7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm1 Part 1: Overview & Managerial.

Slides:



Advertisements
Similar presentations
Layered Architecture Group 1 - Wesley Flowers, Brian Kennedy, Corey Masters, Everett Thayer, Andre Vicente.
Advertisements

ARCHITECTURES FOR ARTIFICIAL INTELLIGENCE SYSTEMS
Lecture 8: Three-Level Architectures CS 344R: Robotics Benjamin Kuipers.
5-1 Chapter 5: REACTIVE AND HYBRID ARCHITECTURES.
Embedded System Lab Kim Jong Hwi Chonbuk National University Introduction to Intelligent Robots.
AuRA: Principles and Practice in Review
7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm1 Part 1: Overview & Managerial.
Lecture 6: Hybrid Robot Control Gal A. Kaminka Introduction to Robots and Multi-Robot Systems Agents in Physical and Virtual Environments.
Experiences with an Architecture for Intelligent Reactive Agents By R. Peter Bonasso, R. James Firby, Erann Gat, David Kortenkamp, David P Miller, Marc.
Cognitive Colonization Tony Stentz, Martial Hebert, Bruce Digney, Scott Thayer Robotics Institute Carnegie Mellon University.
Autonomous Mobile Robots CPE 470/670 Lecture 8 Instructor: Monica Nicolescu.
Motor Schema Based Navigation for a Mobile Robot: An Approach to Programming by Behavior Ronald C. Arkin Reviewed By: Chris Miles.
Behavior- Based Approaches Behavior- Based Approaches.
Distributed Robot Agent Brent Dingle Marco A. Morales.
AuRA: Autonomous Robot Architecture From: Integrating Behavioral, Perceptual, and World Knowledge in Reactive Navigation Ron Arkin, 1990.
Intelligent Agents: an Overview. 2 Definitions Rational behavior: to achieve a goal minimizing the cost and maximizing the satisfaction. Rational agent:
Inventing Hybrid Control The basic idea is simple: we want the best of both worlds (if possible). The goal is to combine closed-loop and open-loop execution.
The Need of Unmanned Systems
What is it? A mobile robotics system controls a manned or partially manned vehicle-car, submarine, space vehicle | Website for Students.
Robotica Lezione 1. Robotica - Lecture 12 Objectives - I General aspects of robotics –Situated Agents –Autonomous Vehicles –Dynamical Agents Implementing.
Mobile Robot Control Architectures “A Robust Layered Control System for a Mobile Robot” -- Brooks 1986 “On Three-Layer Architectures” -- Gat 1998? Presented.
Introduction to Behavior- Based Robotics Based on the book Behavior- Based Robotics by Ronald C. Arkin.
Robotica Lecture 3. 2 Robot Control Robot control is the mean by which the sensing and action of a robot are coordinated The infinitely many possible.
Introduction to AI Robotics Chapter 2. The Hierarchical Paradigm Hyeokjae Kwon.
4 Introduction to AI Robotics (MIT Press)Chapter 4: The Reactive Paradigm1 The Reactive Paradigm Describe the Reactive Paradigm in terms of the 3 robot.
COMP 4640 Intelligent & Interactive Systems Cheryl Seals, Ph.D. Computer Science & Software Engineering Auburn University Lecture 2: Intelligent Agents.
7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm1 Part 1: Overview & Managerial.
9 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning1 Part II Chapter 9: Topological Path Planning.
Artificial Intelligence Chapter 25 Agent Architectures Biointelligence Lab School of Computer Sci. & Eng. Seoul National University.
Outline: Biological Metaphor Biological generalization How AI applied this Ramifications for HRI How the resulting AI architecture relates to automation.
Robotica Lecture 3. 2 Robot Control Robot control is the mean by which the sensing and action of a robot are coordinated The infinitely many possible.
The Hybrid Deliberative/Reactive Paradigm The City College of New York Department of Electrical Engineering Group Member: Jik Cheung Yongwen Zhu Yayi Hu.
University of Amsterdam Search, Navigate, and Actuate - Qualitative Navigation Arnoud Visser 1 Search, Navigate, and Actuate Qualitative Navigation.
Architecture for Autonomous Assembly 1 Reid Simmons Robotics Institute Carnegie Mellon University.
10 Chapter 10: Metric Path Planning a. Representations b. Algorithms.
Intelligent Robotics An Introduction The King’s Academy November 2, 2007.
University of Windsor School of Computer Science Topics in Artificial Intelligence Fall 2008 Sept 11, 2008.
9 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning1 Part II Chapter 9: Topological Path Planning.
Structured Control for Autonomous Robots Reid G. Simmons Carnegie Mellon University Uday Rajanna.
Intelligent Robotics Today: Robot Control Architectures Next Week: Localization Reading: Murphy Sections 2.1, 2.3, 2.5, 3.1, 3.5, 3.6, 4.1 – 4.3, 4.5,
Georgia Tech / Mobile Intelligence 1 Multi-Level Learning in Hybrid Deliberative/Reactive Mobile Robot Architectural Software Systems DARPA MARS Kickoff.
Topological Path Planning JBNU, Division of Computer Science and Engineering Parallel Computing Lab Jonghwi Kim Introduction to AI Robots Chapter 9.
Introduction to Artificial Intelligence CS 438 Spring 2008 Today –AIMA, Ch. 25 –Robotics Thursday –Robotics continued Home Work due next Tuesday –Ch. 13:
4 Introduction to AI Robotics (MIT Press)Chapter 4: The Reactive Paradigm1 The Reactive Paradigm Describe the Reactive Paradigm in terms of the 3 robot.
Mike Graves Summer 2005 University of Texas at Dallas Implicit Invocation: The Task Control Architecture Mike Graves CS6362 Term Paper Dr. Lawrence Chung.
Autonomy for General Assembly Reid Simmons Research Professor Robotics Institute Carnegie Mellon University.
Chapter Twelve Robotics: The Ultimate Intelligent Agents.
Behavior-based Multirobot Architectures. Why Behavior Based Control for Multi-Robot Teams? Multi-Robot control naturally grew out of single robot control.
Overivew Occupancy Grids -Sonar Models -Bayesian Updating
9 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 9: Topological Path Planning1 Part II Chapter 9: Topological Path Planning.
4 Introduction to AI Robotics (MIT Press)Chapter 4: The Reactive Paradigm1 The Reactive Paradigm Describe the Reactive Paradigm in terms of the 3 robot.
Trends in Robotics Research Classical AI Robotics (mid-70’s) Sense-Plan-Act Complex world model and reasoning Reactive Paradigm (mid-80’s) No models: “the.
Functionality of objects through observation and Interaction Ruzena Bajcsy based on Luca Bogoni’s Ph.D thesis April 2016.
Robotics From the book :
ISTC-CNR contribution to D2.2
Learning Fast and Slow John E. Laird
Intelligent Mobile Robotics
Chapter 8: Multi-agents
Artificial Intelligence Chapter 25 Agent Architectures
Today: Classic & AI Control Wednesday: Image Processing/Vision
Part II Chapter 9: Topological Path Planning
Trends in Robotics Research
Overivew Occupancy Grids -Sonar Models -Bayesian Updating
Chapter 7: Hybrid Deliberative/Reactive Paradigm
Subsuption Architecture
Robot Intelligence Kevin Warwick.
Artificial Intelligence Chapter 25. Agent Architectures
Artificial Intelligence Chapter 25 Agent Architectures
Behavior Based Systems
Presentation transcript:

7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm1 Part 1: Overview & Managerial Architectures Part 2: State Hierarchy Architectures

7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm2 Objectives Describe the hybrid paradigm in terms of 1) SPA and 2) sensing organization Given a list of responsibilities, be able to say whether it belongs in the deliberative layer or in the reactive layer List the five basic components of a Hybrid architecture: sequencer agent, resource manager, cartographer, mission planner, performance monitoring and problem solving agent. Be able to describe the difference between managerial, state hierarchy, and model-oriented styles of Hybrid architectures. Be able to describe the use of state to define behaviors and deliberative responsibilities in state hierarchy styles of Hybrid architectures

7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm3 Motivating Example for Deliberation: USAR Worker places robot at entrance to unstable building, loads in the floor plan, contextual knowledge and tells robot to look for survivors efficiently and map out safe routes for workers to pass through contextual knowledge includes probability of where people are more likely to be What can a reactive architecture do? What can’t it do? Path planning, handling detours due to blockage, map making, learn from past rescues

7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm4 Organization: Plan, Sense-Act

7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm5 Sensing Organization Deliberative functions *Can “eavesdrop” *Can have their own Sensors *Have output which Looks like a sensor Output to a behavior (virtual sensor)

7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm6 Deliberation v. Reaction as a function of TIME Past, Present, Future Reactive –exists in the PRESENT (will a bit of duration) Deliberative –can reason about the PAST –can project into the FUTURE

7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm7 Architectures:Key Questions How does the architecture distinguish between reaction and deliberation? How does it organize responsibilities in the deliberative portion? How does overall behavior emerge?

7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm8 Architectures: Common Functionality Mission planner Cartographer Sequencer agent Behavioral manager Performance monitor/problem solving agent (fairly rare)

7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm9 Architectures: 3 Styles Managerial (division of responsibilities looks like in business) –AuRA, SFX State Hierarchies (strictly by time scope) –3T Model-Oriented (models serve as virtual sensors) –Saphira, TCA

7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm10 Mgr Architecture 1: AuRA (Autonomous Robot Arch.) Ron Arkin, Georgia Institute of Technology

7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm11 AuRA Architectural Layout reactive deliberative

7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm12 Architectures: Common Functionality Mission planner Cartographer Sequencer agent Behavioral manager Performance monitor/problem solving agent

7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm13 AuRA Architectural Layout Cartographer Sequencer Mission Planner Behavioral manager (mgr+schemas) Performance Monitoring Emergent behavior

7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm14 HOW WOULD THIS DO USAR TASK?

7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm15 Motivating Example for Deliberation: USAR Worker places robot at entrance to unstable building, loads in the floor plan, contextual knowledge and tells robot to look for survivors efficiently and map out safe routes for workers to pass through contextual knowledge includes probability of where people are more likely to be What can a reactive architecture do? What can’t it do? Path planning, handling detours due to blockage, map making, learn from past rescues

7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm16 Motivating Example for Deliberation: USAR Worker places robot at entrance to unstable building, loads in the floor plan, contextual knowledge and tells robot to look for survivors efficiently and map out safe routes for workers to pass through contextual knowledge includes probability of where people are more likely to be

7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm17 Example USAR (overlay) Cartographer accepts the map Navigator uses path planning algorithm to visit nodes in order of likelihood of survivors Pilot determines the list of behaviors, Motor Schema Manager instantiates them (MS & PS) and waits for termination Homeostatic might notice that robot is running out of power, so opportunistically picks up low probability room on way back to home

7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm18 Mgr. Architecture 2: SFX (Sensor Fusion Effects) Focus on sensing Biomimetic organization deliberative layer consists of managerial agents reactive layer has tactical behaviors

7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm19 SFX (Sensor Fusion Effects)

7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm20 SFX (Sensor Fusion Effects) Behaviors (using direct perception, fusion) Sense Muscle Actuators Deliberative Layer Managers Sense Sensor Sense Receptive Field Choice of behaviors, resource allocation, motivation, context Focus of attention, recalibration Sensor Whiteboard Behavioral Whiteboard Deliberative Layer Reactive Layer Parameters to behaviors, sensor failures, task progress actions Superior Colliculus-like functions Cerebral Cortex-like functions Cartographer (model/map making) Recognition perception

7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm21 Cartographer SFX Implementation Sensor Mgr Task Planner Interface Mgr Behaviors Sensors Acuators Effector Mgr Lisp C++ Behaviors Use, Fuse Information directs sensing HOW WOULD THIS DO USAR?

7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm22 Ability to Substitute Components

7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm23 Motivating Example for Deliberation: USAR Worker places robot at entrance to unstable building, loads in the floor plan, contextual knowledge and tells robot to look for survivors efficiently and map out safe routes for workers to pass through contextual knowledge includes probability of where people are more likely to be

7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm24 Example USAR (overlay) Cartographer accepts the map Task Planner agent asks for path, requests behaviors, passes to managerial layer Sensing and Effector Mgrs negotiate allocation Behaviors run until terminate or encounter exception (either preset condition by mgrs or through monitoring) Mgrs can see “below” but not above--cannot relax constraint of Planner/Boss

7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm25 Tactical Behaviors

7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm26 UGV Competition 1997

7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm27 Summary: Managerial Architectures How does the architecture distinguish between reaction and deliberation? –Deliberation: global knowledge or world models, projection forward or backward in time –Reaction: behaviors which have some past/persistence of perception and external state How does it organize responsibilities in the deliberative portion? –hierarchy of managerial responsibility, managers may be peer software agents How does overall behavior emerge? –From interactions of a set of behaviors dynamically instantiated and modified by the deliberative layer –assemblages of behaviors

7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm28 Chapter 7: Hybrid Deliberative/Reactive Paradigm Part 1: Overview & Managerial Architectures Part 2: State Hierarchy & Model-Based Architectures

7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm29 Objectives Describe the hybrid paradigm in terms of 1) SPA and 2) sensing organization Given a list of responsibilities, be able to say whether it belongs in the deliberative layer or in the reactive layer List the five basic components of a Hybrid architecture: sequencer agent, resource manager, cartographer, mission planner, performance monitoring and problem solving agent. Be able to describe the difference between managerial, state hierarchy, and model-oriented styles of Hybrid architectures. Be able to describe the use of state to define behaviors and deliberative responsibilities in state hierarchy styles of Hybrid architectures

7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm30 Plan, Sense-Act

7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm31 Sensing Organization

7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm32 Architectures: 3 Styles Managerial (division of responsibilities looks like in business) –AuRA, SFX State Hierarchies (strictly by time scope or “state”) –3T Model-Oriented (models serve as virtual sensors) –Saphira, TCA

7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm33 State Hierarchy Architectures How does the architecture distinguish between reaction and deliberation? –Deliberation: requires PAST or FUTURE knowledge –Reaction: behaviors are purely reflexive and have only local, behavior specific; require only PRESENT How does it organize responsibilities in the deliberative portion? –By internal temporal state PRESENT (controller) PAST (sequencer) FUTURE (planner) –By speed of execution How does overall behavior emerge? –From generation and monitoring of a sequence of behaviors –assemblages of behaviors called skills –subsumption

7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm34 3T Architecture Used extensively at NASA Merging of subsumption variation (Gat, Bonasso), RAPs (Firby), and vision (Kortenkamp) Has 3 layers –reactive –deliberative –in-between (reactive planning) Arranges by time Arranges by execution rate –ex. vision in deliberation Dave Kortenkamp, TRAC Labs (NASA JSC)

7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm35

7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm36 cartographer Mission planner sequencer Behavior mgr Performance monitor Emergent behavior

7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm37 Motivating Example for Deliberation: USAR Worker places robot at entrance to unstable building, loads in the floor plan, contextual knowledge and tells robot to look for survivors efficiently and map out safe routes for workers to pass through contextual knowledge includes probability of where people are more likely to be What can a reactive architecture do? What can’t it do? Path planning, handling detours due to blockage, map making, learn from past rescues

7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm38 Model-Oriented Architectures How does the architecture distinguish between reaction and deliberation? –Deliberation: anything relating a behavior to a goal or objective –Reaction: behaviors are “small control units” operating in present, but may use global knowledge as if it were a sensor (virtual sensor) How does it organize responsibilities in the deliberative portion? –Behavioral component –Model of the world and state of the robot –throwback to Hierarchical Paradigm with global world model but virtual sensors –Deliberative functions How does overall behavior emerge? –From generation and monitoring of a sequence of behaviors –voting or fuzzy logic for combination

7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm39 Saphira Architecture Developed at SRI by Konolige, Myers, Saffioti Comes with Pioneer robots Behaviors produce fuzzy outputs, fuzzy logic combines them Has a global rep called a Local Perceptual Structure to filter noise Instead of RAPs, uses PRS-Lite

7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm40 Saphira and LPS

7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm41 Sequencer agent, Mission Planning, Performance mon. Cartographer Behavior mgr Emergent behavior

7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm42 Symbol-Grounding Problem Computers (and AI) reasons using symbols –Ex. “room”, “box,” “corner,” “door” Robots perceive raw data How to convert sensor readings to these labels?

7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm43 Spatial World Knowledge What do you see? How could a robot reliably extract the same labels?

7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm44 Types of Knowledge (Arkin) Spatial World knowledge Object knowledge Perceptual knowledge Behavioral knowledge Ego knowledge Intentional knowledge

7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm45 Task Control Architecture Developed by Reid Simmons Used extensively by CMU Field Robotics Projects –NASA’s Nomad, Ambler, Dante Closest to Hierarchical in philosophy, but strong reactive theme showing up in implementation

7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm46 TCA

7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm47 Motivating Example for Deliberation: USAR Worker places robot at entrance to unstable building, loads in the floor plan, contextual knowledge and tells robot to look for survivors efficiently and map out safe routes for workers to pass through contextual knowledge includes probability of where people are more likely to be What can a reactive architecture do? What can’t it do? Path planning, handling detours due to blockage, map making, learn from past rescues

7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm48 Evaluation of Hybrids Support of Modularity: high Niche targetability: high (ex. Lower levels of AuRA, SFX, 2 1/2 T is just reactive) Robustness: SFX and 3T explicitly monitor Think in closed world, act in open world

7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm49 Hybrid Summary P,S-A, deliberation uses global world models, reactive uses behavior-specific or virtual sensors Architectures generally have modules for mission planner, sequencer, behavioral mgr, cartographer, and performance monitoring Deliberative component is often divided into sub-layers (sequencer/mission planner or managers/mission planner) Reactive component tends to use assemblages of behaviors

7 Introduction to AI Robotics (MIT Press), copyright Robin Murphy 2000 Chapter 7: Hybrid Deliberative/Reactive Paradigm50 Class Exercise Form groups of 3 Design Hostage Rescue robot software –What are the key tasks? Robot capabilities? Environment? –Do you need to know more? –What paradigm? What architectural style? Is there deliberation or just reaction? Which paradigm?