The Experts below are selected from a list of 339 Experts worldwide ranked by ideXlab platform
Calin Belta - One of the best experts on this subject based on the ideXlab platform.
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temporal logic motion planning and control with probabilistic satisfaction guarantees
IEEE Transactions on Robotics, 2012Co-Authors: Morteza Lahijanian, S B Andersson, Calin BeltaAbstract:We describe a Computational framework for automatic deployment of a robot with sensor and actuator noise from a temporal logic specification over a set of properties that are satisfied by the regions of a partitioned environment. We model the motion of the robot in the environment as a Markov decision process (MDP) and translate the motion specification to a formula of probabilistic Computation Tree logic (PCTL). As a result, the robot control problem is mapped to that of generating an MDP control policy from a PCTL formula. We present algorithms for the synthesis of such policies for different classes of PCTL formulas. We illustrate our method with simulation and experimental results.
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Motion planning and control from temporal logic specifications with probabilistic satisfaction guarantees
Proceedings - IEEE International Conference on Robotics and Automation, 2010Co-Authors: Morteza Lahijanian, Jerzy Wasniewski, S B Andersson, Calin BeltaAbstract:We present a Computational framework for automatic deployment of a robot from a temporal logic specification over a set of properties of interest satisfied at the regions of a partitioned environment. We assume that, during the motion of the robot in the environment, the current region can be precisely determined, while due to sensor and actuation noise, the outcome of a control action can only be predicted probabilistically. Under these assumptions, the deployment problem translates to generating a control strategy for a Markov Decision Process (MDP) from a temporal logic formula.We propose an algorithm inspired from probabilistic Computation Tree Logic (PCTL) model checking to find a control strategy that maximizes the probability of satisfying the specification. We illustrate our method with simulation and experimental results.
Morteza Lahijanian - One of the best experts on this subject based on the ideXlab platform.
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temporal logic motion planning and control with probabilistic satisfaction guarantees
IEEE Transactions on Robotics, 2012Co-Authors: Morteza Lahijanian, S B Andersson, Calin BeltaAbstract:We describe a Computational framework for automatic deployment of a robot with sensor and actuator noise from a temporal logic specification over a set of properties that are satisfied by the regions of a partitioned environment. We model the motion of the robot in the environment as a Markov decision process (MDP) and translate the motion specification to a formula of probabilistic Computation Tree logic (PCTL). As a result, the robot control problem is mapped to that of generating an MDP control policy from a PCTL formula. We present algorithms for the synthesis of such policies for different classes of PCTL formulas. We illustrate our method with simulation and experimental results.
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Motion planning and control from temporal logic specifications with probabilistic satisfaction guarantees
Proceedings - IEEE International Conference on Robotics and Automation, 2010Co-Authors: Morteza Lahijanian, Jerzy Wasniewski, S B Andersson, Calin BeltaAbstract:We present a Computational framework for automatic deployment of a robot from a temporal logic specification over a set of properties of interest satisfied at the regions of a partitioned environment. We assume that, during the motion of the robot in the environment, the current region can be precisely determined, while due to sensor and actuation noise, the outcome of a control action can only be predicted probabilistically. Under these assumptions, the deployment problem translates to generating a control strategy for a Markov Decision Process (MDP) from a temporal logic formula.We propose an algorithm inspired from probabilistic Computation Tree Logic (PCTL) model checking to find a control strategy that maximizes the probability of satisfying the specification. We illustrate our method with simulation and experimental results.
Norihiro Kamide - One of the best experts on this subject based on the ideXlab platform.
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paraconsistent Computation Tree logic
New Generation Computing, 2011Co-Authors: Ken Kaneiwa, Norihiro KamideAbstract:It is known that paraconsistent logical systems are more appropriate for inconsistency-tolerant and uncertainty reasoning than other types of logical systems. In this paper, a paraconsistent Computation Tree logic, PCTL, is obtained by adding paraconsistent negation to the standard Computation Tree logic CTL. PCTL can be used to appropriately formalize inconsistency-tolerant temporal reasoning. A theorem for embedding PCTL into CTL is proved. The validity, satisfiability, and model-checking problems of PCTL are shown to be decidable. The embedding and decidability results indicate that we can reuse the existing CTL-based algorithms for validity, satisfiability, and model-checking. An illustrative example of medical reasoning involving the use of PCTL is presented.
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Conceptual modeling in full Computation-Tree logic with sequence modal operator
International Journal of Intelligent Systems, 2011Co-Authors: Ken Kaneiwa, Norihiro KamideAbstract:In this paper, we propose a method for modeling concepts in full Computation-Tree logic with sequence modal operators. An extended full Computation-Tree logic, CTLS*, is introduced as a Kripke semantics with a sequence modal operator. This logic can appropriately represent hierarchical Tree structures in cases where sequence modal operators in CTLS* are applied to Tree structures. We prove a theorem for embedding CTLS* into CTL*. The validity, satisfiability, and model-checking problems of CTLS* are shown to be decidable. An illustrative example of biological taxonomy is presented using CTLS* formulas. © 2011 Wiley Periodicals, Inc. (This paper is an extended version of Kamide and Kaneiwa.)
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extended full Computation Tree logic with sequence modal operator representing hierarchical Tree structures
Australasian Joint Conference on Artificial Intelligence, 2009Co-Authors: Norihiro Kamide, Ken KaneiwaAbstract:An extended full Computation-Tree logic, CTLS*, is introduced as a Kripke semantics with a sequence modal operator. This logic can appropriately represent hierarchical Tree structures where sequence modal operators in CTLS* are applied to Tree structures. An embedding theorem of CTLS* into CTL* is proved. The validity, satisfiability and model-checking problems of CTLS* are shown to be decidable. An illustrative example of biological taxonomy is presented using CTLS* formulas.
Ken Kaneiwa - One of the best experts on this subject based on the ideXlab platform.
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paraconsistent Computation Tree logic
New Generation Computing, 2011Co-Authors: Ken Kaneiwa, Norihiro KamideAbstract:It is known that paraconsistent logical systems are more appropriate for inconsistency-tolerant and uncertainty reasoning than other types of logical systems. In this paper, a paraconsistent Computation Tree logic, PCTL, is obtained by adding paraconsistent negation to the standard Computation Tree logic CTL. PCTL can be used to appropriately formalize inconsistency-tolerant temporal reasoning. A theorem for embedding PCTL into CTL is proved. The validity, satisfiability, and model-checking problems of PCTL are shown to be decidable. The embedding and decidability results indicate that we can reuse the existing CTL-based algorithms for validity, satisfiability, and model-checking. An illustrative example of medical reasoning involving the use of PCTL is presented.
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Conceptual modeling in full Computation-Tree logic with sequence modal operator
International Journal of Intelligent Systems, 2011Co-Authors: Ken Kaneiwa, Norihiro KamideAbstract:In this paper, we propose a method for modeling concepts in full Computation-Tree logic with sequence modal operators. An extended full Computation-Tree logic, CTLS*, is introduced as a Kripke semantics with a sequence modal operator. This logic can appropriately represent hierarchical Tree structures in cases where sequence modal operators in CTLS* are applied to Tree structures. We prove a theorem for embedding CTLS* into CTL*. The validity, satisfiability, and model-checking problems of CTLS* are shown to be decidable. An illustrative example of biological taxonomy is presented using CTLS* formulas. © 2011 Wiley Periodicals, Inc. (This paper is an extended version of Kamide and Kaneiwa.)
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extended full Computation Tree logic with sequence modal operator representing hierarchical Tree structures
Australasian Joint Conference on Artificial Intelligence, 2009Co-Authors: Norihiro Kamide, Ken KaneiwaAbstract:An extended full Computation-Tree logic, CTLS*, is introduced as a Kripke semantics with a sequence modal operator. This logic can appropriately represent hierarchical Tree structures where sequence modal operators in CTLS* are applied to Tree structures. An embedding theorem of CTLS* into CTL* is proved. The validity, satisfiability and model-checking problems of CTLS* are shown to be decidable. An illustrative example of biological taxonomy is presented using CTLS* formulas.
Sanjit A Seshia - One of the best experts on this subject based on the ideXlab platform.
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control improvisation with probabilistic temporal specifications
The Internet of Things, 2016Co-Authors: Ilge Akkaya, Daniel J Fremont, Rafael Valle, Alexandre Donze, Edward A Lee, Sanjit A SeshiaAbstract:We consider the problem of generating randomized control sequences for complex networked systems typically actuated by human agents. Our approach leverages a concept known as control improvisation, which is based on a combination of data-driven learning and controller synthesis from formal specifications. We learn from existing data a generative model (for instance, an explicit-duration hidden Markov model, or EDHMM) and then supervise this model in order to guarantee that the generated sequences satisfy some desirable specifications given in Probabilistic Computation Tree Logic (PCTL). We present an implementation of our approach and apply it to the problem of mimicking the use of lighting appliances in a residential unit, with potential applications to home security and resource management. We present experimental results showing that our approach produces realistic control sequences, similar to recorded data based on human actuation, while satisfying suitable formal requirements.
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robust strategy synthesis for probabilistic systems applied to risk limiting renewable energy pricing
Embedded Software, 2014Co-Authors: Alberto Puggelli, Alberto Sangiovannivincentelli, Sanjit A SeshiaAbstract:We address the problem of synthesizing control strategies for Ellipsoidal Markov Decision Processes (EMDP), i.e., MDPs whose transition probabilities are expressed using ellipsoidal uncertainty sets. The synthesized strategy aims to maximize the total expected reward of the EMDP, constrained to a specification expressed in Probabilistic Computation Tree Logic (PCTL). We prove that the EMDP strategy synthesis problem for the fragment of PCTL disabling operators with a finite time bound is NP-complete and propose a novel sound and complete algorithm to solve it. We apply these results to the problem of synthesizing optimal energy pricing and dispatch strategies in smart grids that integrate renewable sources of energy. We use rewards to maximize the profit of the network operator and a PCTL specification to constrain the risk of power unbalance and guarantee quality-of-service for the users. The EMDP model used to represent the decision-making scenario was trained with measured data and quantitatively captures the uncertainty in the prediction of energy generation. An experimental comparison shows the effectiveness of our method with respect to previous approaches presented in the literature.
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polynomial time verification of pctl properties of mdps with convex uncertainties
Computer Aided Verification, 2013Co-Authors: Alberto Puggelli, Alberto Sangiovannivincentelli, Sanjit A SeshiaAbstract:We address the problem of verifying Probabilistic Computation Tree Logic (PCTL) properties of Markov Decision Processes (MDPs) whose state transition probabilities are only known to lie within uncertainty sets. We first introduce the model of Convex-MDPs (CMDPs), i.e., MDPs with convex uncertainty sets. CMDPs generalize Interval-MDPs (IMDPs) by allowing also more expressive (convex) descriptions of uncertainty. Using results on strong duality for convex programs, we then present a PCTL verification algorithm for CMDPs, and prove that it runs in time polynomial in the size of a CMDP for a rich subclass of convex uncertainty models. This result allows us to lower the previously known algorithmic complexity upper bound for IMDPs from co-NP to PTIME. We demonstrate the practical effectiveness of the proposed approach by verifying a consensus protocol and a dynamic configuration protocol for IPv4 addresses.
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unbounded fully symbolic model checking of timed automata using boolean methods
Lecture Notes in Computer Science, 2003Co-Authors: Sanjit A Seshia, Randal E BryantAbstract:We present a new approach to unbounded, fully symbolic model checking of timed automata that is based on an efficient translation of quantified separation logic to quantified Boolean logic. Our technique preserves the interpretation of clocks over the reals and can check any property in timed Computation Tree logic. The core operations of eliminating quantifiers over real variables and deciding the validity of separation logic formulas are respectively translated to eliminating quantifiers on Boolean variables and checking Boolean satisfiability (SAT). We can thus leverage well-known techniques for Boolean formulas, including Binary Decision Diagrams (BDDs) and recent advances in SAT and SAT-based quantifier elimination. We present preliminary empirical results for a BDD-based implementation of our method.