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Keisuke Otaki

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4 papers

2 author rows

AAAI Conference 2022 Conference Paper

Partial Wasserstein Covering

Keisuke Kawano
Satoshi Koide
Keisuke Otaki

We consider a general task called partial Wasserstein covering with the goal of providing information on what patterns are not being taken into account in a dataset (e. g. , dataset used during development) compared with another dataset(e. g. , dataset obtained from actual applications). We model this task as a discrete optimization problem with partial Wasserstein divergence as an objective function. Although this problem is NP-hard, we prove that it satisfies the submodular property, allowing us to use a greedy algorithm with a 0. 63 approximation. However, the greedy algorithm is still inefficient because it requires solving linear programming for each objective function evaluation. To overcome this inefficiency, we propose quasi-greedy algorithms that consist of a series of acceleration techniques, such as sensitivity analysis based on strong duality and the so-called C-transform in the optimal transport field. Experimentally, we demonstrate that we can efficiently fill in the gaps between the two datasets and find missing scene in real driving scenes datasets.

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AAAI Conference 2022 Conference Paper

Planning with Explanations for Finding Desired Meeting Points on Graphs

Keisuke Otaki

Combinatorial optimization problems are ubiquitous for decision making in planning social infrastructures. In real-world scenarios, a decision-maker needs to solve his/her problem iteratively until he/she satisfies solutions, but such an iterative process remains challenging. This paper studies a new explainable framework, particularly for finding meeting points, which is a key optimization problem for designing facility locations. Our framework automatically fills the gap between its input instance and instances from which a user could obtain the desired outcome, where computed solutions are judged by the user. The framework also provides users with explanations, representing the difference of instances for deeply understanding the process and its inside. Explanations are clues for users to understand their situation and implement suggested results in practice (e. g. , designing a coupon for free travel). We experimentally demonstrate that our search-based framework is promising to solve instances with generating explanations in a sequential decision-making process.

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SoCS Conference 2020 Conference Paper

Cooperative Path Planning for Heterogeneous Agents

Keisuke Otaki
Satoshi Koide
Ayano Okoso
Tomoki Nishi

Cooperation among different vehicles is a promising concept for route planning of Mobility as a Service (MaaS). For instance, vehicle platooning on highways decreases fuel consumption because it reduces the air resistance and several trucks cooperate with each other when planning. Traditional platooning, however, cannot model cooperation among different types of vehicles because it assumes the homogeneity of vehicle types. We study a model that permits heterogeneous cooperation and discuss a route optimization problem under assumption that the heterogeneous cooperation benefits the objective function. We experimentally evaluate the formulation through using synthetic and real graphs based on a modern integer programming solver with various parameter settings, which are not tried in previous studies. We also compare the results by the solves with simple heuristic method developed in this paper and discuss the results to reveal the properties of the optimization problem with heterogeneous vehicle types.

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AAMAS Conference 2019 Conference Paper

Cooperative Routing with Heterogeneous Vehicles

Keisuke Otaki
Satoshi Koide
Ayano Okoso
Tomoki Nishi

Cooperation among different vehicles is a promising application for Mobility as a Service (MaaS). A primary problem is optimizing the vehicle routes. In this paper, we propose a new concept, named delegation, where heterogeneous vehicles cooperate to reduce the total travel cost. Our study models a case in logistics, where a large truck for long-distance delivery carries small self-driving cargoes for the last mile delivery, and the travel cost of the small ones is discounted. We define an optimization problem enabling delegation, propose its integer programming (IP) instance, and discuss our concept through numerical experiments using a modern IP solver.

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