Using Session Types for Reasoning About Boundedness in the Pi-Calculus

The classes of depth-bounded and name-bounded processes are fragments of the pi-calculus for which some of the decision problems that are undecidable for the full calculus become decidable. P is depth-bounded at level k if every reduction sequence for P contains successor processes with at most k active nested restrictions. P is name-bounded at level k if every reduction sequence for P contains successor processes with at most k active bound names. Membership of these classes of processes is undecidable. In this paper we use binary session types to decise two type systems that give a sound characterization of the properties: If a process is well-typed in our first system, it is depth-bounded. If a process is well-typed in our second, more restrictive type system, it will also be name-bounded.Comment: In Proceedings EXPRESS/SOS 2017, arXiv:1709.0004

Compositional Reasoning for Explicit Resource Management in Channel-Based Concurrency

We define a pi-calculus variant with a costed semantics where channels are treated as resources that must explicitly be allocated before they are used and can be deallocated when no longer required. We use a substructural type system tracking permission transfer to construct coinductive proof techniques for comparing behaviour and resource usage efficiency of concurrent processes. We establish full abstraction results between our coinductive definitions and a contextual behavioural preorder describing a notion of process efficiency w.r.t. its management of resources. We also justify these definitions and respective proof techniques through numerous examples and a case study comparing two concurrent implementations of an extensible buffer.Comment: 51 pages, 7 figure

Work-in-progress Assume-guarantee reasoning with ioco

This paper presents a combination between the assume-guarantee paradigm and the testing relation ioco. The assume-guarantee paradigm is a ”divide and conquer” technique that decomposes the verification of a system into smaller tasks that involve the verification of its components. The principal aspect of assume-guarantee reasoning is to consider each component separately, while taking into account assumptions about the context of the component. The testing relation ioco is a formal conformance relation for model-based testing that works on labeled transition systems. Our main result shows that, with certain restrictions, assume-guarantee reasoning can be applied in the context of ioco. This enables testing ioco-conformance of a system by testing its components separately

Characteristic Bisimulation for Higher-Order Session Processes

Characterising contextual equivalence is a long-standing issue for higher-order (process) languages. In the setting of a higher-order pi-calculus with sessions, we develop characteristic bisimilarity, a typed bisimilarity which fully characterises contextual equivalence. To our knowledge, ours is the first characterisation of its kind. Using simple values inhabiting (session) types, our approach distinguishes from untyped methods for characterising contextual equivalence in higher-order processes: we show that observing as inputs only a precise finite set of higher-order values suffices to reason about higher-order session processes. We demonstrate how characteristic bisimilarity can be used to justify optimisations in session protocols with mobile code communication

Deep Reinforcement Learning on a Budget: 3D Control and Reasoning Without a Supercomputer

An important goal of research in Deep Reinforcement Learning in mobile robotics is to train agents capable of solving complex tasks, which require a high level of scene understanding and reasoning from an egocentric perspective. When trained from simulations, optimal environments should satisfy a currently unobtainable combination of high-fidelity photographic observations, massive amounts of different environment configurations and fast simulation speeds. In this paper we argue that research on training agents capable of complex reasoning can be simplified by decoupling from the requirement of high fidelity photographic observations. We present a suite of tasks requiring complex reasoning and exploration in continuous, partially observable 3D environments. The objective is to provide challenging scenarios and a robust baseline agent architecture that can be trained on mid-range consumer hardware in under 24h. Our scenarios combine two key advantages: (i) they are based on a simple but highly efficient 3D environment (ViZDoom) which allows high speed simulation (12000fps); (ii) the scenarios provide the user with a range of difficulty settings, in order to identify the limitations of current state of the art algorithms and network architectures. We aim to increase accessibility to the field of Deep-RL by providing baselines for challenging scenarios where new ideas can be iterated on quickly. We argue that the community should be able to address challenging problems in reasoning of mobile agents without the need for a large compute infrastructure