LTLf and LDLf Synthesis under Partial Observability

In this paper, we study synthesis under partial observability for logical specifications over finite traces expressed in LTLf/LDLf. This form of synthesis can be seen as a generalization of planning under partial observability in nondeterministic domains, which is known to be 2EXPTIME-complete. We start by showing that the usual "belief-state construction" used in planning under partial observability works also for general LTLf/LDLf synthesis, though with a jump in computational complexity from 2EXPTIME to 3EXPTIME. Then we show that the belief-state construction can be avoided in favor of a direct automata construction which exploits projection to hide unobservable propositions. This allow us to prove that the problem remains 2EXPTIME-complete. The new synthesis technique proposed is effective and readily implementable

Control with Probabilistic Signal Temporal Logic

Autonomous agents often operate in uncertain environments where their decisions are made based on beliefs over states of targets. We are interested in controller synthesis for complex tasks defined over belief spaces. Designing such controllers is challenging due to computational complexity and the lack of expressivity of existing specification languages. In this paper, we propose a probabilistic extension to signal temporal logic (STL) that expresses tasks over continuous belief spaces. We present an efficient synthesis algorithm to find a control input that maximises the probability of satisfying a given task. We validate our algorithm through simulations of an unmanned aerial vehicle deployed for surveillance and search missions.Comment: 7 pages, submitted to the 2016 American Control Conference (ACC 2016) on September, 30, 2015 (under review

Cooperative Task Planning of Multi-Agent Systems Under Timed Temporal Specifications

In this paper the problem of cooperative task planning of multi-agent systems when timed constraints are imposed to the system is investigated. We consider timed constraints given by Metric Interval Temporal Logic (MITL). We propose a method for automatic control synthesis in a two-stage systematic procedure. With this method we guarantee that all the agents satisfy their own individual task specifications as well as that the team satisfies a team global task specification.Comment: Submitted to American Control Conference 201

Temporal Data Modeling and Reasoning for Information Systems

Temporal knowledge representation and reasoning is a major research field in Artificial Intelligence, in Database Systems, and in Web and Semantic Web research. The ability to model and process time and calendar data is essential for many applications like appointment scheduling, planning, Web services, temporal and active database systems, adaptive Web applications, and mobile computing applications. This article aims at three complementary goals. First, to provide with a general background in temporal data modeling and reasoning approaches. Second, to serve as an orientation guide for further specific reading. Third, to point to new application fields and research perspectives on temporal knowledge representation and reasoning in the Web and Semantic Web

Technical Report: Distribution Temporal Logic: Combining Correctness with Quality of Estimation

We present a new temporal logic called Distribution Temporal Logic (DTL) defined over predicates of belief states and hidden states of partially observable systems. DTL can express properties involving uncertainty and likelihood that cannot be described by existing logics. A co-safe formulation of DTL is defined and algorithmic procedures are given for monitoring executions of a partially observable Markov decision process with respect to such formulae. A simulation case study of a rescue robotics application outlines our approach.Comment: More expanded version of "Distribution Temporal Logic: Combining Correctness with Quality of Estimation" to appear in IEEE CDC 201

Control with probabilistic signal temporal logic

Autonomous agents often operate in uncertain environments where their decisions are made based on beliefs over states of targets. We are interested in controller synthesis for complex tasks defined over belief spaces. Designing such controllers is challenging due to computational complexity and the lack of expressivity of existing specification languages. In this paper, we propose a probabilistic extension to signal temporal logic (STL) that expresses tasks over continuous belief spaces. We present an efficient synthesis algorithm to find a control input that maximises the probability of satisfying a given task. We validate our algorithm through simulations of an unmanned aerial vehicle deployed for surveillance and search missions