Jiefei Ma

Expressive policy analysis with enhanced system dynamicity

Despite several research studies, the effective analysis of policy based systems remains a significant challenge. Policy analysis should at least (i) be expressive (ii) take account of obligations and authorizations, (iii) include a dynamic system model, and (iv) give useful diagnostic information. We present a logic-based policy analysis framework which satisfies these requirements, showing how many significant policy-related properties can be analysed, and we give details of a prototype implementation.

Towards making network function virtualization a cloud computing service

By allowing network functions to be virtualized and run on commodity hardware, NFV enables new properties (e.g., elastic scaling), and new service models for Service Providers, Enterprises, and Telecommunication Service Providers. However, for NFV to be offered as a service, several research problems still need to be addressed. In this paper, we focus and propose a new service chaining algorithm. Existing solutions suffer two main limitations: First, existing proposals often rely on mixed Integer Linear Programming to optimize VM allocation and network management, but our experiments show that such approach is too slow taking hours to find a solution. Second, although existing proposals have considered the VM placement and network configuration jointly, they frequently assume the network configuration cannot be changed. Instead, we believe that both computing and network resources should be able to be updated concurrently for increased flexibility and to satisfy SLA and Qos requirements. As such, we formulate and propose a Genetic Algorithm based approach to solve the VM allocation and network management problem. We built an experimental NFV platform, and run a set of experiments. The results show that our proposed GA approach can compute configurations to to three orders of magnitude faster than traditional solutions.

DARE: a system for distributed abductive reasoning

Abductive reasoning is a well established field of Artificial Intelligence widely applied to different problem domains not least cognitive robotics and planning. It has been used to abduce high-level descriptions of the world from robot sense data, using rules that tell us what sense data would be generated by certain objects and events of the robots world, subject to certain constraints on their co-occurrence. It has also been used to abduce actions that might result in a desired goal state of the world, using descriptions of the normal effects of these actions, subject to constraints on the action combinations. We can generalise these applications to a multi-agent context. Several robots can collaboratively try to abduce an agreed higher-level description of the state of the world from their separate sense data consistent with their collective constraints on the abduced description. Similarly, multi-agent planning can be accomplished by the abduction of the actions of a collective plan where each agent uses its own description of the effect of its actions within the plan, such that the constraints on the actions of all the participating agents are satisfied. To address this class of problems, we need to generalise the single agent abductive reasoning algorithm to a distributed abductive inference algortihm. In addition, if we want to investigate applications in which the set of collaborating robots/agents is open, we need an algorithm that allows agents to join or leave the collaborating group whilst a particular inference is under way, but which still produces sound abductive inferences. This paper describes such a distributed abductive reasoning system, which we call DARE, and its implementation in the multi-threaded Qu-Prolog variant of Prolog. We prove the soundness of the algorithm it uses and we discuss its completeness in relation to non-distributed abductive reasoning. We illustrate the use of the algorithm with a multi-agent meeting scheduling example. The task is open in that the actual agents who need to attend is not determined in advance. Each individual agent has its own constraints on the possible meeting time and concerning which other agents must or must attend the meeting, if it attends. The algorithm selects the agents to attend and ensures that the constraints of each of the attending agents are satisfied.

Pandora: A Reasoning Toolbox using Natural Deduction Style

Pandora is a tool for supporting the learning of first order natural deduction. It includes a help window, an interactive context sensitive tutorial known as the “e-tutor” and facilities to save, reload and export to LATEX. Every attempt to apply a natural deduction rule is met with either success or a helpful error message, providing the student with instant feedback. Detailed electronic logs of student usage are recorded for evaluation purposes. This paper describes the basic functionality, the e-tutor, our experiences of using the tool in teaching and our future plans.

Learning to recognise disruptive smartphone notifications

Short term studies in controlled environments have shown that user behaviour is consistent enough to predict disruptive smartphone notifications. However, in practice, user behaviour changes over time (concept drift) and individual user preferences need to be considered. There is a lack of research on which methods are best suited for predicting disruptive smartphone notifications longer-term, taking into account varying error costs. In this paper we report on a 16 week field study comparing how well different learners perform at mitigating disruptive incoming phone calls.

Speculative abductive reasoning for hierarchical agent systems

Answer sharing is a key element in multi-agent systems as it allows agents to collaborate towards achieving a global goal. However exogenous knowledge of the world can influence each agents local computation, and communication channels may introduce delays, creating multiple partial answers at different times. Agents answers may, therefore, be incomplete and revisable, giving rise to the concept of speculative reasoning, which provides a framework for managing multiple revisable answers within the context of multi-agent systems. This paper extends existing work on speculative reasoning by introducing a new abductive framework to hierarchical speculative reasoning. This allows speculative reasoning in the presence of both negation and constraints, enables agents to receive conditional answers and to continue their local reasoning using default answers, thus increasing the parallelism of agents collaboration. The paper describes the framework and its operational model, illustrates the main features with an example and states soundness and completeness results.

A declarative approach to distributed computing: Specification, execution and analysis

There is an increasing interest in using logic programming to specify and implement distributed algorithms, including a variety of network applications. These are applications where data and computation are distributed among several devices and where, in principle, all the devices can exchange data and share the computational results of the group. In this paper we propose a declarative approach to distributed computing whereby distributed algorithms and communication models can be (i) specified as action theories of fluents and actions; (ii) executed as collections of distributed state machines, where the devices are abstracted as (input/output) automata that can exchange messages; and (iii) analysed using existing results on connecting causal theories and Answer Set Programming (ASP). This work extends our initial results on the use of an A-type language for writing network applications, by showing that it is possible to achieve similar expressiveness and generality but using only two types of non-logical symbols (fluents and actions). Results on the application of our approach to different classes of network protocols are also presented.

Refinement of history-based policies

We propose an efficient method to evaluate a large class of history-based policies written as logic programs. To achieve this, we dynamically compute, from a given policy set, a finite subset of the history required and sufficient to evaluate the policies. We maintain this history by monitoring rules and transform the policies into a non history-based form. We further formally prove that evaluating history-based policies can be reduced to an equivalent, but more efficient, evaluation of the non history-based policies together with the monitoring rules.

Declarative distributed computing

In this paper we present a language to write distributed applications. We provide an operational semantics of a single computational node based on Datalog. We then introduce a framework that can capture the semantics of a network of computational nodes working together. The framework can express several communication models (e.g. synchronous vs. asynchronous) and can be used to check many properties of the distributed computation under the different communication models. The framework is developed using Answer Set Programs.

Distributed abductive reasoning with constraints

Abductive inference has many known applications in multi-agent systems. However, most abductive frameworks rely on a centrally executed proof procedure whereas many of the application problems are distributed by nature. Confidentiality and communication overhead concerns often preclude transmitting all the knowledge required for centralised reasoning. We present in this paper a novel multi-agent abductive reasoning framework underpinned by a flexible and extensible distributed proof procedure that permits collaborative abductive reasoning with constraints between agents over decentralised knowledge.