We present a framework for verifying strategic behaviour in an unbounded multi-agent system. We introduce a novel probabilistic semantics for parameterised multi-agent systems and define the corresponding verification problem against two probabilistic variants of alternating-time temporal logic. We define a verification procedure using an abstract model construction. We show that the procedure is complete for one variant of our specification language, and partial for the other. We present an implementation and report experimental results.

We present a method for reasoning about fault-tolerance in unbounded robotic swarms. We introduce a novel semantics that accounts for the probabilistic nature of both the swarm and possible malfunctions, as well as the unbounded nature of swarm systems. We define and interpret a variant of probabilistic linear-time temporal logic on the resulting executions, including those arising from faulty behaviour by some of the agents in the swarm. We specify the decision problem of parameterised fault-tolerance, which concerns determining whether a probabilistic specification holds under possibly faulty behaviour. We outline a verification procedure that we implement and use to study a foraging protocol from swarm robotics, and report the experimental results obtained.

We perform a detailed analysis of the C++ implementation of the Cluster Affiliation Model for Big Networks (BigClam) on the Stanford Network Analysis Project (SNAP). BigClam is a popular graph mining algorithm that is capable of finding overlapping communities in networks containing millions of nodes. Our analysis shows a key stage of the algorithm – determining if a node belongs to a community – dominates the runtime of the implementation, yet the computation is not parallelized. We show that by parallelizing computations across multiple threads using OpenMP we can speed up the algorithm by 5.3 times when solving large networks for communities, while preserving the integrity of the program and the result. 2012 ACM Subject Classification Computing methodologies → Concurrent algorithms

We introduce agent-environment systems where the agent is stateful and executing a ReLU recurrent neural network. We define and study their verification problem by providing equivalences of recurrent and feed-forward neural networks on bounded execution traces. We give a sound and complete procedure for their verification against properties specified in a simplified version of LTL on bounded executions. We present an implementation and discuss the experimental results obtained.

We introduce a method for formally verifying properties of arbitrarily large swarms whose agents are modelled probabilistically. We define a parameterised probabilistic semantics for modelling swarms and observe that their verification problem against PLTL specifications is undecidable. We develop a partial procedure for verifying arbitrarily large swarms based on counter abstraction and show its correctness. We present an implementation and report the experimental results obtained when verifying a swarm foraging protocol.

We study open multi-agent systems in which countably many agents may leave and join the system at run-time. We introduce a semantics, based on interpreted systems, to capture the openness of the system and show how an indexed variant of temporal-epistemic logic can be used to express specifications on them. We define the verification problem and show it is undecidable. We isolate one decidable class of open multi-agent systems and give a partial decision procedure for another one. We introduce MCMAS-OP, an open-source toolkit implementing the verification procedures. We present the results obtained using our tool on two examples.

Welcome to the 2018 Imperial College Computing Student Workshop (ICCSW18), the seventh workshop in its series. ICCSW was initiated with a “by students, for students” spirit: it is a workshop organised by Imperial students and aims to give students of all universities a chance to present their research work. The organising students gain valuable experience in all the steps of organising a conference–from writing a call for papers to chairing a session. On the other hand, the participating students benefit from interacting with other researchers who are at an early stage in their career. It is also an opportunity for students to develop skills presenting their work to a general audience of computer scientists working in a variety of different areas.This volume contains the seven papers presented at the 2018 Imperial College Computing Student Workshop. It also includes abstracts of three of the posters presented during the workshop’s poster session and an abstract of a fantastic invited talk given by Matthew Johnson from Microsoft Research.

We introduce a method for formally verifying properties of arbitrarily large swarms whose agents are modelled probabilistically. We define a parameterised probabilistic semantics for modelling swarms and observe that their verification problem against PLTL specifications is undecidable. We develop a partial procedure for verifying arbitrarily large swarms based on counter abstraction and show its correctness. We present an implementation and report the experimental results obtained when verifying a swarm foraging protocol.

We develop a novel model for studying agent-environment systems, where the agents are implemented via feed-forward ReLU neural networks. We provide a semantics and develop a method to verify automatically that no unwanted states are reached by the system during its evolution. We study several reachability problems for the system, ranging from one-step reachability, to fixed multi-step and arbitrary-step to study the system evolution. We also study the decision problem of whether an agent, realised via feed-forward ReLU networks will perform an action in a system run. Whenever possible, we give tight complexity bounds to decision problems introduced. We automate the various reachability problems studied by recasting them as mixed-integer linear programming problems. We present an implementation and discuss the experimental results obtained on a range of test cases.

We study the problem of determining the robustness of a multi-agent system of unbounded size against specifications expressed in a temporal-epistemic logic. We introduce a procedure to synthesise automatically the maximal ratio of faulty agents that may be present at runtime for a specification to be satisfied in a multi-agent system. We show the procedure to be sound and amenable to symbolic implementation. We present an implementation and report the experimental results obtained by running this on a number of protocols from swarm robotics.

We introduce a parameterised semantics for reasoning about swarms as unbounded collections of agents in a probabilistic setting. We develop a method for the formal identification of emergent properties, expressed in a fragment of the probabilistic logic PCTL. We introduce algorithms for solving the related decision problems and show their correctness. We present an implementation and evaluate its performance on an ant coverage algorithm.