Category Archives: Artificial Intelligence

Model-based (on ordinary differential equations) and partially model-free Policy Iteration on continuous space and time

July 7, 2023 11:28 ,

Jaeyoung Lee, Richard S. Sutton, Policy iterations for reinforcement learning problems in continuous time and space — Fundamental theory and methods, . Automatica, Volume 126, 2021 DOI: 10.1016/j.automatica.2020.109421.

Policy iteration (PI) is a recursive process of policy evaluation and improvement for solving an optimal decision-making/control problem, or in other words, a reinforcement learning (RL) problem. PI has also served as the fundamental for developing RL methods. In this paper, we propose two PI methods, called differential PI (DPI) and integral PI (IPI), and their variants, for a general RL framework in continuous time and space (CTS), where the environment is modeled by a system of ordinary differential equations (ODEs). The proposed methods inherit the current ideas of PI in classical RL and optimal control and theoretically support the existing RL algorithms in CTS: TD-learning and value-gradient-based (VGB) greedy policy update. We also provide case studies including (1) discounted RL and (2) optimal control tasks. Fundamental mathematical properties – admissibility, uniqueness of the solution to the Bellman equation (BE), monotone improvement, convergence, and optimality of the solution to the Hamilton–Jacobi–Bellman equation (HJBE) – are all investigated in-depth and improved from the existing theory, along with the general and case studies. Finally, the proposed ones are simulated with an inverted-pendulum model and their model-based and partially model-free implementations to support the theory and further investigate them beyond.

Posted in: Reinforcement learning in AI , Tagged: ,

Formalization of “making sense” of sensory perceptions and use in several practical cases that compare favourably, because of the use of induction, to neural network approaches

July 6, 2023 11:09 ,

Richard Evans, José Hernández-Orallo, Johannes Welbl, Pushmeet Kohli, Marek Sergot, Making sense of sensory input, . Artificial Intelligence, Volume 293, 2021 DOI: 10.1016/j.artint.2020.103438.

This paper attempts to answer a central question in unsupervised learning: what does it mean to “make sense” of a sensory sequence? In our formalization, making sense involves constructing a symbolic causal theory that both explains the sensory sequence and also satisfies a set of unity conditions. The unity conditions insist that the constituents of the causal theory – objects, properties, and laws – must be integrated into a coherent whole. On our account, making sense of sensory input is a type of program synthesis, but it is unsupervised program synthesis. Our second contribution is a computer implementation, the Apperception Engine, that was designed to satisfy the above requirements. Our system is able to produce interpretable human-readable causal theories from very small amounts of data, because of the strong inductive bias provided by the unity conditions. A causal theory produced by our system is able to predict future sensor readings, as well as retrodict earlier readings, and impute (fill in the blanks of) missing sensory readings, in any combination. In fact, it is able to do all three tasks simultaneously. We tested the engine in a diverse variety of domains, including cellular automata, rhythms and simple nursery tunes, multi-modal binding problems, occlusion tasks, and sequence induction intelligence tests. In each domain, we test our engine’s ability to predict future sensor values, retrodict earlier sensor values, and impute missing sensory data. The Apperception Engine performs well in all these domains, significantly out-performing neural net baselines. We note in particular that in the sequence induction intelligence tests, our system achieved human-level performance. This is notable because our system is not a bespoke system designed specifically to solve intelligence tests, but a general-purpose system that was designed to make sense of any sensory sequence.

Continuation paper: https://doi.org/10.1016/j.artint.2021.103521

Notes:

  • Use HMMs with the states being sets of atomic propositions and the transition function logical predicates, therefore mixing a non-symbolic framework (HMM) with a completely symbolic one.
  • Assume perceptions to be previously discretized and modelled as grounded atoms.
  • Need to be provided with both the sensory (discretized) input and commonsense knowledge about the predicates used for making sense.
  • Include a very clear and simple representation of deduction, induction and abduction (Fig. 1).
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Classification with decision trees based on POMDPs

June 30, 2023 12:25 ,

Shlomi Maliah, Guy Shani, Using POMDPs for learning cost sensitive decision trees, . Artificial Intelligence, Volume 292, 2021 DOI: 10.1016/j.artint.2020.103400.

In classification, an algorithm learns to classify a given instance based on a set of observed attribute values. In many real world cases testing the value of an attribute incurs a cost. Furthermore, there can also be a cost associated with the misclassification of an instance. Cost sensitive classification attempts to minimize the expected cost of classification, by deciding after each observed attribute value, which attribute to measure next. In this paper we suggest Partially Observable Markov Decision Processes (POMDPs) as a modeling tool for cost sensitive classification. POMDPs are typically solved through a policy over belief states. We show how a relatively small set of potentially important belief states can be identified, and define an MDP over these belief states. To identify these potentially important belief states, we construct standard decision trees over all attribute subsets, and the leaves of these trees become the state space of our tree-based MDP. At each phase we decide on the next attribute to measure, balancing the cost of the measurement and the classification accuracy. We compare our approach to a set of previous approaches, showing our approach to work better for a range of misclassification costs.

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A new clustering algorithm based on swarm intelligence that is alleged to require no parameterization

June 29, 2023 10:12 ,

Michael C. Thrun, Alfred Ultsch, Swarm intelligence for self-organized clustering, . Artificial Intelligence, Volume 290, 2021, DOI: 10.1016/j.artint.2020.103237.

Algorithms implementing populations of agents which interact with one another and sense their environment may exhibit emergent behavior such as self-organization and swarm intelligence. Here a swarm system, called Databionic swarm (DBS), is introduced which is able to adapt itself to structures of high-dimensional data characterized by distance and/or density-based structures in the data space. By exploiting the interrelations of swarm intelligence, self-organization and emergence, DBS serves as an alternative approach to the optimization of a global objective function in the task of clustering. The swarm omits the usage of a global objective function and is parameter-free because it searches for the Nash equilibrium during its annealing process. To our knowledge, DBS is the first swarm combining these approaches. Its clustering can outperform common clustering methods such as K-means, PAM, single linkage, spectral clustering, model-based clustering, and Ward, if no prior knowledge about the data is available. A central problem in clustering is the correct estimation of the number of clusters. This is addressed by a DBS visualization called topographic map which allows assessing the number of clusters. It is known that all clustering algorithms construct clusters, irrespective of the data set contains clusters or not. In contrast to most other clustering algorithms, the topographic map identifies, that clustering of the data is meaningless if the data contains no (natural) clusters. The performance of DBS is demonstrated on a set of benchmark data, which are constructed to pose difficult clustering problems and in two real-world applications.

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Finding the policy that generalizes the best in a sample of possible real scenarios by leveraging PAC-Bayes

June 28, 2023 15:45 ,

Majumdar A, Farid A, Sonar A., PAC-Bayes control: learning policies that provably generalize to novel environments. The International Journal of Robotics Research. 2021;40(2-3):574-593 DOI: 10.1177/0278364920959444.

Our goal is to learn control policies for robots that provably generalize well to novel environments given a dataset of example environments. The key technical idea behind our approach is to leverage tools from generalization theory in machine learning by exploiting a precise analogy (which we present in the form of a reduction) between generalization of control policies to novel environments and generalization of hypotheses in the supervised learning setting. In particular, we utilize the probably approximately correct (PAC)-Bayes framework, which allows us to obtain upper bounds that hold with high probability on the expected cost of (stochastic) control policies across novel environments. We propose policy learning algorithms that explicitly seek to minimize this upper bound. The corresponding optimization problem can be solved using convex optimization (relative entropy programming in particular) in the setting where we are optimizing over a finite policy space. In the more general setting of continuously parameterized policies (e.g., neural network policies), we minimize this upper bound using stochastic gradient descent. We present simulated results of our approach applied to learning (1) reactive obstacle avoidance policies and (2) neural network-based grasping policies. We also present hardware results for the Parrot Swing drone navigating through different obstacle environments. Our examples demonstrate the potential of our approach to provide strong generalization guarantees for robotic systems with continuous state and action spaces, complicated (e.g., nonlinear) dynamics, rich sensory inputs (e.g., depth images), and neural network-based policies.

Posted in: Reinforcement learning in AI , Tagged:

Extracting video summaries from RL processes to explain and understand them

June 28, 2023 15:40 ,

Pedro Sequeira, Melinda Gervasio, Interestingness elements for explainable reinforcement learning: Understanding agents’ capabilities and limitations. Artificial Intelligence, Volume 288, 2020 DOI: 10.1016/j.artint.2020.103367.

We propose an explainable reinforcement learning (XRL) framework that analyzes an agent’s history of interaction with the environment to extract interestingness elements that help explain its behavior. The framework relies on data readily available from standard RL algorithms, augmented with data that can easily be collected by the agent while learning. We describe how to create visual summaries of an agent’s behavior in the form of short video-clips highlighting key interaction moments, based on the proposed elements. We also report on a user study where we evaluated the ability of humans to correctly perceive the aptitude of agents with different characteristics, including their capabilities and limitations, given visual summaries automatically generated by our framework. The results show that the diversity of aspects captured by the different interestingness elements is crucial to help humans correctly understand an agent’s strengths and limitations in performing a task, and determine when it might need adjustments to improve its performance.

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Expressing POMDPs policies through Knowledge-Based programs

June 28, 2023 15:37 ,

Bruno Zanuttini, Jérôme Lang, Abdallah Saffidine, François Schwarzentruber Knowledge-based programs as succinct policies for partially observable domains. Artificial Intelligence, Volume 288, 2020 DOI: 10.1016/j.artint.2020.103365.

We suggest to express policies for contingent planning by knowledge-based programs (KBPs). KBPs, introduced by Fagin et al. (1995) [32], are high-level protocols describing the actions that the agent should perform as a function of their current knowledge: branching conditions are epistemic formulas that are interpretable by the agent. The main aim of our paper is to show that KBPs can be seen as a succinct language for expressing policies in single-agent contingent planning. KBP are conceptually very close to languages used for expressing policies in the partially observable planning literature: like them, they have conditional and looping structures, with actions as atomic programs and Boolean formulas on beliefs for choosing the execution path. Now, the specificity of KBPs is that branching conditions refer to the belief state and not to the observations. Because of their structural proximity, KBPs and standard languages for representing policies have the same power of expressivity: every standard policy can be expressed as a KBP, and every KBP can be “unfolded” into a standard policy. However, KBPs are more succinct, more readable, and more explainable than standard policies. On the other hand, they require more online computation time, but we show that this is an unavoidable tradeoff. We study knowledge-based programs along four criteria: expressivity, succinctness, complexity of online execution, and complexity of verification.

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A new theory: we are curious about tasks that increase our ability to solve as many future tasks as possible

June 26, 2023 12:12 ,

Franziska Brändle, Charley M. Wu, Eric Schulz, What Are We Curious about?, . Trends in Cognitive Sciences, Volume 24, Issue 9, 2020 DOI: 10.1016/j.tics.2020.05.010.

(no abstract).

Posted in: Psycho-physiological bases of engineering, Reinforcement learning in AI , Tagged:

Synthesizing a supervisor (a Finite State Machine) instead of finding a standard policy in MDPs, applied to multi-agent systems

September 16, 2019 15:23 ,

B. Wu, X. Zhang and H. Lin Permissive Supervisor Synthesis for Markov Decision Processes Through Learning. IEEE Transactions on Automatic Control, vol. 64, no. 8, pp. 3332-3338, Aug. 2019. DOI: 10.1109/TAC.2018.2879505.

This paper considers the permissive supervisor synthesis for probabilistic systems modeled as Markov Decision Processes (MDP). Such systems are prevalent in power grids, transportation networks, communication networks, and robotics. We propose a novel supervisor synthesis framework using automata learning and compositional model checking to generate the permissive local supervisors in a distributed manner. With the recent advances in assume-guarantee reasoning verification for MDPs, constructing the composed system can be avoided to alleviate the state space explosion. Our framework learns the supervisors iteratively using counterexamples from the verification and is guaranteed to terminate in finite steps and to be correct.

Posted in: Reinforcement learning in AI , Tagged: ,

A Survey of Knowledge Representation in Service Robotics

June 17, 2019 12:14 ,

avid Paulius, Yu Sun, A Survey of Knowledge Representation in Service Robotics,Robotics and Autonomous Systems, Volume 118, 2019, Pages 13-30 DOI: 10.1016/j.robot.2019.03.005.

Within the realm of service robotics, researchers have placed a great amount of effort into learning, understanding, and representing motions as manipulations for task execution by robots. The task of robot learning and problem-solving is very broad, as it integrates a variety of tasks such as object detection, activity recognition, task/motion planning, localization, knowledge representation and retrieval, and the intertwining of perception/vision and machine learning techniques. In this paper, we solely focus on knowledge representations and notably how knowledge is typically gathered, represented, and reproduced to solve problems as done by researchers in the past decades. In accordance with the definition of knowledge representations, we discuss the key distinction between such representations and useful learning models that have extensively been introduced and studied in recent years, such as machine learning, deep learning, probabilistic modeling, and semantic graphical structures. Along with an overview of such tools, we discuss the problems which have existed in robot learning and how they have been built and used as solutions, technologies or developments (if any) which have contributed to solving them. Finally, we discuss key principles that should be considered when designing an effective knowledge representation.

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