Knowledge Fusion Research WorkshopOctober 20 - 22 2004 1 A High-level Language for Military Fusion Problems Richard Scherl Computer Science Department.

Slides:



Advertisements
Similar presentations
1 Probability and the Web Ken Baclawski Northeastern University VIStology, Inc.
Advertisements

Slide 1 of 18 Uncertainty Representation and Reasoning with MEBN/PR-OWL Kathryn Blackmond Laskey Paulo C. G. da Costa The Volgenau School of Information.
Hard or Soft: Does it matter? Michael Lyons Manager, Strategic Analysis and Research.
ARTIFICIAL INTELLIGENCE [INTELLIGENT AGENTS PARADIGM] Professor Janis Grundspenkis Riga Technical University Faculty of Computer Science and Information.
Workpackage 2: Norms
The Logic of Intelligence Pei Wang Department of Computer and Information Sciences Temple University.
Artificial Intelligence Chapter 21 The Situation Calculus Biointelligence Lab School of Computer Sci. & Eng. Seoul National University.
GOLOG David Mui EEL6938. Outline Introduction Situational Calculus GOLOG Personal Banking Assistant Using GOLOG ConGOLOG – GOLOG variant Conclusion.
Abstract An intelligent agent operating in a complex world cannot base its decisions solely on the world's objective and preprogrammed rules. Rather, the.
Sheila McIlraith, Knowledge Systems Lab, Stanford University AAAI’00 08/2000 What Sensing Tells Us: Towards a Formal Theory of Testing for Dynamical Systems.
Arizona Counter Terrorism Information Center Arizona Office of Homeland Security Deputy Director: John Phelps.
ISBN Chapter 3 Describing Syntax and Semantics.
Introductory Lecture. What is Discrete Mathematics? Discrete mathematics is the part of mathematics devoted to the study of discrete (as opposed to continuous)
CS Formal Logic 1 GOLOG Maruthappan Shanmugasundaram (Satish) Graduate Student Department of Computer Science University of Manitoba Winnipeg, R3T.
CPSC 322, Lecture 19Slide 1 Propositional Logic Intro, Syntax Computer Science cpsc322, Lecture 19 (Textbook Chpt ) February, 23, 2009.
Adding Organizations and Roles as Primitives to the JADE Framework NORMAS’08 Normative Multi Agent Systems, Matteo Baldoni 1, Valerio Genovese 1, Roberto.
1 © Franz J. Kurfess Constrained Access Franz J. Kurfess Cal Poly SLO Computer Science Department.
An Architecture-Based Approach to Self-Adaptive Software Presenters Douglas Yu-cheng Su Ajit G. Sonawane.
Sensemaking and Ground Truth Ontology Development Chinua Umoja William M. Pottenger Jason Perry Christopher Janneck.
Knowledge Acquisitioning. Definition The transfer and transformation of potential problem solving expertise from some knowledge source to a program.
Artificial Intelligence and Lisp Lecture 13 Additional Topics in Artificial Intelligence LiU Course TDDC65 Autumn Semester, 2010
Copyright © 2006 The McGraw-Hill Companies, Inc. Programming Languages 2nd edition Tucker and Noonan Chapter 18 Program Correctness To treat programming.
Formal Aspects of Computer Science – Week 12 RECAP Lee McCluskey, room 2/07
Artificial Intelligence 2005/06 Situation Calculus GOLOG.
Describing Syntax and Semantics
Community Manager A Dynamic Collaboration Solution on Heterogeneous Environment Hyeonsook Kim  2006 CUS. All rights reserved.
Self-Learning Ontologies Presented to the 25 th Soar Workshop Ann Arbor, MI June 15-17, 2005 Tim Darr, Ph. D. University of Michigan AI Lab ‘96.
Katanosh Morovat.   This concept is a formal approach for identifying the rules that encapsulate the structure, constraint, and control of the operation.
School of Computing and Mathematics, University of Huddersfield Computing Science: WEEK 17 Announcement: next few weeks… 9 nd Feb: Comparative Programming.
Chapter 6: Objections to the Physical Symbol System Hypothesis.
Notes for Chapter 12 Logic Programming The AI War Basic Concepts of Logic Programming Prolog Review questions.
An approach to Intelligent Information Fusion in Sensor Saturated Urban Environments Charalampos Doulaverakis Centre for Research and Technology Hellas.
Kansas State University Department of Computing and Information Sciences CIS 730: Introduction to Artificial Intelligence Lecture 26 of 41 Friday, 22 October.
Ontologies Reasoning Components Agents Simulations Belief Update, Planning and the Fluent Calculus Jacques Robin.
Knowledge representation
Knowledge Fusion Research WorkshopNovember 29 - December 1, Knowledge Fusion Education Richard Scherl Computer Science Department Monmouth University.
Benjamin Gamble. What is Time?  Can mean many different things to a computer Dynamic Equation Variable System State 2.
School of Computer Science and Technology, Tianjin University
Argumentation and Trust: Issues and New Challenges Jamal Bentahar Concordia University (Montreal, Canada) University of Namur, Belgium, June 26, 2007.
Recognizing Activities of Daily Living from Sensor Data Henry Kautz Department of Computer Science University of Rochester.
Feb 24, 2003 Agent-based Proactive Teamwork John Yen University Professor of IST School of Information Sciences and Technology The Pennsylvania State University.
Discrete Structures for Computing
Learning Agents Center George Mason University Computer Science Department Partners Day Symposium May 4, 2004 Gheorghe Tecuci, Mihai Boicu, Dorin Marcu,
An Ontological Framework for Web Service Processes By Claus Pahl and Ronan Barrett.
1 CS 2710, ISSP 2610 Foundations of Artificial Intelligence introduction.
Kansas State University Department of Computing and Information Sciences CIS 730: Introduction to Artificial Intelligence Lecture 11 of 41 Wednesday, 15.
Computing & Information Sciences Kansas State University Lecture 13 of 42 CIS 530 / 730 Artificial Intelligence Lecture 13 of 42 William H. Hsu Department.
ARTIFICIAL INTELLIGENCE [INTELLIGENT AGENTS PARADIGM] Professor Janis Grundspenkis Riga Technical University Faculty of Computer Science and Information.
A Quantitative Trust Model for Negotiating Agents A Quantitative Trust Model for Negotiating Agents Jamal Bentahar, John Jules Ch. Meyer Concordia University.
KNOWLEDGE BASED SYSTEMS
Introduction to Artificial Intelligence CS 438 Spring 2008.
Lisp "List Processing". Lisp history John McCarthy developed the basics behind Lisp during the 1956 Dartmouth Summer Research Project on Artificial Intelligence.
Ontology Development As Undergraduate Research Antonio M. Lopez, Jr pp , CCSC’02 xx Feb 2015 SNU IDB Inyong Lee.
Feng Zhiyong Tianjin University Fall  An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that.
RULES Patty Nordstrom Hien Nguyen. "Cognitive Skills are Realized by Production Rules"
Computing & Information Sciences Kansas State University Wednesday, 13 Sep 2006CIS 490 / 730: Artificial Intelligence Lecture 10 of 42 Wednesday, 13 September.
CMPB454 ARTIFICIAL INTELLIGENCE (AI) CHAPTER 1.1 Background Information CHAPTER 1.1 Background Information Instructor: Alicia Tang Y. C.
ITEC 1010 Information and Organizations Chapter V Expert Systems.
Slide no 1 Cognitive Systems in FP6 scope and focus Colette Maloney DG Information Society.
CSC3315 (Spring 2009)1 CSC 3315 Languages & Compilers Hamid Harroud School of Science and Engineering, Akhawayn University
Intelligent Agents Chapter 2. How do you design an intelligent agent? Definition: An intelligent agent perceives its environment via sensors and acts.
Alborz Geramifard Logic Programming and MDPs for Planning Winter 2009.
Introductory Lecture. What is Discrete Mathematics? Discrete mathematics is the part of mathematics devoted to the study of discrete (as opposed to continuous)
Gheorghe Tecuci 1,2, Mihai Boicu 1, Dorin Marcu 1 1 Learning Agents Laboratory, George Mason University 2 Center for Strategic Leadership, US Army War.
Artificial Intelligence and Lisp Lecture 13 Additional Topics in Artificial Intelligence LiU Course TDDC65 Autumn Semester,
Intelligent Agents Chapter 2.
Logic for Artificial Intelligence
Representations & Reasoning Systems (RRS) (2.2)
The Vision Mobilizing the Web with DAML-Enabled Web Services
Presentation transcript:

Knowledge Fusion Research WorkshopOctober A High-level Language for Military Fusion Problems Richard Scherl Computer Science Department Monmouth University

Knowledge Fusion Research Workshop October Cognitive Robotics/GoLog Integrating reasoning, perception and action within a uniform theoretical and implementation framework. Logic-based. High-level language.

Knowledge Fusion Research Workshop October The Situation Calculus McCarthy and Hayes A predicate calculus formalization of states, actions, and effects. Reiter 1991,2001.

Knowledge Fusion Research Workshop October GOLOG GOLOG -- AlGol in logic. Sequences. Nondeterministic choice of actions. Nondeterministic choice of arguments. Conditions, While loops. Recursion.

Knowledge Fusion Research Workshop October Plans Vs Computer Programs There is a long tradition of viewing plans as computer programs. (Green, manna and Waldinger). There are many problems with this view of plans. An agent may not know whether a test is true. Agents may not know enough to execute the action.

Knowledge Fusion Research Workshop October Incomplete Knowledge Generally, agents do not have complete knowledge of the world. Formalism must distinguish between what is true in the world and what the agent knows.

Knowledge Fusion Research Workshop October Incomplete Knowledge (Cont) Agents must reason about: Actions that produce knowledge --- perception, reading, comunicative acts. The knowledge prerequisites of actions.

Knowledge Fusion Research Workshop October References Levesque, Reiter, Lesperance,Lin, Scherl. GOLOG. JLP, Reiter. Knowledge in action: logical foundations for specifying and implementing dynamical systems. MIT press, Scherl and Levesque. Knowledge, action, and the frame problem. AIJ, 2003.

Knowledge Fusion Research Workshop October Goal Can a similar sort of language be used for the specification of high-level and flexible plans useful in domains relevant to the military and also homeland security?

Knowledge Fusion Research Workshop October Example Scenarios Silent prairie: agricultural bio-terrorism exercise developed by the national strategic gaming center at the national defense university Battlespace challenge problem: road to war developed by signal solutions for ARL

Knowledge Fusion Research Workshop October Silent Prairie Foot and mouth disease suspected in north Carolina and Kansas Bio-terrorism considered a possibility

Knowledge Fusion Research Workshop October Steps FBI notified – but the 82 nd airborne recently returned to north Carolina from Iraq. Need to notify state officials and farm companies. All leaders of states to which N. Carolina cattle are shipped must be notified. Send sample to plum island facility for analysis.

Knowledge Fusion Research Workshop October Steps (Cont) Start initial containment strategy (quarantine zones). Notify governor and other state officials. Notify USDA, FDA. Enact regional containment strategy. Notify DHS and DOD. Check to see if there was “chatter” about FMD.

Knowledge Fusion Research Workshop October Sample Plan While ¬knows(  x State(x)  Considered(x)) (  X).state(x)?; If ¬Kwhether(Shipto(f,a,x)) then sense_ship(f,a,x) endIf; If knows(Shipto(f,a,x) then notify(f,a,x) endIf; assert(Considered(x)); endWhile;

Knowledge Fusion Research Workshop October Battlespace Challenge Problem Signal Solutions 2015 North Korea Kim Jung-Il dies Civil War

Knowledge Fusion Research Workshop October Forces Involved U.S. ROK. DPRK Hardliners. DPRK Reformers.

Knowledge Fusion Research Workshop October Determining Friend from Foe Sensors(ELINT, SIGINT) HUMINT COP needs to indicate which forces are friends and which are foes (DPRK Hardliners) based upon both intelligence information and inferences.

Knowledge Fusion Research Workshop October Rules in Knowledge-Base  x,y hardliner(x)  engages(x,y)  ¬ hardliner(y)  y,z  x supplies(x,y)  hardliner(y)  supplies(x,z)  hardliner(z)

Knowledge Fusion Research Workshop October Identify Threats to Inchon Airport Sensors and human intelligence are utilized to identify threats. Which U.S. Units can deal with the threats? How long would it take them to arrive at location of threat given current conditions?

Knowledge Fusion Research WorkshopOctober

Knowledge Fusion Research Workshop October Implementation GoLog interpreter written in Prolog Integration into Jade agent platform

Knowledge Fusion Research Workshop October Further Topics Concurrency (ConGolog, IndiGolog) Exogenous actions Probabilistic action occurrences and effects Ability Time Integrating semantic web ontology languages

Knowledge Fusion Research Workshop October Theory of Actions (Cont) Real time, resource bounded behavior Belief revision Execution monitoring and failure recovery Automated plan construction