Time Window Temporal Logic

This paper introduces time window temporal logic (TWTL), a rich expressivity language for describing various time bounded specifications. In particular, the syntax and semantics of TWTL enable the compact representation of serial tasks, which are typically seen in robotics and control applications. This paper also discusses the relaxation of TWTL formulae with respect to deadlines of tasks. Efficient automata-based frameworks to solve synthesis, verification and learning problems are also presented. The key ingredient to the presented solution is an algorithm to translate a TWTL formula to an annotated finite state automaton that encodes all possible temporal relaxations of the specification. Case studies illustrating the expressivity of the logic and the proposed algorithms are included

Time window temporal logic

This paper introduces time window temporal logic (TWTL), a rich expressive language for describing various time bounded specifications. In particular, the syntax and semantics of TWTL enable the compact representation of serial tasks, which are prevalent in various applications including robotics, sensor systems, and manufacturing systems. This paper also discusses the relaxation of TWTL formulae with respect to the deadlines of the tasks. Efficient automata-based frameworks are presented to solve synthesis, verification and learning problems. The key ingredient to the presented solution is an algorithm to translate a TWTL formula to an annotated finite state automaton that encodes all possible temporal relaxations of the given formula. Some case studies are presented to illustrate the expressivity of the logic and the proposed algorithms

A Message Passing Strategy for Decentralized Connectivity Maintenance in Agent Removal

In a multi-agent system, agents coordinate to achieve global tasks through local communications. Coordination usually requires sufficient information flow, which is usually depicted by the connectivity of the communication network. In a networked system, removal of some agents may cause a disconnection. In order to maintain connectivity in agent removal, one can design a robust network topology that tolerates a finite number of agent losses, and/or develop a control strategy that recovers connectivity. This paper proposes a decentralized control scheme based on a sequence of replacements, each of which occurs between an agent and one of its immediate neighbors. The replacements always end with an agent, whose relocation does not cause a disconnection. We show that such an agent can be reached by a local rule utilizing only some local information available in agents' immediate neighborhoods. As such, the proposed message passing strategy guarantees the connectivity maintenance in arbitrary agent removal. Furthermore, we significantly improve the optimality of the proposed scheme by incorporating $\delta$-criticality (i.e. the criticality of an agent in its $\delta$-neighborhood).Comment: 9 pages, 9 figure

Robust satisfaction of temporal logic specifications via reinforcement learning

We consider the problem of steering a system with unknown, stochastic dynamics to satisfy a rich, temporally-layered task given as a signal temporal logic formula. We represent the system as a finite-memory Markov decision process with unknown transition probabilities and whose states are built from a partition of the state space. We present provably convergent reinforcement learning algorithms to maximize the probability of satisfying a given specification and to maximize the average expected robustness, i.e. a measure of how strongly the formula is satisfied. Robustness allows us to quantify progress towards satisfying a given specification. We demonstrate via a pair of robot navigation simulation case studies that, due to the quantification of progress towards satisfaction, reinforcement learning with robustness maximization performs better than probability maximization in terms of both probability of satisfaction and expected robustness with a low number of training examples

Q-learning for robust satisfaction of signal temporal logic specifications

This paper addresses the problem of learning optimal policies for satisfying signal temporal logic (STL) specifications by agents with unknown stochastic dynamics. The system is modeled as a Markov decision process, in which the states represent partitions of a continuous space and the transition probabilities are unknown. We formulate two synthesis problems where the desired STL specification is enforced by maximizing the probability of satisfaction, and the expected robustness degree, that is, a measure quantifying the quality of satisfaction. We discuss that Q-learning is not directly applicable to these problems because, based on the quantitative semantics of STL, the probability of satisfaction and expected robustness degree are not in the standard objective form of Q-learning. To resolve this issue, we propose an approximation of STL synthesis problems that can be solved via Q-learning, and we derive some performance bounds for the policies obtained by the approximate approach. The performance of the proposed method is demonstrated via simulations

Robust Satisfaction of Temporal Logic Specifications via Reinforcement Learning

We consider the problem of steering a system with unknown, stochastic dynamics to satisfy a rich, temporally layered task given as a signal temporal logic formula. We represent the system as a Markov decision process in which the states are built from a partition of the state space and the transition probabilities are unknown. We present provably convergent reinforcement learning algorithms to maximize the probability of satisfying a given formula and to maximize the average expected robustness, i.e., a measure of how strongly the formula is satisfied. We demonstrate via a pair of robot navigation simulation case studies that reinforcement learning with robustness maximization performs better than probability maximization in terms of both probability of satisfaction and expected robustness.Comment: 8 pages, 4 figure

II. Ulusal Biyoloji Eğitimi Kongresi Bildiri Özetleri Kitabı 03-05 Temmuz 2018 Aksaray Üniversitesi

II. Ulusal Biyoloji Eğitimi Kongresi’ni (UBEK 2018) 03–05 Temmuz 2018 tarihlerinde “Aksaray Üniversitesi Eğitim Fakültesi”, “Millî Eğitim Bakanlığı Öğretmen Yetiştirme ve Geliştirme Genel Müdürlüğü” ve “Fen Eğitimi ve Araştırmaları Derneği (FEAD)” işbirliğiyle Aksaray Üniversitesi’nde gerçekleştirmekten mutluluk duyuyoruz. Kongrede, Biyoloji Eğitimi alanında çalışan akademisyenler, lisansüstü öğrenciler ve öğretmenlerin (biyoloji, fen, okul öncesi, sınıf öğretmenleri ve öğretmen adayları) tanışması ve etkileşimi amaçlanmaktadır. Kongre süresince sunulan sözlü-poster bildiriler ve çalıştaylar ile ülkemizdeki biyoloji eğitimi çalışmalarının değerlendirilebileceği bilimsel ve akademik bir ortamın oluşturulması hedeflenmektedir. Kongre kapsamında, 80 adet sözlü bildiri, 2 adet poster bildiri sunulması ve 5 adet çalıştay gerçekleştirilmesi planlanmaktadır. Bilimsel çalışmaların yanında kongrenin son günü sosyal etkinlik kapsamında Aksaray’da yer alan tarihi ve kültürel mekânların (Aksaray Müzesi, Ulu Cami, Somuncubaba Türbesi, Selime Katedrali) ziyaret edilmesi planlanmıştır. Ardından, Ihlara Vadisi Gezisi kapsamında mikroklima iklime sahip, fauna-flora ve kültürel peyzaj değerleri açısından çok zengin olan Ihlara Vadisi’nde yürüyüş gerçekleştirilecektir. Kongrenin gerçekleştirilmesine destek olan Milli Eğitim Bakanlığı’na, Aksaray Valiliği’ne, Aksaray Belediyesi’ne, Aksaray Üniversitesi’ne, Fen Eğitimi ve Araştırmaları Derneği’ne, PEGEM Akademi’ye, kongrede görev alan tüm kurullara ve değerli çalışmalarını bizimle paylaşan tüm katılımcılara teşekkür ederim. Aksaray’da sizi ağırlamaktan mutluluk duyacağız. Biyoloji eğitimi alanında çalışan akademisyenler ile birlikte öğretmenlerimize ve lisansüstü/lisans öğrencilerine faydalı bir kongre olmasını diler, sevgi ve saygılarımı sunarım