Distributed Task Assignment in Real World Environments

1. Alessandro Farinelli farinelli @ dis.uniroma1.it www.dis.uniroma1.it/~lastname Distributed Task Assignment in Real World Environments SIED Lab sied.dis.uniroma1.it

2. Reference Scenarios • Physical entities embedded in the working environment • World is dynamic and unpredictable • Knowledge is acquired throughperceptioncapabilities

3. Task Assignment Approaches • Market Based Method[Gerkey et al. 00, Dias et al. 01]: • Flexibility, tasks given to best available agent • High number of msgs, sync. back and forth • Iterative task assignment[Iocchi et al. 03, Werger et al. 00]: • all task allocated at fixed time interval • Based on updated information, robust • Very high communication overhead • Reactive task assignment (ALLIANCE) [Parker 98]: • Reduced communication, robust to failures • Global properties very hard to guarantees. • Negotiation (SPAM) [Mailler et al. 03]: • Resource allocation, anytime approach • Very complex, back and forth of msgs • DCOP: ADOPT [Modi et al. 03]: • Asynchronous, optimal approach • High communication overhead MRS Towards Robustness MAS Towards Optimality

4. Problem Definition Set of Robots Set of Tasks Set of Roles for each task Allocation Value Given: • No conflicts on role allocation • Role constraints are satisfied

5. Token Passing Approach to TA: LA-DCOP • Proposed in [Scerri, Farinelli, Okamoto, Tambe 2005] • Tasks are tokens circulating among agents • Can be framed as a DCOP: • Agents = Variable • Tasks = Values • LA-DCOP (Low communication Approximate DCOP)

6. LA-DCOP General Idea Task Ag Ag Ag Constrained Tasks (AND constraint) Ag Ag Ag Ag • Agent decision (should I take on this Role ?) is always local • Thresholds on capabilities => maximize team perf. • Potential tokens for constrained tasks • Tokens are a mean to circulate information

7. LA-DCOP ANDed Tasks Deadlock Starvation ANDed Tasks => deadlock or starvation situations Ag Ag Ag Ag Ag Ag • Potential Tokens for constrained tasks • Manager Agent for potential tokens • Specific messages to manage potential tokens (i.e. Release, Lock, Retained)

8. Dynamic Token Generation • Dynamic Task Generation => Token repetition • No control on the number of tokens for the same task • Conflicts among team members T2(Obj) T1(Obj) R R Obj R T3(Obj) • Distributed Conflict Detection • Use Negative Tokens to announce token generation • Invalidate Conflicting Tokens • Requires Total ordering among agents to resolve conflicts

9. Distributed conflict detection Ann(O,A1) AnnMsgS AnnMsgS AnnMsgS A1 A2 T(O) A1 T(O) A2 T(O) A2 T(O) A2 ITS ITS O Ann(O,A2) T(O) A1 T(O) A1 Ann(O,A1) KTS KTS T(O) T(O) Ann(O,A2) AnnMsgS A3 ITS KTS T(O) A2 T(O) A1 T(O)

10. Obtained Results

11. MRS Noisy Perception • Sensor data are incorrect • Feature extraction process can be faulty Solutions so Far: • Centralize sensor data • Aggregate and filter data Problems: • Huge amount of communication • Data have to be synchronized Courtesy of Grisetti and Stachniss Noisy readings

12. Cooperative action and perception • Cooperative perception [Dietel et al 01] • Integrate data coming from different sources • Mainly centralized, no coordination • No cooperative perception [Gerkey et al. 02, Parker 98] • Individual perceived data assumed to be correct • Individual perception errors propagate • POMDPs [Pynadath-Tambe 02] • Can model uncertainty about actions and perceptions • Too complex for complex scenarios

13. Our Approach Use task assignment to drive the situation assessment process I don’t see any obj. there Let’s examine this obj

14. Our Approach – key ideas • Decision theoretic Reasoning to revise past decisions • Robots continually monitor their beliefs • Beliefs associated to coordination messages • Robots send explanations for their decisions to team mates • Integrate past beliefs • Past beliefs of other robots can be integrated to refute or support current beliefs (Bayesian Filter) • Robots need to remember seen and not seen features

15. Problem Formulation • observed features • observation probabilities • Robot’s belief • World change model Robots should act only when: Cost for acting not-properly Reward for acting properly Cost for not acting

16. Algorithm Observations A2 A3 WorldBelief DoNothing Justification For Acting No Act ? Acting ? A1 No Yes Yes SendCoordMsg StopAction

17. World Representation IPS = Interesting Point Set OS = Observable Space PO = Positive Observations Given a Coord Message for an event(feature) e: For each entry of my OS History: • if f is inside entry and PO contains e => Justification: support • If e is inside entry and PO does not contain e => Justification: refute • If e is not inside entry => do nothing

18. Knowledge Update t’ = t • Histories for OS, PO to generate observations (negative and positive) • History of IPS to maintain past belief over interesting features • Re-run bayesian filter using the histories

19. Reference Scenario Hypotheses Robots know the environment evolution model Robots know their observation model Robots know the map of the environment Robots know their position inside the environment

20. Performance Measures • PGood = Corr_stopped_plans/stopped_plans relates to correctness • PFound = Corr_stopped_plans/Inc_started_plans relates to completness Strategies Compared • Our approach • Share All Strategy: broadcast all observations

21. Experimental Framework • Abstract Simulator • Large scale • Low fidelity • Developed by scratch • Player/Stage Simulator • High fidelity 2D simulation environment • Good sensor simulations (Laser Range Finder, Color Camera) • Fairly accurate robots model • Shared experimental environment in robotic community • RDK = Robot Development Kit • Framework developed by our group • General framework to develop robotic applications

22. Results: Player/Stage + RDK One Group Two Groups Three Groups

23. Conclusions • Token Passing for Task Assignment • Approximate solutions in real time • Totally distributed framework • Uncertainty at the coordination level • Novel strategy for combining Task Assignment and Situation Assessment • Distributed approach with low communication requirement

24. Future Work • Focus on Distributed mapping and exploration • Multi Objective Exploration • Communication maintenance • Human in the loop, “correct” degree of autonomy • Refinement of the information gathering approach • Dealing with real noisy sensors • Data integration techniques • Transfer technology in the field • Collaborate with experts • Devise “pragmatic solutions”

25. References related to this work • Multi Robot Systems: A classification based on coordination, A. Farinelli, L. Iocchi and D. Nardi. IEEE Transactions on System Man and Cybernetics, part B. Vol 34 Numb 5 pp. 2015-2028 • Token Approach for Role Allocation in Extreme Teams, Scerri, P. and Farinelli, A. and Okamoto, S. and Tambe, M. In Proc. of AAMAS 2005 pp 727-734 • Low-Overhead Cooperative Detection of False Sensor Readings, Farinelli, A.andScerri, P. In Proc. of AAMAS workshop: Challenges in the Coordination of Large Scale Multi-Agent Systems (LSMAS) pp 11-16, 2005 • Our Group: • Daniele Nardi, Luca Iocchi, Alessandro Farinelli, Giorgio Grisetti, Vittorio Amos Ziparo, Shahram Bahadori, Daniele Calisi, Gian Diego Tipaldi, Luca Marchetti, Giuseppe Paolo Settembre

26. Coordinationas Task Assignment Given a decomposition in sub-tasks => which entity performs which sub-task(s) • Relevant both to MAS and MRS • Task Assignment => Strong Coordination • distributed task assignment = centralized task assignment

27. Task Assignment in MAS • Auction Based Method: CNP [Smith 80] • Flexibility, tasks given to best available agent • High number of msgs, sync. back and forth • Negotiation: SPAM [Mailler et al. 03] • Resource allocation, anytime approach • Very complex, back and forth of msgs • Distributed Constraint Based Reasoning: DSA [Fitzpatrick et al. 01] • Good performance and low communication • Critical point for degree of parallelization

28. Task Assignment in MRS • Sequential Task Assignment: task allocated as they enter the system [Gerkey et al. 00, Dias et al. 01] • Based on CNP • Iterative task assignment: all task allocated at fixed time interval [Castelpietra et al. 00, Werger et al. 00] • Based on updated information, robust • Very high communication overhead • Reactive task assignment: ALLIANCE [Parker 98] • Reduced communication, robust to failures • Global properties very hard to guarantee

29. MRS Vs. MAS • MRS => Robustness and reactivity critical • Noisy and unreliable perception • Iterative and reactive TA • MRS => Optimality not a primary issue • Very hard to obtain and even to measure • Quick reallocation is preferred • MRS => Broadcasting is not a big problem • Small teams (at least up to now) • MRS => Agent perception capabilities are crucial • Tasks perceived with consistent limitations

30. Results: Abstract Simulator Orders of Magnitude less messages