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Stochastic Processes And Filtering Theory

Autonomous AI systems need complex computational techniques for planning and performing actions. Planning and acting require significant deliberation because an intelligent system must coordinate and integrate these activities in order to act effectively in the real world. This book presents a comprehensive paradigm of planning and acting using the most recent and advanced automated-planning techniques. It explains the computational deliberation capabilities that allow an actor, whether physical or virtual, to reason about its actions, choose them, organize them purposefully, and act deliberately to achieve an objective. Useful for students, practitioners, and researchers, this book covers state-of-the-art planning techniques, acting techniques, and their integration which will allow readers to design intelligent systems that are able to act effectively in the real world.

Automated Planning and Acting

https://hal.archives-ouvertes.fr/hal-01137921/document

Deliberation for autonomous robots: A survey

Multi-object detection and multi-object-tracking in diverse driving situations is the main challenge in autonomous vehicles. Vehicle manufacturers and research organizations are addressing this problem, with multiple sensors such as camera, LiDAR, RADAR, ultrasonic-sensors, GPS, and Vehicle-to-Everything-technology. Deep Neural Networks (DNN) are playing a predominant role to solve this. Fusing the sensing modalities with DNN will be the leading solution to this challenge. This paper evaluates the state-of-the-art techniques that address this challenge, with three primary sensors camera, LiDAR, and RADAR with DNN, and fusion of sensor data with DNN. The analysis shows that there exists an excellent potential to design a more optimized solution to address this challenge. This work proposes a perception model for autonomous vehicles.

Multi-Object Detection and Tracking, Based on DNN, for Autonomous Vehicles: A Review

This paper gives an overview of the research papers published in Symbol Grounding in the period from the beginning of the 21st century up 2012. The focus is in the use of symbol grounding for robotics and intelligent system. The review covers a number of subtopics, that include, physical symbol grounding, social symbol grounding, symbol grounding for vision systems, anchoring in robotic systems, and learning symbol grounding in software systems and robotics. This review is published in conjunction with a special issue on Symbol Grounding in the Kunstliche Intelligenz Journal.

/pdf/a-short-review-of-symbol-grounding-in-robotic-and-3a0bvpne4h.pdf

A Short Review of Symbol Grounding in Robotic and Intelligent Systems

The difficulties encountered in sequential decision-making problems under uncertainty are often linked to the large size of the state space. Exploiting the structure of the problem, for example by employing a factored representation, is usually an efficient approach but, in the case of partially observable Markov decision processes, the fact that some state variables may be visible has not been sufficiently appreciated. In this article, we present a complementary analysis and discussion about MOMDPs, a formalism that exploits the fact that the state space may be factored in one visible part and one hidden part. Starting from a POMDP description, we dig into the structure of the belief update, value function, and the consequences in value iteration, specifically how classical algorithms can be adapted to this factorization, and demonstrate the resulting benefits through an empirical evaluation.

/pdf/a-closer-look-at-momdps-1jxnq8cfvf.pdf

A Closer Look at MOMDPs

Many state-of-the-art algorithms for solving Partially Observable Markov Decision Processes (POMDPs) rely on turning the problem into a “fully observable” problem—a belief MDP—and exploiting the piece-wise linearity and convexity (PWLC) of the optimal value function in this new state space (the belief simplex ∆) This approach has been extended to solving ρ-POMDPs—ie, for information-oriented criteria—when the reward ρ is convex in ∆ General ρ-POMDPs can also be turned into “fully observable” problems, but with no means to exploit the PWLC property In this paper, we focus on POMDPs and ρ-POMDPs with λ ρ -Lipschitz reward function, and demonstrate that, for finite horizons, the optimal value function is Lipschitz-continuous Then, value function approximators are proposed for both upper- and lower-bounding the optimal value function, which are shown to provide uniformly improvable bounds This allows proposing two algorithms derived from HSVI which are empirically evaluated on various benchmark problems

/pdf/rho-pomdps-have-lipschitz-continuous-epsilon-optimal-value-1ojdcvx2dg.pdf

rho-POMDPs have Lipschitz-Continuous epsilon-Optimal Value Functions

We consider the active learning problem of inferring the transition model of a Markov Decision Process by acting and observing transitions. This is particularly useful when no reward function is a priori defined. Our proposal is to cast the active learning task as a utility maximization problem using Bayesian reinforcement learning with belief-dependent rewards. After presenting three possible performance criteria, we derive from them the belief-dependent rewards to be used in the decision-making process. As computing the optimal Bayesian value function is intractable for large horizons, we use a simple algorithm to approximately solve this optimization problem. Despite the sub-optimality of this technique, we show experimentally that our proposal is efficient in a number of domains.

/pdf/active-learning-of-mdp-models-3ldvpxsn38.pdf

Active learning of MDP models

Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI

The localization of a mobile vehicle in an dynamic outdoor environment is a very hard task that requires robust data processing and interpretation. This paper proposes a localization device, based on both image processing and stochastic evaluation. As far it is concerned the data processing task is performed by a vision device that uses a principal component analysis in order to find out the most probable position of the mobile vehicle considering the image acquired. This vision device works in parallel with a stochastic position evaluation that uses Partially Observable Markov Decision Process where the observation probability is conditioned by the results of the vision device. The method developed in this paper seems to give very good results compared with the standard methods and particularly the neighborhoods based ones.

Robot Localization by Stochastic Vision Based Device

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