Market-based Risk Allocation for Multi-agent Systems
This paper proposes Market-based Iterative Risk Allocation (MIRA), a new market-based distributed planning algorithm for multi-agent systems under uncertainty. In large coordination problems, from power grid management to multi-vehicle missions, multiple agents act collectively in order to opti...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Association for Computing Machinery
2012
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| Online Access: | http://hdl.handle.net/1721.1/67892 https://orcid.org/0000-0002-1057-3940 |
| _version_ | 1811088641320026112 |
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| author | Ono, Masahiro Williams, Brian Charles |
| author2 | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics |
| author_facet | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Ono, Masahiro Williams, Brian Charles |
| author_sort | Ono, Masahiro |
| collection | MIT |
| description | This paper proposes Market-based Iterative Risk Allocation
(MIRA), a new market-based distributed planning
algorithm for multi-agent systems under uncertainty.
In large coordination problems, from power grid
management to multi-vehicle missions, multiple agents
act collectively in order to optimize the performance of
the system, while satisfying mission constraints. These
optimal plans are particularly susceptible to risk when
uncertainty is introduced. We present a distributed planning
algorithm that minimizes the system cost while
ensuring that the probability of violating mission constraints
is below a user-specified level. We build upon the paradigm of risk allocation (Ono
& Williams 2008), in which the planner optimizes not
only the sequence of actions, but also its allocation of
risk among each constraint at each time step. We extend
the concept of risk allocation to multi-agent systems
by highlighting risk as a commodity that is traded
in a computational market. The equilibrium price of
risk that balances the supply and demand is found by
an iterative price adjustment process called tˆatonnement
(also known as Walrasian auction). Our work is distinct
from the classical tˆatonnement approach in that we use
Brent’s method to provide fast guaranteed convergence
to the equilibrium price. The simulation results demonstrate
the efficiency of the proposed distributed planner. |
| first_indexed | 2024-09-23T14:04:53Z |
| format | Article |
| id | mit-1721.1/67892 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T14:04:53Z |
| publishDate | 2012 |
| publisher | Association for Computing Machinery |
| record_format | dspace |
| spelling | mit-1721.1/678922022-10-01T19:03:52Z Market-based Risk Allocation for Multi-agent Systems Ono, Masahiro Williams, Brian Charles Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Williams, Brian Charles Williams, Brian Charles Ono, Masahiro This paper proposes Market-based Iterative Risk Allocation (MIRA), a new market-based distributed planning algorithm for multi-agent systems under uncertainty. In large coordination problems, from power grid management to multi-vehicle missions, multiple agents act collectively in order to optimize the performance of the system, while satisfying mission constraints. These optimal plans are particularly susceptible to risk when uncertainty is introduced. We present a distributed planning algorithm that minimizes the system cost while ensuring that the probability of violating mission constraints is below a user-specified level. We build upon the paradigm of risk allocation (Ono & Williams 2008), in which the planner optimizes not only the sequence of actions, but also its allocation of risk among each constraint at each time step. We extend the concept of risk allocation to multi-agent systems by highlighting risk as a commodity that is traded in a computational market. The equilibrium price of risk that balances the supply and demand is found by an iterative price adjustment process called tˆatonnement (also known as Walrasian auction). Our work is distinct from the classical tˆatonnement approach in that we use Brent’s method to provide fast guaranteed convergence to the equilibrium price. The simulation results demonstrate the efficiency of the proposed distributed planner. 2012-01-03T17:09:56Z 2012-01-03T17:09:56Z 2010-05 Article http://purl.org/eprint/type/ConferencePaper http://hdl.handle.net/1721.1/67892 Ono, Masahiro and Brian C. Williams. "Market-based Risk Allocation for Multi-agent Systems." In Proceedings of the Ninth International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2010, Toronto, Canada, May 10-14, 2010. https://orcid.org/0000-0002-1057-3940 en_US http://www.cse.yorku.ca/AAMAS2010/index.php#content=accepted_papers Proceedings of the Ninth International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2010 Attribution-Noncommercial-Share Alike 3.0 Unported http://creativecommons.org/licenses/by-nc-sa/3.0/ application/pdf Association for Computing Machinery MIT web domain |
| spellingShingle | Ono, Masahiro Williams, Brian Charles Market-based Risk Allocation for Multi-agent Systems |
| title | Market-based Risk Allocation for Multi-agent Systems |
| title_full | Market-based Risk Allocation for Multi-agent Systems |
| title_fullStr | Market-based Risk Allocation for Multi-agent Systems |
| title_full_unstemmed | Market-based Risk Allocation for Multi-agent Systems |
| title_short | Market-based Risk Allocation for Multi-agent Systems |
| title_sort | market based risk allocation for multi agent systems |
| url | http://hdl.handle.net/1721.1/67892 https://orcid.org/0000-0002-1057-3940 |
| work_keys_str_mv | AT onomasahiro marketbasedriskallocationformultiagentsystems AT williamsbriancharles marketbasedriskallocationformultiagentsystems |