Author name cluster

Joseph Kim

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5 papers

2 author rows

SoCS Conference 2022 Conference Paper

Trajectory Constraint Heuristics for Optimal Probabilistic Planning

John R. Peterson
Anagha Kulkarni 0005
Emil Keyder
Joseph Kim
Shlomo Zilberstein

Search algorithms such as LAO* and LRTDP coupled with admissible heuristics are widely used methods for optimal probabilistic planning. Their effectiveness depends on the degree to which heuristics are able to approximate the optimal cost of a state. Most common domain-independent heuristics, however, rely on determinization, and ignore the probabilities associated with different effects of actions. Here, we present a method for decomposing a probabilistic planning problem into subproblems by constraining possible action outcomes. Admissible heuristics evaluated for each subproblem can then be combined via a weighted sum to obtain an admissible heuristic for the original problem that takes into account a limited amount of probabilistic information. We use this approach to derive new admissible heuristics for probabilistic planning, and show that for some problems they are significantly more informative than existing heuristics, leading to up to an order of magnitude speedups in the time to converge to an optimal policy.

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IJCAI Conference 2019 Conference Paper

Bayesian Inference of Linear Temporal Logic Specifications for Contrastive Explanations

Joseph Kim
Christian Muise
Ankit Shah
Shubham Agarwal
Julie Shah

Temporal logics are useful for providing concise descriptions of system behavior, and have been successfully used as a language for goal definitions in task planning. Prior works on inferring temporal logic specifications have focused on "summarizing" the input dataset - i. e. , finding specifications that are satisfied by all plan traces belonging to the given set. In this paper, we examine the problem of inferring specifications that describe temporal differences between two sets of plan traces. We formalize the concept of providing such contrastive explanations, then present BayesLTL - a Bayesian probabilistic model for inferring contrastive explanations as linear temporal logic (LTL) specifications. We demonstrate the robustness and scalability of our model for inferring accurate specifications from noisy data and across various benchmark planning domains.

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IJCAI Conference 2018 Conference Paper

Intelligent Decision Support for Human Team Planning

Joseph Kim

In my thesis, I develop computational models for an agent providing intelligent decision support (IDS) during human team planning sessions. My focus is on the development of an agent that help a team of human planners reach an agreement and produce high-quality plans prior to plan execution. I intend to develop novel techniques for an IDS agent that can 1) infer the team's intended plan from their planning conversation, 2) predict parts of the plan where the team's shared understanding is weak, and 3) suggest a resolution strategy when plan conflicts occur among teammates.

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IJCAI Conference 2018 Conference Paper

Learning to Infer Final Plans in Human Team Planning

Joseph Kim
Matthew E. Woicik
Matthew C. Gombolay
Sung-Hyun Son
Julie A. Shah

We envision an intelligent agent that analyzes conversations during human team meetings in order to infer the team’s plan, with the purpose of providing decision support to strengthen that plan. We present a novel learning technique to infer teams' final plans directly from a processed form of their planning conversation. Our method employs reinforcement learning to train a model that maps features of the discussed plan and patterns of dialogue exchange among participants to a final, agreed-upon plan. We employ planning domain models to efficiently search the large space of possible plans, and the costs of candidate plans serve as the reinforcement signal. We demonstrate that our technique successfully infers plans within a variety of challenging domains, with higher accuracy than prior art. With our domain-independent feature set, we empirically demonstrate that our model trained on one planning domain can be applied to successfully infer team plans within a novel planning domain.

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AAAI Conference 2017 Conference Paper

Collaborative Planning with Encoding of Users’ High-Level Strategies

Joseph Kim
Christopher Banks
Julie Shah

The generation of near-optimal plans for multi-agent systems with numerical states and temporal actions is computationally challenging. Current off-the-shelf planners can take a very long time before generating a near-optimal solution. In an effort to reduce plan computation time, increase the quality of the resulting plans, and make them more interpretable by humans, we explore collaborative planning techniques that actively involve human users in plan generation. Speciﬁcally, we explore a framework in which users provide high-level strategies encoded as soft preferences to guide the low-level search of the planner. Through human subject experimentation, we empirically demonstrate that this approach results in statistically signiﬁcant improvements to plan quality, without substantially increasing computation time. We also show that the resulting plans achieve greater similarity to those generated by humans with regard to the produced sequences of actions, as compared to plans that do not incorporate userprovided strategies.

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