The 2011 Imperial College Computing Student Workshop proceedings are available online as well as individual submissions below.

Combining Markov Decision Processes with Linear Optimal Controllers

Ekaterina Abramova, Daniel Kuhn, and Aldo Faisal

Imperial College London

Abstract. Linear Quadratic Gaussian (LQG) control has a known analytical solution but non-linear problems do not. The state of the art method used to find approximate solutions to non-linear control problems (iterative LQG) carries a large computational cost associated with iterative calculations. We propose a novel approach for solving nonlinear Optimal Control (OC) problems which combines Reinforcement Learning (RL) with OC. The new algorithm, RLOC, uses a small set of localized optimal linear controllers and applies a Monte Carlo algorithm that learns the mapping from the state space to controllers. We illustrate our approach by solving a non-linear OC problem of the 2-joint arm operating in a plane with two point masses. We show that controlling the arm with the RLOC is less costly than using the Linear Quadratic Regulator (LQR). This finding shows that non-linear optimal control problems can be solved using a novel approach of adaptive RL.

Neural-Symbolic Cognitive Agents: Architecture and Theory

H.L.H. de Penning, A.S. d'Avila Garcez, L.C. Lamb, J-J.C. Meyer

TNO Behaviour and Societal Sciences, City University London, Universidade Federal do Rio Grande do Sul, Utrecht University

Abstract. In real-world applications, the effective integration of learning and reasoning in a cognitive agent model is a difficult task. However, such integration may lead to a better understanding, use and construction of more realistic models. Unfortunately, existing models are either oversimplified or require much processing time, which is unsuitable for online learning and reasoning. Currently, controlled environments like training simulators do not effectively integrate learning and reasoning. In particular, higher-order concepts and cognitive abilities have many unknown temporal relations with the data, making it impossible to represent such relationships by hand. We introduce a novel cognitive agent model and architecture for online learning and reasoning that seeks to effectively represent, learn and reason in complex real-world applications. The agent architecture of the model combines neural learning with symbolic knowledge representation. It is capable of learning new hypotheses from observed data, and inferring new beliefs based on these hypotheses. Furthermore, it deals with uncertainty and errors in the data using a Bayesian inference model. The model has successfully been applied in real-time simulation and visual intelligence systems.

Time-Bounded Verification of CTMCs against MTL specifications

Taolue Chen, Marco Diciolla, Marta Kwiatkowska, and Alexandru Mereacre

Oxford University

Abstract. In this paper we study time-bounded verification of a finite continuoustime Markov chain (CTMC) C against a real-time specification, provided as a metric temporal logic (MTL) property ϕ. The key question is: what is the probability of the set of timed paths of C that satisfy ϕ over a time interval of fixed, bounded length? We provide approximation algorithms to solve these problems. We first derive a bound N such that timed paths of C with at most N discrete jumps are sufficient to approximate the desired probability up to ε. Then, for each discrete (untimed) path σ of length at most N , we generate timed constraints over variables determining the residence time of each state along σ, depending on the real-time specification under consideration. The probability of the set of timed paths, determined by the discrete path and the associated timed constraints, can thus be formulated as a multidimensional integral. Summing up all such probabilities yields the result.

MASSPA-Modeller: A Spatial Stochastic Process Algebra modelling tool

Marcel C. Guenther and Jeremy T. Bradley

Imperial College London

Abstract. We introduce MASSPA–Modeller, a visual modelling tool for the recently developed spatial stochastic process algebra MASSPA which describes Markovian Agent Models (MAM)s. The major advantage of using a visual editor to generate MASSPA models is that the laborious task of modelling the communication between agents is partially automated by the tool. Furthermore the tool can separately check the correctness of local and spatial aspects of the model and thereby help users to find mistakes. For the analysis of the resulting models MASSPA–Modeller uses the powerful GPA–analyser engine. Additionally we briefly summarise the latest developments in spatial stochastic process algebras.

Argumentation and Temporal Persistence

E. Hadjisoteriou and A. Kakas

University of Cyprus

Abstract. We study how the problem of temporal projection can be formalized in terms of argumentation. In particular, we extend earlier work of translating the language E for Reasoning about Actions and Change into a Logic Programming argumentation framework, by introducing new types of arguments for (i) backward persistence and (ii) persistence from observations. This forms a conservative extension of the language E that gives semantic meaning to domains that cannot be interpreted in the language E.

Facial Action Recognition using sparse appearance descriptors and their pyramid representations

Bihan Jiang, Michel F. Valstar, and Maja Pantic

Imperial College London, University of Twente

Abstract. Most existing work on automatic analysis of facial expressions has focused on a small set of prototypic emotional facial expressions such as fear, happiness, and surprise. The system proposed here enables detection of a much larger range of facial behaviour by detecting facial muscle actions (action units, AUs). It automatically detect all 9 upper face AUs using local apperance descriptors. Meanwhile, the merits of the family of local binary pattern descriptors are investigated. We compare Local Binary Patterns, Local Phase Quantisation, Pyramid Local Binary Pattern, as well as our proposed descriptors Block-based Pyramid Local Binary Pattern and Block-based Pyramid Local Phase Quantisation for AU detection. Results show that our proposed descriptor Block-based pyramid Local Binary Pattern outperforms all other tested methods for the problem of FACS Action Unit analysis and the systems that utilise pyramid representation outperform those that use basic appearance descriptors.

Note on a Dichotomy for the Classes W[P](C) Defined via Symmetric Connectives

Bjorn Lellmann

Imperial College London

Abstract. Adopting a generalised notion of connectives as ptime-computable symmetric boolean functions, for finite sets C of such connectives the classes W[P](C) are defined via the parameterised weighted satisfiability problem for circuits with C-gates. This note will prove the following dichotomy result: for all finite sets C of connectives W[P](C) = FPT or W[P](C) = W[P].

Agent Oriented Programming: from Revolution to Evolution (Position Paper)

Alex Muscar

University of Craiova

Abstract. Despite being around for quite some time, agents have failed to gain wide acceptance. Their AI heritage has forced them into a niche from which they cannot seem to escape: being the vehicle of AI experimentation. Even though the premises of Agent Oriented Programming (AOP) is a revolutionary departure from Object Oriented Programming (OOP) the vision has not materialized. In this paper we propose taking a step back and looking at AOP as an evolution from OOP. Rather than viewing agents as specialized AI tools, we adopt a view on agents as a generic metaphor for building complex software systems. The guiding lines of our approach are: (i) overall conceptual simplicity; and (ii) the use of programming languages as the main means of expression. We will explore some paradigms and approaches that we think can greatly benefit the view of agents as an evolution from OOP.

Conditional Labelling for Abstract Argumentation

Guido Boella, Dov M. Gabbay, Alan Perotti, Leendert van der Torre, and Serena Villata

Universita di Torino, King’s College London, University of Luxembourg, INRIA

Abstract. Agents engage in dialogues having as goals to make some arguments acceptable or unacceptable. To do so they may put forward arguments, adding them to the argumentation framework. Argumentation semantics can relate a change in the framework to the resulting extensions but it is not clear, given an argumentation framework and a desired acceptance state for a given set of arguments, which further arguments should be added in order to achieve those justification statuses. Our methodology, called conditional labelling, is based on argument labelling and assigns to each argument three propositional formulae. These formulae describe which arguments should be attacked by the agent in order to get a particular argument in, out, or undecided, respectively. Given a conditional labelling, the agents have a full knowledge about the consequences of their attacks on the acceptability of each arguments, without having to recompute the overall labelling of the framework for each possible set of attacks they may raise.

A Persuasive Dialogue Game for Coalition Formation

Luke Riley

University of Liverpool

Abstract. In this paper, I propose a formal dialogue framework that enables autonomous agents to engage in a process of practical reasoning, in which they can propose to form coalitions that perform joint actions, using argumentation. An argumentation scheme is used to drive this coalition formation process that results in qualitative payoffs. This paper builds on existing work that uses value-based argumentation in the context of a dialogue system, which has been empirically verified. This framework is designed explicitly for closed cooperative systems where agents hold different preferences.

Model-based Self-Adaptive Components: A Preliminary Approach

Pedro Rodrigues and Emil Lupu

Imperial College London

Abstract. Due to the increasing scale, complexity, dynamicity and heterogeneity of modern software systems, it is not feasible to solely rely upon human management to guarantee a good service level with such availability demand. Self-managing systems are needed as an effective approach to deal with those issues by exploiting adaptive techniques to adjust a system. On top of that, model-based adaptation improves reliability, hence enhancing trust in self-managing systems. However, a centralised approach can be too complex to manage thus compromising system dependability. This paper presents a preliminary decentralised approach on model-based self-adaptive components.

Safe, Flexible Recursive Types for Featherweight Java

Reuben N. S. Rowe

Imperial College London

Abstract. This paper presents a type assignment system with recursive types for Featherweight Java, inspired by the work of Nakano. Nakano’s innovation consists in adding a modal type constructor which acts to control the folding of recursive types, resulting in a head-normalisation guarantee. We build on this approach by introducing a second modal type constructor which prevents the unfolding of types in contexts where doing so results in non-termination. Moreover our system inherits the flexibility of Nakano’s approach, allowing object-oriented features (such as binary methods) to be typed in a safe and intuitive way. The work described in this paper is preliminary, and no formal results are claimed. However, we conjecture that our type system enjoys strong normalisation and we motivate this by working through some apposite examples.

Measuring minimal change in argument premise revision

Mark Snaith and Chris Reed

University of Dundee

Abstract. The field of belief revision studies how information can be given up in the face of new, conflicting information, while argumentation provides methods through which conflict can be modelled and the resultant acceptability of arguments evaluated. Prominent theories of belief revision depend on the notion of minimal change, measured in terms of epistemic entrenchment, to determine what beliefs to give up. In this paper, we take an initial look at the effects of removing an argument from a system of structured argumentation, in terms of both argument construction and acceptability, and how these can be used in the determination of minimal change.

Applying Algebraic Specifications on Digital Right Management Systems

Nikolaos Triantafyllou, Katerina Ksystra, Petros Stefaneas and Panayiotis Frangos

National Technical University of Athens

Abstract. Digital Right Management (DRM) Systems have been created to meet the need for digital content protection and distribution. In this survey paper we present some of the directions of our ongoing research on the applications of the algebraic specification techniques on mobile DRM systems.

Reduction of Variability in Split–Merge Systems

Iryna Tsimashenka and William Knottenbelt

Imperial College London

Abstract. We consider an optimisation problem applicable to systems that can be represented as split–merge queueing networks with a limited buffer space for processed subtasks. We assume Poisson arrivals and generally distributed service times. The proposition is to reduce variability in terms of the difference in the times of arrival of the first and last subtasks in systems where the release times of the subtasks can be controlled. This stands in contrast to the overwhelming majority of research which is focused on reduction of mean response time or percentiles of response time. We formally define our notion of variability in split–merge systems and construct an associated cost function and optimisation problem. For two case studies we use simulation to explore the optimisation landscape and to solve the associated optimisation problem.

Real-Time Detection of Process Change using Process Mining

Phil Weber, Behzad Bordbar, and Peter Tino

University of Birmingham

Abstract. Process Mining is the discovery of business processes from log files. One application is ensuring conformance to prescribed processes or business rules. Since businesses operate in real time, needing to quickly react to change, processes change; but how can such changes be detected? We consider requirements for process mining to support this: a notion of real time, and methods to compare processes and detect significant change. We present initial results confirming the validity of the approach.