each modality variable v –s.t. *v = Holds(s, t) –Make *NOW a subinterval of t So the agent believes it is now doing everything it is, in fact, doing.

S.C. Shapiro When you stop When state s ceases –For each modality variable vi –s.t. *vi = Holds(s, ti) Set vi to nil –Move NOW –Believe each ti is before *NOW.

S.C. Shapiro When you start When state s starts –For each modality v that s occupies –set v to Holds(s, ti) –Move NOW.

S.C. Shapiro Outline Introduction Subjective Time Time and Bodily Knowledge Time and External States Summary

S.C. Shapiro The Problem of the Fleeting Now How can you reason about “now” if it never stands still?

S.C. Shapiro Motivating Joke 9:30:00 AM (Door-to-Door Salesman): May I interest you in a brush? 9:30:02 AM (Homeowner): Not now. 9:30:03 AM (Salesman): Now?

S.C. Shapiro Fleeting Now Example 1 9:15:00: If the walk light is on now, cross the street. = If the walk light is on at 9:15:00, cross the street. 9:15:01: Turn to look at walk light. 9:15:02: The walk light is on at 9:15:02. Should you cross the street? Yes, but why?

S.C. Shapiro Fleeting Now Example 2 12:15:00: “Is John having lunch now?” 12:15:02: Agent walks to John’s office. 12:17:00: Agent sees John at his desk, eating. 12:19:00: Agent reports “yes”. Appropriate granularity.

S.C. Shapiro Fleeting Now Example 3 12:15:00: “Is John having lunch now?” Agent knows John is at home without a phone. Agent contemplates driving to John’s home. Don’t bother---inappropriate granularity.

S.C. Shapiro The Vagueness of “now” I’m now giving a talk. I’m now teaching a course. I’m now visiting Houston. I’m now living in Buffalo. The agent is now walking to John’s office. The agent is now seeing if John is eating lunch. Multiple now’s at different granularities.

S.C. Shapiro NOW-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

S.C. Shapiro Moving NOW with MTF NOW

S.C. Shapiro Typical Durations “If the walk light is on now, cross the street.” Relevant duration is typical duration of walk lights. “Is John having lunch now?” Relevant duration is typical duration of lunch. Use quantized typical durations when updating NOW-MTFs.

S.C. Shapiro Using Appropriate Granularity NOW Lunch time Lunch? Lunch! Yes!

S.C. Shapiro Outline Introduction Subjective Time Time and Bodily Knowledge Time and External States Summary

S.C. Shapiro Summary Distinguish body & mind, but align them. Body (PML): –What people and things look like. –Primitive and routine actions. –Time intervals. –Pacemaker: Feel for elapsing time. –Deictic Center variables. –Modality variables. Mind: –Conceptualized people, things, actions, times, states.

S.C. Shapiro When Inquire about States Put them into MTF According to their typical duration.

S.C. Shapiro When NOW Moves Use Pacemaker to measure old NOW. Include current actions in MTF. Include other states according to their typical durations.

S.C. Shapiro For More Information