SA-1 Practical Planning for Robots and Other Autonomous Agents Michael Brenner & Patrick Eyerich.

Slides:



Advertisements
Similar presentations
2/2010 Parent Contacts Early Intervention Parent Leadership Project.
Advertisements

JSIMS 28-Jan-99 1 JOINT SIMULATION SYSTEM Modeling Command and Control (C2) with Collaborative Planning Agents Randall Hill and Jonathan Gratch University.
Talking story Joanne Franny Author: Joanne Chen voice actor : Joanne and Franny.
EXPRESSIVE INTELLIGENCE STUDIO FROM ABSTRACTION TO REALITY: Integrating Drama Management into a Playable Game Experience Anne Sullivan, Sherol Chen and.
SRTA: The Soft-Real Time Agent Control Architecture Bryan Horling, Victor Lesser, Regis Vincent, Thomas Wagner presented by Anita Raja.
Participatory Simulation & Emergent Behavior Author : Uri Wilensky Presenter : Krunal Doshi.
CASTLES AND KNIGHTS PRINCES AND PRINCESSES A walk in the past.
PlanSIG, Dec, Temporal Plans and Resource Management Pieter Buzing & Cees Witteveen Delft University of Technology.
Improving Market-Based Task Allocation with Optimal Seed Schedules IAS-11, Ottawa. September 1, 2010 G. Ayorkor Korsah 1 Balajee Kannan 1, Imran Fanaswala.
Group 7. Goals and Objectives to teach the children about genes and how different combinations produce different offspring. To help children easily recognize.
Analyzing the tradeoffs between breakup and cloning in the context of organizational self-design By Sachin Kamboj.
Cognitive Science 1 Kartik Talamadupula Subbarao Kambhampati J. Benton Dept. of Computer Science Arizona State University Paul Schermerhorn Matthias Scheutz.
Agent Mediated Grid Services in e-Learning Chun Yan, Miao School of Computer Engineering Nanyang Technological University (NTU) Singapore April,
Provisional draft 1 ICT Work Programme Challenge 2 Cognition, Interaction, Robotics NCP meeting 19 October 2006, Brussels Colette Maloney, PhD.
Intelligent Agents revisited.
Multirobot Coordination in USAR Katia Sycara The Robotics Institute
BNAIC, Oct, Temporal Plans and Resource Management Pieter Buzing & Cees Witteveen TU Delft.
Applications of agent technology in communications: a review S. S. Manvi &P. Venkataram Presented by Du-Shiau Tsai Computer Communications, Volume 27,
Jordan-Webb - All Rights Reserved Technology Supporting Distributed and Virtual Teams Overview Paul Collins Jordan-Webb (773)
Incorporating the Culture of Virtual Reality Games into Educational Software via an Authoring Tool Maria Virvou, Constantinos Manos, George Katsionis,
Biointelligence Laboratory School of Computer Science and Engineering Seoul National University Cognitive Robots © 2014, SNU CSE Biointelligence Lab.,
Probe. Listen. Empathize. Articulate. Solve. End ™ The PLEASE ! Workshops Debt collection through call centre Pre-training assessment Training Post-training.
January 13, 2012 Oscar Lin Steve Leung School of Computing and Information Systems Faculty of Science and Technology Athabasca University, Canada.
Students Speak - Are We Listening? 2012 CCCSE Workshop at NISOD.
Intelligent Agents. Software agents O Monday: O Overview video (Introduction to software agents) O Agents and environments O Rationality O Wednesday:
Oral Examination Review PHM 421. Exam Overview Format Logistics of day Content.
Autonomous Multiagent Systems Instructor: Peter Stone.
2010 CCCSE Workshop Students Speak – We Listen June 1, 2010.
Intelligent Agents & Agent-oriented systems James Harland School of Computer Science and IT Intelligent Agents & Agent-oriented systems.
Intelligent Mobile Robotics Czech Technical University in Prague Libor Přeučil
L 9 : Collaborations Why? Terminology Coherence Coordination Reference s :
Mobile Computing: The Why’s and the How’s Patrick M. O’Shea, Ph.D.
Semantic Interoperability Berlin, 25 March 2008 Semantically Enhanced Resource Allocator Marc de Palol Jorge Ejarque, Iñigo Goiri, Ferran Julià, Jordi.
Boğaziçi University Planning and Coordination in A Multi-Agent Environment. Gökay Burak AKKUŞ cmpe530.
Distance Learning and Education Center for Advanced Research in Technology for Education Lewis Johnson, Ph.D., Director Erin Shaw, presenter Research Scientist,
ARTIFICIAL INTELLIGENCE [INTELLIGENT AGENTS PARADIGM] Professor Janis Grundspenkis Riga Technical University Faculty of Computer Science and Information.
A Language for Task-Level Executives Reid Simmons David Apfelbaum Carnegie Mellon University.
Marice the Mermaid By Rita Kelly Once upon a time there was a mermaid named Marice. She wanted to be a fancy princess living in a big castle. But she.
1. Warm-up, BOGGLE! 2. Test review 3. Writing 4. Figure it Out 5. HW Check 6. I wish (present) 7. Presentations (Mami, Yuka, Noriko) 8. HW/Exit Ticket.
Intelligent Agents RMIT Prof. Lin Padgham (leader) Ass. Prof. Michael Winikoff Ass. Prof James Harland Dr Lawrence Cavedon Dr Sebastian Sardina.
Making an Impact in a Diplomas Now Mathematics Classroom.
 Motivated by desire for natural human-robot interaction  Encapsulates what the robot knows about the human  Identity  Location  Intentions Human.
Collaboration in eRegion- ICT for Growth and Empowerment Bror Salmelin Head of Unit, New working environments European Commission, DG Information Society.
Riga Technical University Department of System Theory and Design Usage of Multi-Agent Paradigm in Multi-Robot Systems Integration Assistant professor Egons.
Planning Your Advanced Lecture 1 Brian C. Williams J/6.834J Sept 26 th, 2001.
Partnership for International Research and Education A Global Living Laboratory for Cyberinfrastructure Application Enablement II. International Experience.
120 I am an even number. 120 I am an even number. I am more than 100.
Algorithmic, Game-theoretic and Logical Foundations
Introduction to Artificial Intelligence CS 438 Spring 2008 Today –AIMA, Ch. 25 –Robotics Thursday –Robotics continued Home Work due next Tuesday –Ch. 13:
The Princess and the Pea Next Slide - Click on the Peas.
The Princess and the Pea Next Slide - Click on the Peas.
Students Speak – We Listen CCCSE Workshop, May 31, 2011.
Software Agents & Agent-Based Systems Sverker Janson Intelligent Systems Laboratory Swedish Institute of Computer Science
CARRYING BORROWING CARRYING AND BORROWING ON THE PYTHABACUS.
Collaborative Grasp Planning with Multiple Object Representations Peter Brook Matei Ciocarlie Kaijen Hsiao.
Autonomous Intelligent Systems at King’s College London Derek Long
Princess and the pea The story book By Emily Daymond-King Hot I tell you!
The Princess That Was Not There Written By Jarrett Holmes.
Constraint-Based Motion Planning for Multiple Agents Luv Kohli COMP259 March 5, 2003.
 starter activity Royal residence Fortress Religious centre Military garrison Aid to navigation Prison Other Your teacher will give you a card. Move to.
Pathways for Scaling Up Capacity building at all levels Focus on the most vulnerable How do we support capacity building at scale— what are appropriate.
How to Involve Families in the Child Outcome Summary (COS) Process Debi Donelan, MSSA Early Support for Infants and Toddlers Katrina Martin, Ph.D. SRI.
December 3, 2014AISC-CODISCO 2014, revised Nov From Agent-based models to network analysis (and return): the policy-making perspective Magda Fontana.
Functionality of objects through observation and Interaction Ruzena Bajcsy based on Luca Bogoni’s Ph.D thesis April 2016.
More with Ch. 2 Ch. 3 Problem Solving Agents
رؤية مستقبلية لتطوير كلية الزراعة جامعة الفيوم
Simplify My Meds Patient’s prescriptions…simplified!
Subsuption Architecture
Presenter : Seokjun Lee Kyonggi University
Presentation transcript:

SA-1 Practical Planning for Robots and Other Autonomous Agents Michael Brenner & Patrick Eyerich

CoSy review, September 2008, BirminghamMichael Brenner, ALU Freiburg Planning for Autonomous Agents Autonomous agents do not only make plans, but also... execute and monitor them in dynamic environments with limited knowledge concurrently with others in interaction with others

CoSy review, September 2008, BirminghamMichael Brenner, ALU Freiburg Robots The prototypical autonomous agents! real-time planning and acting limited sensing human-robot interaction We study planning for high-level robot control in several projects Cosy, Desire, CogX

CoSy review, September 2008, BirminghamMichael Brenner, ALU Freiburg Dora the Explorer

CoSy review, September 2008, BirminghamMichael Brenner, ALU Freiburg What we're working on Active Continual Planning: Intelligently interleaving planning, execution, and monitoring Multiagent Planning: Reasoning about others Dialogue Planning: Planning how to interact with others (in particular: humans) Internal process planning: coordination of subarchitectures, demand-driven generation of planning states, planning for internal information gathering Extended expressivity: action durations, cost measures, probabilistic outcomes,...

CoSy review, September 2008, BirminghamMichael Brenner, ALU Freiburg Approach Research framework: Base planners: FF, TFD Continual Collaborative Planning MAPSIM Evaluation: Simulation Simulation + human participation Real Human-Robot Interaction

CoSy review, September 2008, BirminghamMichael Brenner, ALU Freiburg A MAPSIM Run MAPSIM run starts. There are 2 agents: Lilli and R2D2. (1) Lilli: ”Please give me the cookie, R2D2.” (2) R2D2: ”Okay, Lilli.” (3) R2D2: ”Where is the cookie, Lilli?” (4) Lilli: ”The cookie is in the kitchen, R2D2.” (5) R2D2: ”Thanks.” (6) R2D2: ”Please open the kitchen door, Lilli.” (7) Lilli opens the kitchen door. (8) R2D2: ”Thanks.” (9) R2D2 moves to the kitchen. (10) R2D2 grasps the cookie. (11) R2D2 moves to the living room. (12) R2D2 gives Lilli the cookie. (13) Lilli: ”Thanks for giving me the cookie, R2D2.” MAPSIM terminates successfully.

CoSy review, September 2008, BirminghamMichael Brenner, ALU Freiburg And a fun side project... This is a story about Prince Valiant, King Arthur and a dragon. King Arthur traveled to the castle. Prince Valiant saw King Arthur. King Arthur said: "Please bring me the treasure, Prince Valiant!". Prince Valiant said: "As you wish, King Arthur." Prince Valiant asked: "Where is the treasure, King Arthur?" King Arthur said: "The treasure is in the dragon's lair." Prince Valiant said: "Thank you." Prince Valiant traveled to the dragon's lair. Prince Valiant saw the dragon. The dragon tried to kill Prince Valiant, but failed. Prince Valiant killed the dragon. Prince Valiant took the treasure. Prince Valiant traveled to the castle. Prince Valiant gave the treasure to King Arthur. King Arthur said: "Thank you for bringing me the treasure, Prince Valiant."

CoSy review, September 2008, BirminghamMichael Brenner, ALU Freiburg Interested? Many projects possible: practical extensions to the TFD planner interaction planning active plan monitoring story generation... Contact us: