LSTM-Based Goal Recognition in Latent Space

Amado, Leonardo; Aires, João Paulo; Pereira, Ramon Fraga; Magnaguagno, Maurício Cecílio; Granada, Roger; Meneguzzi, Felipe

doi:10.48550/arxiv.1808.05249

Cited by 5 publications

(4 citation statements)

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“…MaxEnt IRL was proposed by Ziebart et al [17], which uses a probability approach to solve the ill-posed problem in the original IRL. Zeng et al modified MaxEnt IRL to solve the goal recognition problem in dynamic environment [18]. However, this work does not consider the situation when the agent implements deceptive plan and the proposed method does not work well in this scenario.…”

Section: Learning In Goal/plan Recognitionmentioning

confidence: 99%

Recognition and interfere deceptive behavior based on inverse reinforcement learning and game theory

Zeng

2023

J. of Syst. Eng. Electron.

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In real-time strategy (RTS) games, the ability of recognizing other players' goals is important for creating artifical intelligence (AI) players. However, most current goal recognition methods do not take the player 's deceptive behavior into account which often occurs in RTS game scenarios, resulting in poor recognition results. In order to solve this problem, this paper proposes goal recognition for deceptive agent, which is an extended goal recognition method applying the deductive reason method (from general to special) to model the deceptive agent 's behavioral strategy. First of all, the general deceptive behavior model is proposed to abstract features of deception, and then these features are applied to construct a behavior strategy that best matches the deceiver 's historical behavior data by the inverse reinforcement learning (IRL) method. Final, to interfere with the deceptive behavior implementation, we construct a game model to describe the confrontation scenario and the most effective interference measures.

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Section: Learning In Goal/plan Recognitionmentioning

confidence: 99%

Recognition and interfere deceptive behavior based on inverse reinforcement learning and game theory

Zeng

2023

J. of Syst. Eng. Electron.

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“…The search space generated by Latplan was shown to be compatible to an existing Goal Recognition system (Amado et al 2018a;2018b). Another recent approach replacing SAE/AMA 2 with InfoGAN (Kurutach et al 2018) has no explicit mechanism for improving the stability of the binary representation.…”

Section: Related Workmentioning

confidence: 99%

Towards Stable Symbol Grounding with Zero-Suppressed State AutoEncoder

Asai¹,

Kajino²

2019

ICAPS

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While classical planning has been an active branch of AI, its applicability is limited to the tasks precisely modeled by humans. Fully automated high-level agents should be instead able to find a symbolic representation of an unknown environment without supervision, otherwise it exhibits the knowledge acquisition bottleneck. Meanwhile, Latplan (Asai and Fukunaga 2018) partially resolves the bottleneck with a neural network called State AutoEncoder (SAE). SAE obtains the propositional representation of the image-based puzzle domains with unsupervised learning, generates a state space and performs classical planning. In this paper, we identify the problematic, stochastic behavior of the SAE-produced propositions as a new sub-problem of symbol grounding problem, the symbol stability problem. Informally, symbols are stable when their referents (e.g. propositional values) do not change against small perturbation of the observation, and unstable symbols are harmful for symbolic reasoning. We analyze the problem in Latplan both formally and empirically, and propose “Zero-Suppressed SAE”, an enhancement that stabilizes the propositions using the idea of closed-world assumption as a prior for NN optimization. We show that it finds the more stable propositions and the more compact representations, resulting in an improved success rate of Latplan. It is robust against various hyperparameters and eases the tuning effort, and also provides a weight pruning capability as a side effect.

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“…While there are several learning-based AMA methods that approximate AMA 1 (e.g. AMA 2 (Asai & Fukunaga, 2018) and Action Learner (Amado, Pereira, Aires, Magnaguagno, Granada, & Meneguzzi, 2018b;Amado, Aires, Pereira, Magnaguagno, Granada, & Meneguzzi, 2018a)), there is information loss between the learned action model and the original search space generated.…”

Section: Baseline Performance Experimentsmentioning

confidence: 99%

Photo-Realistic Blocksworld Dataset

Asai

2018

Preprint

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In this report, we introduce an artificial dataset generator for Photo-realistic Blocksworld domain. Blocksworld is one of the oldest high-level task planning domain that is well defined but contains sufficient complexity, e.g., the conflicting subgoals and the decomposability into subproblems. We aim to make this dataset a benchmark for Neural-Symbolic integrated systems and accelerate the research in this area. The key advantage of such systems is the ability to obtain a symbolic model from the real-world input and perform a fast, systematic, complete algorithm for symbolic reasoning, without any supervision and the reward signal from the environment.

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LSTM-Based Goal Recognition in Latent Space

Cited by 5 publications

References 0 publications

Recognition and interfere deceptive behavior based on inverse reinforcement learning and game theory

Recognition and interfere deceptive behavior based on inverse reinforcement learning and game theory

Towards Stable Symbol Grounding with Zero-Suppressed State AutoEncoder

Photo-Realistic Blocksworld Dataset

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