2,579 research outputs found
Market-based Risk Allocation for Multi-agent Systems
No full textThis 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
[14C]Fluciclovine (alias anti-[14C]FACBC) uptake and ASCT2 expression in castration-resistant prostate cancer cells．
No full text金沢大学博士（保健学）博士論文本文Full 以下に掲載：Nuclear Medicine and Biology 42(11) pp.887-892 2015. Elsevier. 共著者：Masahiro Ono, Shuntaro Oka, Hiroyuki Okudaira, Takeo Nakanishi, Atsushi Mizokami, Masato Kobayashi, David M. Schuster, Mark M. Goodman, Yoshifumi Shirakami, Keiichi Kawaidoctoral thesi
[14C]Fluciclovine (alias anti-[14C]FACBC) uptake and ASCT2 expression in castration-resistant prostate cancer cells．
No full text博士論文本文Full 以下に掲載：Nuclear Medicine and Biology 42(11) pp.887-892 2015. Elsevier. 共著者：Masahiro Ono, Shuntaro Oka, Hiroyuki Okudaira, Takeo Nakanishi, Atsushi Mizokami, Masato Kobayashi, David M. Schuster, Mark M. Goodman, Yoshifumi Shirakami, Keiichi Kawa
Decentralized chance-constrained finite-horizon
No full textThis paper considers finite-horizon optimal control for multi-agent systems subject to additive Gaussian-distributed stochastic disturbance and a chance constraint. The problem is particularly difficult when agents are coupled through a joint chance constraint, which limits the probability of constraint violation by any of the agents in the system. Although prior approaches can solve such a problem in a centralized manner, scalability is an issue. We propose a dual decomposition-based algorithm, namely Market-based Iterative Risk Allocation (MIRA), that solves the multi-agent problem in a decentralized manner. The algorithm addresses the issue of scalability by letting each agent optimize its own control input given a fixed value of a dual variable, which is shared among agents. A central module optimizes the dual variable by solving a root-finding problem iteratively. MIRA gives exactly the same optimal solution as the centralized optimization approach since it reproduces the KKT conditions of the centralized approach. Although the algorithm has a centralized part, it typically uses less than 0.1% of the total computation time. Our approach is analogous to a price adjustment process in a competitive market called tatonnement or Walrasian auction: each agent optimizes its demand for risk at a given price, while the central module (or the market) optimizes the price of risk, which corresponds to the dual variable. We give a proof of the existence and optimality of the solution of our decentralized problem formulation, as well as a theoretical guarantee that MIRA can find the solution. The empirical results demonstrate a significant improvement in scalability.Boeing Company (grant MIT-BA-GTA-1
Chance-Constrained Optimal Path Planning With Obstacles
No full textAutonomous vehicles need to plan trajectories to a specified goal that avoid obstacles. For robust execution, we must take into account uncertainty, which arises due to uncertain localization, modeling errors, and disturbances. Prior work handled the case of set-bounded uncertainty. We present here a chance-constrained approach, which uses instead a probabilistic representation of uncertainty. The new approach plans the future probabilistic distribution of the vehicle state so that the probability of failure is below a specified threshold. Failure occurs when the vehicle collides with an obstacle or leaves an operator-specified region. The key idea behind the approach is to use bounds on the probability of collision to show that, for linear-Gaussian systems, we can approximate the nonconvex chance-constrained optimization problem as a disjunctive convex program. This can be solved to global optimality using branch-and-bound techniques. In order to improve computation time, we introduce a customized solution method that returns almost-optimal solutions along with a hard bound on the level of suboptimality. We present an empirical validation with an aircraft obstacle avoidance example.National Science Foundation (U.S.) (Grant IIS-1017992)Boeing Company (Grant MIT-BA-GTA-1
Chance Constrained Finite Horizon Optimal Control
No full textThis paper considers finite-horizon optimal control for dynamic systems subject to additive Gaussian-distributed stochastic disturbance and a chance constraint on the system state defined on a non-convex feasible space. The chance constraint requires that the probability of constraint violation is below a user-specified risk bound. A great deal of recent work has studied joint chance constraints, which are defined on the a conjunction of linear state constraints. These constraints can handle convex feasible regions, but do not extend readily to problems with non-convex state spaces, such as path planning with obstacles. In this paper we extend our prior work on chance constrained control in non-convex feasible regions to develop a new algorithm that solves the chance constrained control problem with very little conservatism compared to prior approaches. In order to address the non-convex chance constrained optimization problem, we present two innovative ideas in this paper. First, we develop a new bounding method to obtain a set of decomposed chance constraints that is a sufficient condition of the original chance constraint. The decomposition of the chance constraint enables its efficient evaluation, as well as the application of the branch and bound method. However, the slow computation of the branch and bound algorithm prevents practical applications. This issue is addressed by our second innovation called Fixed Risk Relaxation (FRR), which efficiently gives a tight lower bound to the convex chance-constrained optimization problem. Our empirical results show that the FRR typically makes branch and bound algorithm 10-20 times faster. In addition we show that the new algorithm is significantly less conservative than the existing approach.Boeing Company (Grant MIT-BA-GTA-1)United States. National Aeronautics and Space Administratio
ジェンツーペンギンにおける画像検査を目的とした全身麻酔法の確立に関する研究
No full text原著論文
Koji Ono, Masahiro Yamasaki, Toshihiro Ichijo, Hiroshi Satoh
Effects of alfaxalone on the induction and maintenance of total intravenous anesthesia in gentoo penguins (Pygoscelis papua)
Journal of Avian Medicine and Surgery 37(1), 13-21, 202
Letter from Gerald Masahiro Sato, attorney at law, World Trade Center, JABA Board Members, May 6, 1982
No full textLetter from Gerald Masahiro Sato, attorney at law, World Trade Center, to the Japanese American Board Association (JABA) board members, about endorsing the National Coalition for Redress/Reparations (NCRR).The Jim Matsuoka Nikkei for Civil Rights and Redress Collection includes brochures, meeting notes and agendas, publications, booklets, and other material related to the Nikkei for Civil Rights and Redress (NCRR), formally known as the National Coalition for Redress/Reparations. The National Coalition for Redress/Reparations was officially formed on July 12, 1980, and included members of the Los Angeles Community Coalition for Redress/Reparations (LACCRR), Japanese Community Progressive Alliance (JCPA), Tule Lake Committee, Nihonmachi Outreach Committee, the Asian/Pacific Student Union, and other members of the community. The material was collected by Jim Matsuoka, a founding member of the organization. Matsuoka also served on the board and was the treasurer. In addition to the NCRR material, the collection also contains event flyers and Day of Remembrance material. For issues of the Nikkei for Civil Rights and Redress newsletter "Banner" published after 2007, visit the NCRR website at https://ncrr-la.org/
A probabilistic particle-control approximation of chance-constrained stochastic predictive control
No full textRobotic systems need to be able to plan control actions that are robust to the inherent uncertainty in the real world. This uncertainty arises due to uncertain state estimation, disturbances, and modeling errors, as well as stochastic mode transitions such as component failures. Chance-constrained control takes into account uncertainty to ensure that the probability of failure, due to collision with obstacles, for example, is below a given threshold. In this paper, we present a novel method for chance-constrained predictive stochastic control of dynamic systems. The method approximates the distribution of the system state using a finite number of particles. By expressing these particles in terms of the control variables, we are able to approximate the original stochastic control problem as a deterministic one; furthermore, the approximation becomes exact as the number of particles tends to infinity. This method applies to arbitrary noise distributions, and for systems with linear or jump Markov linear dynamics, we show that the approximate problem can be solved using efficient mixed-integer linear-programming techniques. We also introduce an important weighting extension that enables the method to deal with low-probability mode transitions such as failures. We demonstrate in simulation that the new method is able to control an aircraft in turbulence and can control a ground vehicle while being robust to brake failures
Study on the establishment of general anesthesia for imaging examinations of gentoo penguins
No full text岩手大学博士（獣医学）原著論文
Koji Ono, Masahiro Yamasaki, Toshihiro Ichijo, Hiroshi Satoh
Effects of alfaxalone on the induction and maintenance of total intravenous anesthesia in gentoo penguins (Pygoscelis papua)
Journal of Avian Medicine and Surgery 37(1), 13-21, 2023doctoral thesi
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