Logical Task Research Articles

“Object permanence” test for rats. First experiments

The description of the developed experimental set, permitting to evaluate rats solution of the “object permanence” test (be J. Piaget), which reflects animal capacity to operate one of the most simple empirical rules (by Krushinsky), i.e. capacity to solve elementary logic task. An animal, being placed in the brightly lit part of the box, is eager to run into the dark one via underpass, although after the first attempts the underpass was hidden by several means, and now an animal can solve the task overcoming the obstacle in case it operates the object permanence rule. The test solution does not require the previous learning, and it is similar to the test used in mice experiments. The test was applied in experiments with rats of five genetic groups – outbred Wistar and Long-Evans rats, rats of inbred audiogenic-prone Krushinsky – Molodkina (KM) strain, the strain derived from KM rats, selected for audiogenic non-proneness and inbred SHR strain with spontaneous arterial hypertension. The preliminary experiments (performed with an animal during one experimental day) revealed interstrain differences with the test solution prevalence in SHR strain. This test could be used in pharmacological and neurophysiological experiments with rats.

ExaGRyPE: Numerical general relativity solvers based upon the hyperbolic PDEs solver engine ExaHyPE

ExaGRyPE describes a suite of solvers and solver ingredients for numerical relativity that are based upon ExaHyPE 2, the second generation of our Exascale Hyperbolic PDE Engine. Numerical relativity simulations are crucial in resolving astrophysical phenomena in strong gravitational fields and are fundamental in analyzing and understanding gravitational wave emissions. The presented generation of ExaGRyPE solves the Einstein field equations in the standard CCZ4 formulation under a 3+1 foliation and focuses on black hole space-times. It employs a block-structured Cartesian grid carrying a higher-order Finite Difference scheme with full support of adaptive mesh refinement (AMR), it facilitates massive parallelism combining message passing, domain decomposition and task parallelism, and it supports the injection of particles into the grid as static data probes or as moving tracers. We introduce the ExaGRyPE-specific building blocks within ExaHyPE 2, and discuss its software architecture and compute-n-feel.For this, we formalize the creation of any specific astrophysical simulation with ExaGRyPE as a sequence of lowering operations, where a few abstract logical tasks are successively broken down into finer and finer tasks until we obtain an abstraction level which can directly be mapped onto a C++ executable. The overall program logic is fully specified via a domain-specific Python interface, we automatically map this logic onto a more detailed set of numerical tasks, subsequently lower this representation onto technical tasks that the underlying ExaHyPE engine uses to parallelize the application, before eventually the technical tasks in turn are mapped onto small task graphs including the actual astrophysical PDE term evaluations, initial conditions, boundary conditions, and so forth. These can be injected manually by the user, or users might instruct the solver on the most abstract user interface level to use out-of-the-box ExaGRyPE implementations. We end up with a rigorous separation of concerns which shields ExaGRyPE users from technical details and hence simplifies the development of novel physical models. We present the simulations and data for the gauge wave, static single black holes and rotating binary black hole systems, demonstrating that the code base is mature and usable. However, we also uncover domain-specific numerical challenges that need further study by the community in future work.

The interstrain differences in laboratory mouse cognitive abilities (elementary logic task, attention, memory)

Mice of two strains, selected, respectively, for successful solution of puzzle-box test (addressed to “object permanence” rule operation) and for non-solution of this test, were tested for short term memory, attention to moving object and neophagia. The data obtained demonstrated, that mice, selected for successful “object permanence” test solution demonstrated higher scores in recent memory and attention indices. It was suggested, that interstrain differences discovered should be addressed to differences in the “executive functions” expression.

Demonstrative Learning for Human-Agent Knowledge Transfer

Demonstrative learning in virtual reality (VR) is a pivotal learning strategy for knowledge transfer for embodied agents. While existing studies have extensively explored agents’ knowledge transfer through self-demonstrative learning (SDL) or teacher-demonstrative learning (TDL), there has been limited focus on a system that integrates both paradigms. This paper proposes a comprehensive system that combines the SDL paradigm with the TDL paradigm in VR from a top-down perspective. The system involves using directed probabilistic graphs (DPGs) for knowledge representation, constructing detectors and actuators based on object fluents and atomic actions, representing knowledge acquired from both learning paradigms on the DPGs, and incorporating knowledge integration and visualization. Through system evaluation, we show the advantages of integrating two demonstrative learning paradigms, including increased learning efficiency, mitigating demonstrator's deficiencies, and more logical task execution. The study also reveals that a dynamically decreasing fusion factor with the learning progresses, performs well across different correct percentages of teacher demonstrations. Additionally, we show a more decentralized demonstration in the middle of the agent's learning progress maximizing learning efficiency when demonstrating only a few atomic actions. Finally, we assess the system's generalizability and delineate its boundaries. With the ongoing development and the increasing availability of human data in VR, we anticipate that our system can be applied to future scenarios of efficient human-agent knowledge transfer in human-agent symbiosis.

Real-time reactive task allocation and planning of large heterogeneous multi-robot systems with temporal logic specifications

Existing methods for the task allocation and planning (TAP) of multi-robot systems with temporal logic specifications mainly rely on optimization-based approaches or graph search techniques applied to the product automaton. However, these methods suffer from high computational cost and scale poorly with the number of robots and the complexity of temporal logic tasks, thus limiting the applicability in real-time implementation, especially for large multi-robot systems. To address these challenges, this work develops a novel TAP framework that can solve reactive temporal logic planning problems for large-scale heterogeneous multi-robot systems (HMRS) in real time. Specifically, we develop a planning decision tree (PDT) to represent the task progression and task allocation specialized for HMRS with temporal logic specifications. Based on the PDT, we develop two key search algorithms—the planning decision tree search (PDTS) and the interactive planning decision tree search (IPDTS)—where PDTS generates an offline plan which will be modified online by PDTS and IPDTS jointly to enable fast reactive planning if environmental changes or temporary tasks occur. Such a design can generate satisfying plan for HMRS with multiple orders of magnitude more robots than those that existing methods can manipulate. Rigorous analysis shows that the PDT-based planning is feasible (i.e., the generated plan is applicable) and complete (i.e., a feasible plan, if exits, is guaranteed to be found). The algorithm complexity further indicates that the solution time is only linearly proportional to the robot numbers and types. Simulation and experiment results demonstrate that reactive plan can be generated for large HMRS in real-time, which outperforms the state-of-the-art methods.

Scaling Learning based Policy Optimization for Temporal Logic Tasks by Controller Network Dropout

This paper introduces a model-based approach for training feedback controllers for an autonomous agent operating in a highly nonlinear (albeit deterministic) environment. We desire the trained policy to ensure that the agent satisfies specific task objectives and safety constraints, both expressed in Discrete-Time Signal Temporal Logic (DT-STL). One advantage for reformulation of a task via formal frameworks, like DT-STL, is that it permits quantitative satisfaction semantics. In other words, given a trajectory and a DT-STL formula, we can compute the robustness , which can be interpreted as an approximate signed distance between the trajectory and the set of trajectories satisfying the formula. We utilize feedback control, and we assume a feed forward neural network for learning the feedback controller. We show how this learning problem is similar to training recurrent neural networks (RNNs), where the number of recurrent units is proportional to the temporal horizon of the agent's task objectives. This poses a challenge: RNNs are susceptible to vanishing and exploding gradients, and naïve gradient descent-based strategies to solve long-horizon task objectives thus suffer from the same problems. To tackle this challenge, we introduce a novel gradient approximation algorithm based on the idea of dropout or gradient sampling. One of the main contributions is the notion of controller network dropout , where we approximate the NN controller in several time-steps in the task horizon by the control input obtained using the controller in a previous training step. We show that our control synthesis methodology, can be quite helpful for stochastic gradient descent to converge with less numerical issues, enabling scalable backpropagation over long time horizons and trajectories over high dimensional state spaces. We demonstrate the efficacy of our approach on various motion planning applications requiring complex spatio-temporal and sequential tasks ranging over thousands of time-steps.

Development and Validation of a Culturally Adapted, Event-related Potential Paradigm for Assessing Alcohol Cue Reactivity and Error Processing in Alcohol Dependence.

Research on event-related potentials (ERP) in addiction highlights the importance of cognitive ERP markers, such as P300 and error-related negativity (ERN), in distinguishing between alcohol-dependent patients and healthy controls. We aimed to develop and validate ERP paradigms utilizing culturally validated stimuli to evoke P300 and ERN for the Indian population. In a cross-sectional study, 16 alcohol-dependent patients and age-matched healthy controls were recruited. For P300, we designed a visual oddball (Go/No-Go) task using culturally appropriate alcohol and non-alcohol-related images. To study ERN, we used the Assessing Neurocognition via Gamified Experimental Logic (ANGEL) task, developed in India, integrating its "game" levels to capture error monitoring with fewer trials. A 32-channel EEG-ERP system was used for data acquisition. Participants showed high engagement in the visual Go/No-Go task, with 94.4% accuracy. The P300 difference wave showed significantly higher amplitudes (p = .01, Cohen's d = 0.97) and latencies (p < .001, Cohen's d = 1.62) in patients compared to controls at the Pz electrode. No significant group differences were found in accuracy or reaction times for frequent and rare stimuli. In the ANGEL task, patients had a significantly lower ERN difference wave amplitude at the Pz electrode than controls (p < .001, Cohen's d = 1.47), with no latency differences. Task accuracy was lower, and reaction time was longer in patients. The visual Go/No-Go task and the ANGEL task are promising tools for understanding the neurocognitive mechanisms underlying alcohol dependence in the Indian context.

Computationally Hard Problems for Logic Programs under Answer Set Semantics

Showing that a problem is hard for a model of computation is one of the most challenging tasks in theoretical computer science, logic and mathematics. For example, it remains beyond reach to find an explicit problem that cannot be computed by polynomial size propositional formulas (PF). As a model of computation, logic programs (LP) under answer set semantics are as expressive as PF, and also \(\mathtt{NP}\) -complete for satisfiability checking. In this paper, we show that the PAR problem is hard for LP, i.e., deciding whether a binary string contains an odd number of \(1\) ’s requires exponential size logic programs. The proof idea is first to transform logic programs into equivalent boolean circuits, and then apply a probabilistic method known as random restriction to obtain an exponential lower bound. Based on the main result, we generalize a sufficient condition for identifying hard problems for LP, and give a separation map for a logic program family from a computational point of view, whose members are all equally expressive and share the same reasoning complexity.

Multiple tasks control of nonlinear systems under signal temporal logic and its application to mobile robots

AbstractThis paper addresses the control problem of complex temporal logic tasks for nonlinear systems. A new control barrier function (CBF) called time‐varying extent‐compatible control barrier function (TV‐ECCBF) is proposed, which extends the traditional CBF by considering time‐varying extent or volumes. The main aim of TV‐ECCBF is to tackle the issue of avoiding obstacles in real‐world situations that have irregular or dynamically changing shapes, which cannot be efficiently resolved through traditional CBF methods. To encode complex task specifications, a control framework is presented based on signal temporal logic (STL). Additionally, we introduce robust semantics to discuss the satisfaction of target formulas. Moreover, the control framework generates controllers automatically based on different types of tasks, exhibiting global performance in achieving complex tasks. Finally, two examples of mobile robot systems are presented to demonstrate the effectiveness of the TV‐ECCBF and the proposed design framework.

ВИВЧЕННЯ ЛОГІКИ ЯК УМОВА РОЗВИТКУ КРИТИЧНОГО МИСЛЕННЯ У ЗДОБУВАЧІВ ВИЩОЇ ОСВІТИ

The purpose of the study is the process of critical thinking development based on the study of educational material on logic by students of higher education. The research methodology is based on modern educational and scientific approaches to the development of thinking, the development of critical thinking, active cognition and assessment of the environment, the ability to analyze business situations and problems, the development of professional skills for making current and strategic management decisions, etc. Scientific novelty: it has been proven that the condition for the development of critical thinking of students of higher education is the study of logic, solving problematic tasks. Conclusions. Classes in logic provide a wide range of necessary problem tasks for the development of critical thinking of students of higher education. Tasks in logic can involve both elementary calculations and tasks with complex calculations, both illustrative examples and sophisticated abstractions, long searches from the “general” and instant concentration of a large amount of information in the thesis, generally accepted stereotypes and the search for a solution to problem situations. The main condition for the development of critical thinking in logic classes should be the solving of a task constructed in a certain way by students. A logic task can be standard or non-standard, mathematical or not so much. The main thing is that a non-standard logical task should have some kind of surprise in it, should have an original character – whether it is an unusual condition of the task, an original idea, an unexpected solution. The ability to see the depth of the task, to distinguish the main from general details is important for solving this type of tasks. Logic contributes to sharpening the skills of rational analysis, critical evaluation of the information flow. Logic is a necessary tool for the development of a person’s critical thinking. Key words: critical thinking, logic, student of higher education, professional education, information.

The Past and Future of High Technology

Abstract This interview was given in 2008 by Arkady Zakrevsky (1928–2014), Corresponding Member of the National Academy of Sciences of Belarus (1972), Doctor of Technical Sciences (1967), and Professor (1969). He stood at the origins of the birth of cybernetics in the Soviet Union. He proposed the programming language for logical tasks LYaPAS, on the basis of which a series of computer-aided design systems for discrete devices was created, and methods for implementing parallel algorithms for the logical control of interacting processes. Some monographs: LYaPAS: A Programming Language for Logic and Coding Algorithms (N.-Y., L.: Academic Press, 1969; with M. A. Gavrilov); Boolesche Gleichungen: Theorie, Anwendung, Algorithmen (Berlin: VEB Verlag Technik, 1984; with Dieter Bochmann and Christian Posthoff); Combinatorial Algorithms of Discrete Mathematics (Tallinn: TUT Press, 2008; with Yu. Pottosin, L. Cheremisinova); Optimization in Boolean Space (Tallinn: TUT Press, 2009; with Yu. Pottosin, L. Cheremisinova); Design of Logical Control Devices (Tallinn: TUT Press, 2009; with Yu. Pottosin, L. Cheremisinova); Combinatorial Calculations in Many-Dimensional Boolean Space (Tallinn: TUT Press, 2012); Solving Large Systems Logical Equations (Tallinn: TUT Press, 2013).

Finite-step alternating approximately bi-similar symbolic model for Markov jump systems

Establishing a bi-similar abstraction (symbolic model) of control systems has been demonstrated as a viable approach for implementing temporal logic tasks on control systems. Previous research primarily focused on logic tasks over an infinite time horizon. However, practical scenarios often involve control tasks defined within finite time intervals. Hence, this paper addresses the finite-time bi-similar problem specifically for a significant class of stochastic switched systems known as Markov jump systems (MJSs). To achieve this, two novel concepts are introduced: finite-time stochastic incremental stability (FSIS) and finite-step alternating approximate bi-simulation relation. A sufficient condition is firstly established for FSIS of MJSs by leveraging the incremental weak infinitesimal operator. Subsequently, a symbolic model is developed for MJSs by employing gridding techniques. Furthermore, a sufficient condition is derived to ensure that the developed symbolic model exhibits finite-step alternating approximate bi-similarity with the original MJSs. Finally, two numerical examples illustrate the application of the proposed results.

Dynamic event‐triggered prescribed performance control for nonlinear systems with signal temporal logic

AbstractThis article is concerned with the problem of dynamic event‐triggered prescribed performance control for nonlinear systems under signal temporal logic tasks. By utilizing the method of prescribed performance control, the constrained plant can be transformed into an unconstrained one, and a dynamic event‐triggered feedback control law is generated for the transformed system to ensure that the signal temporal logic specification is satisfied. A dynamic event‐triggered mechanism is designed to guarantee the event‐triggered stability, safety and complex specification. Besides, Zeno phenomenon is definitely avoided. Compared with the continuous‐time feedback controller, the event‐triggered controller has proven to be effective in reducing sensing, communication and computation costs. Finally, two simulations are given to illustrate the effectiveness of theoretical results.

Planning and control of multi-robot-object systems under temporal logic tasks and uncertain dynamics

We develop an algorithm for the motion and task planning of a system composed of multiple robots and unactuated objects under tasks expressed as Linear Temporal Logic (LTL) constraints. The robots and objects evolve subject to uncertain dynamics in an obstacle-cluttered environment. The key part of the proposed solution is the intelligent construction of a coupled transition system that encodes the motion and tasks of the robots and the objects. We achieve such a construction by designing appropriate adaptive control protocols in the lower level, which guarantee the safe robot navigation/object transportation in the environment while compensating for the dynamic uncertainties. The transition system is efficiently interfaced with the temporal logic specification via a sampling-based algorithm to output a discrete path as a sequence of synchronized actions of the robots; such actions satisfy the robots’ as well as the objects’ specifications. The robots execute this discrete path by using the derived low level control protocol. Numerical experiments verify the proposed framework.

Funnel-Based Reward Shaping for Signal Temporal Logic Tasks in Reinforcement Learning

Funnel-Based Reward Shaping for Signal Temporal Logic Tasks in Reinforcement Learning

A Dual-Layer Network Deep Reinforcement Learning Algorithm for Multi-objective Signal Temporal Logic Tasks

A Dual-Layer Network Deep Reinforcement Learning Algorithm for Multi-objective Signal Temporal Logic Tasks

Fast and Adaptive Multi-Agent Planning under Collaborative Temporal Logic Tasks via Poset Products

Efficient coordination and planning is essential for large-scale multi-agent systems that collaborate in a shared dynamic environment. Heuristic search methods or learning-based approaches often lack the guarantee on correctness and performance. Moreover, when the collaborative tasks contain both spatial and temporal requirements, e.g., as linear temporal logic (LTL) formulas, formal methods provide a verifiable framework for task planning. However, since the planning complexity grows exponentially with the number of agents and the length of the task formula, existing studies are mostly limited to small artificial cases. To address this issue, a new planning paradigm is proposed in this work for system-wide temporal task formulas that are released online and continually. It avoids two common bottlenecks in the traditional methods, i.e., (a) the direct translation of the complete task formula to the associated Büchi automaton and (b) the synchronized product between the Büchi automaton and the transition models of all agents. Instead, an adaptive planning algorithm is proposed, which computes the product of relaxed partially ordered sets (R-posets) on-the-fly and assigns these subtasks to the agents subject to the ordering constraints. It is shown that the first valid plan can be derived with a polynomial time and memory complexity with respect to the system size and the formula length. Our method can take into account task formulas with a length of more than 400 and a fleet with more than 400 agents, while most existing methods fail at the formula length of 25 within a reasonable duration. The proposed method is validated on large fleets of service robots in both simulation and hardware experiments.

Neurosymbolic Motion and Task Planning for Linear Temporal Logic Tasks

Neurosymbolic Motion and Task Planning for Linear Temporal Logic Tasks

Automated workflow generation supporting the value stream design of reconfigurable robot assembly cells

The paper addresses the automated generation of value stream optimized assembly workflows for reconfigurable robot cells. The focus is on the generation of asynchronous concurrent action workflows from assembly task descriptions. The main motivation is to enable automated search for optimized solutions within the enormous design spaces of these systems which results from their structural and procedural configurability. The proposed approach conducts task assignment, task sequencing and logical task planning (AI Planning) for multiple agents. The approach combines existing methods from the distinct research fields of robot task sequencing and discrete task planning. The contribution of the research is twofold. Firstly, the paper proposes a generic domain meta model enabling automated workflow generation for multi-agent assembly tasks. Secondly, a workflow generation approach based on artificial intelligence planning combined with the game-theoretic concept of extensive form games is presented. The approach iteratively generates a cooperative strategy for acting agents which is subsequently optimized in terms of individual strategies. The obtained logical action plans are further transformed into parametric concurrent action schedules to support downstream design activities. Both the meta model and the workflow generation approach are applied in a case study and provide valuable information on achievable assembly speedup.

A Formal Control Framework of Autonomous Vehicle for Signal Temporal Logic Tasks and Obstacle Avoidance

This paper investigates the control problem of making an autonomous vehicle modeled as a nonlinear affine system, achieve both temporal logic tasks and obstacle avoidance. A new formal control framework based on the control barrier function (CBF) and potential field method is proposed, which aims at eliminating the singular points caused by the conflict between the target and the obstacle in traditional CBF methods. A feasibility detection mechanism algorithm is proposed to detect the task in terms of workspace topology and determines the structure of the framework. On the one hand, obstacle avoidance can always be fulfilled such that the safety of the system is guaranteed. On the other hand, satisfaction for the target formulas is discussed in terms of obstacles, workspace topology, and formula preference. The proposed control framework generates controllers based on different feasibility types of tasks automatically, which shows global performance in both safety guarantee and task achievement. Finally, the effectiveness of the overall framework is illustrated by two examples.