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Ping Hou

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12 papers
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12

JAIR Journal 2022 Journal Article

Proactive Dynamic Distributed Constraint Optimization Problems

  • Khoi D. Hoang
  • Ferdinando Fioretto
  • Ping Hou
  • William Yeoh
  • Makoto Yokoo
  • Roie Zivan

The Distributed Constraint Optimization Problem (DCOP) formulation is a powerful tool for modeling multi-agent coordination problems. To solve DCOPs in a dynamic environment, Dynamic DCOPs (D-DCOPs) have been proposed to model the inherent dynamism present in many coordination problems. D-DCOPs solve a sequence of static problems by reacting to changes in the environment as the agents observe them. Such reactive approaches ignore knowledge about future changes of the problem. To overcome this limitation, we introduce Proactive Dynamic DCOPs (PD-DCOPs), a novel formalism to model D-DCOPs in the presence of exogenous uncertainty. In contrast to reactive approaches, PD-DCOPs are able to explicitly model possible changes of the problem and take such information into account when solving the dynamically changing problem in a proactive manner. The additional expressivity of this formalism allows it to model a wider variety of distributed optimization problems. Our work presents both theoretical and practical contributions that advance current dynamic DCOP models: (i) We introduce Proactive Dynamic DCOPs (PD-DCOPs), which explicitly model how the DCOP will change over time; (ii) We develop exact and heuristic algorithms to solve PD-DCOPs in a proactive manner; (iii) We provide theoretical results about the complexity of this new class of DCOPs; and (iv) We empirically evaluate both proactive and reactive algorithms to determine the trade-offs between the two classes. The final contribution is important as our results are the first that identify the characteristics of the problems that the two classes of algorithms excel in.

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AAMAS Conference 2017 Conference Paper

Infinite-Horizon Proactive Dynamic DCOPs

  • Khoi D. Hoang
  • Ping Hou
  • Ferdinando Fioretto
  • William Yeoh
  • Roie Zivan
  • Makoto Yokoo

The Distributed Constraint Optimization Problem (DCOP) formulation is a powerful tool for modeling multi-agent coordination problems. Researchers have recently extended this model to Proactive Dynamic DCOPs (PD-DCOPs) to capture the inherent dynamism present in many coordination problems. The PD-DCOP formulation is a finite-horizon model that assumes a finite horizon is known a priori. It ignores changes to the problem after the horizon and is thus not guaranteed to find optimal solutions for infinite-horizon problems, which often occur in the real world. Therefore, we (i) propose the Infinite-Horizon PD-DCOP (IPD- DCOP) model, which extends PD-DCOPs to handle infinite horizons; (ii) exploit the convergence properties of Markov chains to determine the optimal solution to the problem after it has converged; (iii) propose three distributed greedy algorithms to solve IPD-DCOPs; (iv) provide theoretical quality guarantees on the new model; and (v) empirically evaluate both proactive and reactive algorithms to determine the tradeoffs between the two classes. The final contribution is important as, thus far, researchers have exclusively evaluated the two classes of algorithms in isolation. As a result, it is difficult to identify the characteristics of problems that they excel in. Our results are the first in this important direction.

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IJCAI Conference 2017 Conference Paper

New Metrics and Algorithms for Stochastic Goal Recognition Design Problems

  • Christabel Wayllace
  • Ping Hou
  • William Yeoh

Goal Recognition Design (GRD) problems involve identifying the best ways to modify the underlying environment that agents operate in, typically by making a subset of feasible actions infeasible, in such a way that agents are forced to reveal their goals as early as possible. The Stochastic GRD (S-GRD) model is an important extension that introduced stochasticity to the outcome of agent actions. Unfortunately, the worst-case distinctiveness (wcd) metric proposed for S-GRDs has a formal definition that is inconsistent with its intuitive definition, which is the maximal number of actions an agent can take, in the expectation, before its goal is revealed. In this paper, we make the following contributions: (1) We propose a new wcd metric, called all-goals wcd (wcdag), that remedies this inconsistency; (2) We introduce a new metric, called expected-case distinctiveness (ecd), that weighs the possible goals based on their importance; (3) We provide theoretical results comparing these different metrics as well as the complexity of computing them optimally; and (4) We describe new efficient algorithms to compute the wcdag and ecd values.

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IJCAI Conference 2016 Conference Paper

Goal Recognition Design with Stochastic Agent Action Outcomes

  • Christabel Wayllace
  • Ping Hou
  • William Yeoh
  • Tran Cao Son

Goal Recognition Design (GRD) problems involve identifying the best ways to modify the underlying environment that the agents operate in, typically by making a subset of feasible actions infeasible, in such a way that agents are forced to reveal their goals as early as possible. Thus far, existing work assumes that the outcomes of the actions of the agents are deterministic, which might be unrealistic in real-world problems. For example, wheel slippage in robots cause the outcomes of their movements to be stochastic. In this paper, we generalize the GRD problem to Stochastic GRD (S-GRD) problems, which handle stochastic action outcomes. We also generalize the worst-case distinctiveness (wcd) measure, which measures the goodness of a solution, to take stochasticity into account. Finally, we introduce Markov decision process (MDP) based algorithms to compute the wcd and minimize it by making up to k actions infeasible.

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AAMAS Conference 2016 Conference Paper

Proactive Dynamic Distributed Constraint Optimization

  • Khoi D. Hoang
  • Ferdinando Fioretto
  • Ping Hou
  • Makoto Yokoo
  • William Yeoh
  • Roie Zivan

Current approaches that model dynamism in DCOPs solve a sequence of static problems, reacting to changes in the environment as the agents observe them. Such approaches thus ignore possible predictions on future changes. To overcome this limitation, we introduce Proactive Dynamic DCOPs (PD-DCOPs), a novel formalism to model dynamic DCOPs in the presence of exogenous uncertainty. In contrast to reactive approaches, PD-DCOPs are able to explicitly model the possible changes to the problem, and take such information into account proactively, when solving the dynamically changing problem. The additional expressivity of this formalism allows it to model a wider variety of distributed optimization problems. Our work presents both theoretical and practical contributions that advance current dynamic DCOP models: (i) we introduce the PD-DCOP model, which explicitly captures dynamic changes of the DCOP over time; (ii) we discuss the complexity of this new class of DCOPs; and (iii) we develop both exact and approximation algorithms with quality guarantees to solve PD- DCOPs proactively.

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IJCAI Conference 2016 Conference Paper

Probabilistic Planning with Risk-Sensitive Criterion

  • Ping Hou

While probabilistic planning have been extensively studied by artificial intelligence communities for planning under uncertainty, the objective to minimize the expected cumulative cost is inappropriate for high-stake planning problems. With this motivation in mind, we revisit the Risk-Sensitive criterion (RS-criterion), where the objective is to find a policy that maximizes the probability that the cumulative cost is within some user-defined cost threshold. By combining goal-directed MDPs and POMDPs with the RS-criterion, the corresponding risk-sensitive probabilistic planning models -Risk-Sensitive MDPs (RS-MDPs) and Risk-Sensitive POMDPs (RS-POMDPs) - can be formalized. The overall scope of this research is to develop efficient and scalable RS-MDP and RS-POMDP algorithms.

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AAAI Conference 2016 Conference Paper

Solving Risk-Sensitive POMDPs With and Without Cost Observations

  • Ping Hou
  • William Yeoh
  • Pradeep Varakantham

Partially Observable Markov Decision Processes (POMDPs) are often used to model planning problems under uncertainty. The goal in Risk-Sensitive POMDPs (RS-POMDPs) is to find a policy that maximizes the probability that the cumulative cost is within some user-defined cost threshold. In this paper, unlike existing POMDP literature, we distinguish between the two cases of whether costs can or cannot be observed and show the empirical impact of cost observations. We also introduce a new search-based algorithm to solve RS-POMDPs and show that it is faster and more scalable than existing approaches in two synthetic domains and a taxi domain generated with real-world data.

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ICAPS Conference 2014 Conference Paper

Revisiting Risk-Sensitive MDPs: New Algorithms and Results

  • Ping Hou
  • William Yeoh 0001
  • Pradeep Varakantham

While Markov Decision Processes (MDPs) have been shown to be effective models for planning under uncertainty, the objective to minimize the expected cumulative cost is inappropriate for high-stake planning problems. As such, Yu, Lin, and Yan (1998) introduced the Risk-Sensitive MDP (RS-MDP) model, where the objective is to find a policy that maximizes the probability that the cumulative cost is within some user-defined cost threshold. In this paper, we revisit this problem and introduce new algorithms that are based on classical techniques, such as depth-first search and dynamic programming, and a recently introduced technique called Topological Value Iteration (TVI). We demonstrate the applicability of our approach on randomly generated MDPs as well as domains from the ICAPS 2011 International Probabilistic Planning Competition (IPPC).

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AAAI Conference 2014 Conference Paper

Solving Uncertain MDPs by Reusing State Information and Plans

  • Ping Hou
  • William Yeoh
  • Tran Cao Son

While MDPs are powerful tools for modeling sequential decision making problems under uncertainty, they are sensitive to the accuracy of their parameters. MDPs with uncertainty in their parameters are called Uncertain MDPs. In this paper, we introduce a general framework that allows off-theshelf MDP algorithms to solve Uncertain MDPs by planning based on currently available information and replan if and when the problem changes. We demonstrate the generality of this approach by showing that it can use the VI, TVI, ILAO*, LRTDP, and UCT algorithms to solve Uncertain MDPs. We experimentally show that our approach is typically faster than replanning from scratch and we also provide a way to estimate the amount of speedup based on the amount of information being reused.

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