h burkhard

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Software Architectures for Agents and Mobile Robots:

Software Architectures for Agents and Mobile Robots Hans-Dieter Burkhard Humboldt University Berlin Institute of Informatics www.ki.informatik.hu-berlin.de

Topics of the talk:

Topics of the talk Software Architectures for Agents and Mobile Robots AI at Humboldt University Agents & Robots Architectures Mental states Control, Planning Double Pass Architecture

Artificial Intelligence at Humboldt University:

Artificial Intelligence at Humboldt University

Artificial Intelligence at Humboldt University:

Artificial Intelligence at Humboldt University Case Based Reasoning Knowledge Management Agent Oriented Techniques Distributed AI Socionics Applications in Medicine Intelligent Robotics www.ki.informatik.hu-berlin.de English version

Example: Online Travel Agency :

Example: Online Travel Agency

Travel Agent: How does it work:

“Stimulus-Response” Travel Agent: How does it work Customer: Agent: Specify wish (fill in form) Prepare answer (select and present best matching offers)

Travel Agent: How does it work:

“Stimulus-Response” Agent needs: Knowledge about offers (data base) similarity (acceptable alternative offers) Capabilities to Update offers Interaction with customer Search of best matching offers (  Case Retrieval Nets) Travel Agent: How does it work

Travel Agent: How does it work:

Travel Agent: How does it work CRN = CASE RETRIEVAL NET

Travel Agent: How could it work:

Advisory agent Travel Agent: How could it work Customer Agent I would like to go for holidays.

Travel Agent: How could it work:

Advisory agent Travel Agent: How could it work Customer Agent I would like to go for holidays. Fine. Do you like swimming?

Travel Agent: How could it work:

Advisory agent Travel Agent: How could it work Customer Agent Yes, I like to be with my friend on a white strand, no other tourists. And I enjoy sports. Fine. Do you like swimming?

Travel Agent: How could it work:

Advisory agent Travel Agent: How could it work Customer Agent Yes, I like to be with my friend on a white strand, no other tourists. And I enjoy sports. Wonderful. And in the evening?

Travel Agent: How could it work:

Advisory agent Travel Agent: How could it work Customer Agent Good entertainment, exclusive bars, etc. Wonderful. And in the evening?

Travel Agent: How could it work:

Advisory agent Travel Agent: How could it work Customer Agent Good entertainment, exclusive bars, etc. Sounds fantastic, is this like what you want? (presents an offer)

Travel Agent: How could it work:

Advisory agent Travel Agent: How could it work Customer Agent Looks fantastic. But it is far behind of my financial limits, may be less exclusive. Sounds fantastic, is this like what you want? (presents an offer)

Travel Agent: How could it work:

Advisory agent Travel Agent: How could it work Customer Agent Looks fantastic. But it is far behind of my financial limits, may be less exclusive. So, let´s see. What´s about that? (presents another offer)

Travel Agent: How could it work:

Advisory Agent needs: Needs of “Stimulus Response” Agent (offers, capabilities, ...) as before Travel Agent: How could it work Dialog

Travel Agent: How could it work:

Advisory Agent needs: “Dynamic” knowledge about dialog with customer History of dialog (Hypothetical) Model of current customer Wishes, intentions Capabilities Beliefs (Flexible) Plan for Discovering customer´s wishes, intentions, ... Selling most valuable products Travel Agent: How could it work

Agent Oriented Techniques:

Agent Oriented Techniques Information agents Autonomous systems Cooperative systems Socionics: humans + autonomous machines Cooperation Sociological requirements Organizational aspects “Agents work autonomously on behalf of their users.” Autonomy: Following „own“ rules (example: chess program) Autonomy w.r.t. somebody Complexity of decisions

Control of Autonomous Mobile Robots:

Control of Autonomous Mobile Robots Problem: Dynamically changing environments “Autonomous agents in real environments” Problems: Localization, Movements, Control

Classical distinction of agents (robots):

Classical distinction of agents (robots) Reactive: Simple stimulus response behavior No planning No persistent states Deliberative Complicated deliberation Planning Persistent states

Sense-Think-Act-Cycle, Persistency:

Sense-Think-Act-Cycle, Persistency Environment

Reactive Systems:

Reactive Systems Obstacle avoidance by keeping distance Chess program ( ? - not “simple”) Sensor-Actor-Coupling

Deliberative Systems:

Deliberative Systems With 3 persistent states for worldmodel, goals, plans

Travel agent:

Travel agent Agent Worldmodel: Discriminating customer update select Goal: Sell pricey means-ends Plan: Show attractive offers etc. execute output

Unfolding the cycle:

Unfolding the cycle

Synchronization Problem:

Synchronization Problem update Simple Synchronization select means-ends update Problems for dynamical environments complex processes select means-ends Conflict

Question:

Question ROBOT = AGENT INSIDE A BODY ?

Simple architectures for physical agents:

Simple architectures for physical agents Stimulus-Response Immediate reactions to inputs from the real world. „The best model of the world is the world itself.“ Braitenberg Vehicle No need for a complex agent inside the robot

Soccer Playing Robots:

Soccer Playing Robots By the year 2050, develop a team of fully autonomous humanoid robots that can win against the human world soccer champion team. ENIAC 1946 Deep Blue 1997 Test field for Goal driven research

Annual World Championships and Conferences:

Annual World Championships and Conferences Simulation Rescue Sony legged Middle size Small size Humanoid www.robocup.org

Simple Stimulus-Response Behavior:

Simple Stimulus-Response Behavior Run to the ball

Simple Stimulus-Response Behavior:

Simple Stimulus-Response Behavior Run to the ball

Simple Stimulus-Response Behavior:

Simple Stimulus-Response Behavior Run to the ball

Simple Stimulus-Response Behavior:

Simple Stimulus-Response Behavior Run to the ball LOOP worldmodel := perceive (input); commitment := deliberate (worldmodel); output := execute(commitment); select sense execute think Agent A: xxx B: yyy C: zzz Sensor-Actor-Coupling

Why are they acting: Triggering events:

Why are they acting: Triggering events Stimulus-Response recent events in the environment Goal-directed recent events in the environment internal goals

Goal-directed Behavior:

Goal-directed Behavior Improvement: Anticipate future situations: Goal x

Goal-directed Behavior:

Goal-directed Behavior Improvement: Anticipate future situations: Goal x

Goal-directed Behavior:

Goal-directed Behavior Improvement: Anticipate future situations: Goal

Mental States:

Mental States Concerning past: Worldmodel Concerning future: Commitment (goal, intention, plan, ...) Mental states are persistent states: Keep information for more than one cycle

Stimulus-Response with Worldmodel:

Stimulus-Response with Worldmodel Simulate unobservable events: worldmodel

Stimulus-Response with Worldmodel:

Stimulus-Response with Worldmodel Simulate unobservable events: worldmodel

Stimulus-Response with Worldmodel:

Stimulus-Response with Worldmodel Simulate unobservable events: worldmodel LOOP worldmodel_new := update (input, worldmodel_old); commitment := deliberate (worldmodel); output := execute(commitment);

Worldmodel:

Worldmodel persistent state concerning the past: Worldmodel (Belief) worldmodel_new := update (input, worldmodel_old); Preprocessing of input from sensory signals