232 results found
Mitchener L, Tuckey D, Crosby M, et al., 2022, Detect, understand, act: a neuro-symbolic hierarchical reinforcement learning framework, Machine Learning, ISSN: 0885-6125
In this paper we introduce Detect, Understand, Act (DUA), a neuro-symbolic reinforcement learning framework. The Detect component is composed of a traditional computer vision object detector and tracker. The Act component houses a set of options, high-level actions enacted by pre-trained deep reinforcement learning (DRL) policies. The Understand component provides a novel answer set programming (ASP) paradigm for symbolically implementing a meta-policy over options and effectively learning it using inductive logic programming (ILP). We evaluate our framework on the Animal-AI (AAI) competition testbed, a set of physical cognitive reasoning problems. Given a set of pre-trained DRL policies, DUA requires only a few examples to learn a meta-policy that allows it to improve the state-of-the-art on multiple of the most challenging categories from the testbed. DUA constitutes the first holistic hybrid integration of computer vision, ILP and DRL applied to an AAI-like environment and sets the foundations for further use of ILP in complex DRL challenges.
Koschate M, Naserian E, Dickens L, et al., 2021, ASIA: Automated Social Identity Assessment using linguistic style, Behavior Research Methods, Vol: 53, Pages: 1762-1781, ISSN: 1554-351X
The various group and category memberships that we hold are at the heart of who we are. They have been shown to affect our thoughts, emotions, behavior, and social relations in a variety of social contexts, and have more recently been linked to our mental and physical well-being. Questions remain, however, over the dynamics between different group memberships and the ways in which we cognitively and emotionally acquire these. In particular, current assessment methods are missing that can be applied to naturally occurring data, such as online interactions, to better understand the dynamics and impact of group memberships in naturalistic settings. To provide researchers with a method for assessing specific group memberships of interest, we have developed ASIA (Automated Social Identity Assessment), an analytical protocol that uses linguistic style indicators in text to infer which group membership is salient in a given moment, accompanied by an in-depth open-source Jupyter Notebook tutorial (https://github.com/Identity-lab/Tutorial-on-salient-social-Identity-detection-model). Here, we first discuss the challenges in the study of salient group memberships, and how ASIA can address some of these. We then demonstrate how our analytical protocol can be used to create a method for assessing which of two specific group memberships—parents and feminists—is salient using online forum data, and how the quality (validity) of the measurement and its interpretation can be tested using two further corpora as well as an experimental study. We conclude by discussing future developments in the field.
Law M, Russo A, Broda K, et al., 2021, Scalable non-observational predicate learning in ASP, IJCAI, Publisher: IJCAI, ISSN: 1045-0823
Recently, novel ILP systems under the answer set semantics have been proposed, some of which are robust to noise and scalable over large hypothesis spaces. One such system is FastLAS, which is significantly faster than other state-of-the-art ASP-based ILP systems. FastLAS is, however, only capable of Observational Predicate Learning (OPL),where the learned hypothesis defines predicates that are directly observed in the examples. It cannot learn knowledge that is indirectly observable, such as learning causes of observed events. This class of problems, known as non-OPL, is known to be difficult to handle in the context of non-monotonic semantics. Solving non-OPL learning tasks whilst preserving scalability is a challenging open problem. We address this problem with a new abductive method for translating examples of a non-OPL task to a set of examples, called possibilities, such that the original example is covered if at least one of the possibilities is covered. This new method al-lows an ILP system capable of performing OPL tasks to be “upgraded” to solve non-OPL tasks. In particular, we present our new FastNonOPL system, which upgrades FastLAS with the new possibility generation. We compare it to other state-of-the-art ASP-based ILP systems capable of solving non-OPL tasks, showing that FastNonOPL is significantly faster, and in many cases more accurate, than these other systems.
Furelos-Blanco D, Law M, Jonsson A, et al., 2021, Induction and exploitation of subgoal automata for reinforcement learning, Journal of Artificial Intelligence Research, Vol: 70, Pages: 1031-1116, ISSN: 1076-9757
In this paper we present ISA, an approach for learning and exploiting subgoals in episodic reinforcement learning (RL) tasks. ISA interleaves reinforcement learning with the induction of a subgoal automaton, an automaton whose edges are labeled by the task’s subgoals expressed as propositional logic formulas over a set of high-level events. A subgoal automaton also consists of two special states: a state indicating the successful completion of the task, and a state indicating that the task has finished without succeeding. A state-of-the-art inductive logic programming system is used to learn a subgoal automaton that covers the traces of high-level events observed by the RL agent. When the currently exploited automaton does not correctly recognize a trace, the automaton learner induces a new automaton that covers that trace. The interleaving process guarantees the induction of automata with the minimum number of states, and applies a symmetry breaking mechanism to shrink the search space whilst remaining complete. We evaluate ISA in several gridworld and continuous state space problems using different RL algorithms that leverage the automaton structures. We provide an in-depth empirical analysis of the automaton learning performance in terms of the traces, the symmetry breaking and specific restrictions imposed on the final learnable automaton. For each class of RL problem, we show that the learned automata can be successfully exploited to learn policies that reach the goal, achieving an average reward comparable to the case where automata are not learned but handcrafted and given beforehand.
Cingillioglu N, Russo A, 2021, pix2rule: End-to-end Neuro-symbolic Rule Learning, 15th International Workshop on Neural-Symbolic Learning and Reasoning (NeSy) as part of the 1st International Joint Conference on Learning and Reasoning (IJCLR), Publisher: RWTH AACHEN, Pages: 15-56, ISSN: 1613-0073
Cunnington D, Law M, Russo A, et al., 2021, Towards Neural-Symbolic Learning to support Human-Agent Operations
This paper investigates neural-symbolic policy learning for information fusion in distributed human-agent operations. The architecture integrates a pre-trained neural network for feature extraction, with a state-of-the-art symbolic Inductive Logic Programming (ILP) system to learn policies, expressed as a set of logical rules. We firstly outline the challenge of policy learning within a military environment, by investigating the accuracy and confidence of neural network predictions given data outside the training distribution. Secondly, we introduce a neural-symbolic integration for policy learning and demonstrate that the symbolic ILP component, when considering the length of the learned policy rules, can generalise and learn a robust policy despite unstructured data observed at policy learning time originating from a different distribution than observed during training.
Furelos-Blanco D, Law M, Jonsson A, et al., 2021, Induction and Exploitation of Subgoal Automata for Reinforcement Learning, JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH, Vol: 70, Pages: 1031-1116, ISSN: 1076-9757
Al-Negheimish H, Madhyastha P, Russo A, 2021, Numerical reasoning in machine reading comprehension tasks: are we there yet?, Pages: 9643-9649
Numerical reasoning based machine reading comprehension is a task that involves reading comprehension along with using arithmetic operations such as addition, subtraction, sorting, and counting. The DROP benchmark (Dua et al., 2019) is a recent dataset that has inspired the design of NLP models aimed at solving this task. The current standings of these models in the DROP leaderboard, over standard metrics, suggest that the models have achieved near-human performance. However, does this mean that these models have learned to reason? In this paper, we present a controlled study on some of the top-performing model architectures for the task of numerical reasoning. Our observations suggest that the standard metrics are incapable of measuring progress towards such tasks.
Drozdov A, Law M, Lobo J, et al., 2021, Online Symbolic Learning of Policies for Explainable Security, Pages: 269-278
Statistical Machine Learning (ML) has been proved to be an invaluable tool in many areas including privacy and security. On the other hand, recent advances in the field of Symbolic Learning have included novel scalable algorithms that learn highly accurate classifiers encoded as logic programs. In this paper we advocate adding Symbolic Learning to the security and privacy ML toolset. Through an example in anomaly detection, we present a framework for developing systems capable of performing symbolic-based learning of security policies. Our framework, called Online Learning of Anomaly detection Policies from Historical data (OLAPH), uses a symbolic learning system and a domain-specific function for scoring candidate rules to guide the learning process towards the best policies for anomaly detection. The learned policies are fully explainable since the underlying symbolic learning system is inherently explainable: there is a one-to-one mapping between the learned symbolic rules and the anomaly detection policies. The online feature of OLAPH uses a notion of policy confidence to decide when to relearn the policy and what data to relearn the policy from. OLAPH has been evaluated on a dataset of network requests from a commercial security provider, and shown to have a strong anomaly detection performance in addition to the usability and explainability benefits induced by its symbolic learning approach.
Preece A, Braines D, Cerutti F, et al., 2021, Coalition Situational Understanding via Explainable Neuro-Symbolic Reasoning and Learning, Conference on Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III, Publisher: SPIE-INT SOC OPTICAL ENGINEERING, ISSN: 0277-786X
Al-Negheimish H, Madhyastha P, Russo A, 2021, Discrete reasoning templates for natural language understanding, Pages: 80-87
Reasoning about information from multiple parts of a passage to derive an answer is an open challenge for reading-comprehension models. In this paper, we present an approach that reasons about complex questions by decomposing them to simpler subquestions that can take advantage of single-span extraction reading-comprehension models, and derives the final answer according to instructions in a predefined reasoning template. We focus on subtraction based arithmetic questions and evaluate our approach on a subset of the DROP dataset. We show that our approach is competitive with the state of the art while being interpretable and requires little supervision.
Stromfelt H, Dickens L, Garcez AD, et al., 2021, Coherent and Consistent Relational Transfer Learning with Auto-encoders, 15th International Workshop on Neural-Symbolic Learning and Reasoning (NeSy) as part of the 1st International Joint Conference on Learning and Reasoning (IJCLR), Publisher: RWTH AACHEN, Pages: 176-192, ISSN: 1613-0073
Cingillioglu N, Russo A, 2020, Learning invariants through soft unification, Advances in Neural Information Processing Systems 33, ISSN: 1049-5258
Human reasoning involves recognising common underlying principles across many examples. The by-products of such reasoning are invariants that capture patterns such as “if someone went somewhere then they are there”, expressed using variables “someone” and “somewhere” instead of mentioning specific people or places. Humans learn what variables are and how to use them at a young age. This paper explores whether machines can also learn and use variables solely from examples without requiring human pre-engineering. We propose Unification Networks, an end-to-end differentiable neural network approach capable of lifting examples into invariants and using those invariants to solve a given task. The core characteristic of our architecture is soft unification between examples that enables the network to generalise parts of the input into variables, thereby learning invariants. We evaluate our approach on five datasets to demonstrate that learning invariants captures patterns in the data and can improve performance over baselines.
Ricca F, Russo A, 2020, Introduction to the 36th international conference on logic programming special issue II, THEORY AND PRACTICE OF LOGIC PROGRAMMING, Vol: 20, Pages: 815-817, ISSN: 1471-0684
This is the second issue with the selected papers of the 36th International Conference on LogicProgramming (ICLP 2020), held virtually in Rende (CS), Italy, from September 20 to September25, 2020. The two issues contain 27 papers selected from several tracks of the conference program for publication in Theory and Practice of Logic Programming. The preceding issue of thisvolume of the journal contains a detailed editorial by the conference chairs (?), as well as thefollowing fourteen papers selected for publication.
Abu Jabal A, Bertino E, Lobo J, et al., 2020, Polisma - a framework for learning attribute-based access control policies, European Symposium on Research in Computer Security, Publisher: Springer International Publishing, Pages: 523-544, ISSN: 0302-9743
Attribute-based access control (ABAC) is being widely adopted due to its flexibility and universality in capturing authorizations in terms of the properties (attributes) of users and resources. However, specifying ABAC policies is a complex task due to the variety of such attributes. Moreover, migrating an access control system adopting a low-level model to ABAC can be challenging. An approach for generating ABAC policies is to learn them from data, namely from logs of historical access requests and their corresponding decisions. This paper proposes a novel framework for learning ABAC policies from data. The framework, referred to as Polisma, combines data mining, statistical, and machine learning techniques, capitalizing on potential context information obtained from external sources (e.g., LDAP directories) to enhance the learning process. The approach is evaluated empirically using two datasets (real and synthetic). Experimental results show that Polisma is able to generate ABAC policies that accurately control access requests and outperforms existing approaches.
Aspis Y, Broda K, Russo A, et al., 2020, Stable and supported semantics in continuous vector spaces, 17th International Conference on Principles of Knowledge Representation and Reasoning, KR 2020, Publisher: https://proceedings.kr.org/2020/7/, Pages: 58-67
We introduce a novel approach for the computation of stable and supported models of normal logic programs in continuous vector spaces by a gradient-based search method. Specifically, the application of the immediate consequence operator of a program reduct can be computed in a vector space. To do this, Herbrand interpretations of a propositional program are embedded as 0-1 vectors in R and program reducts are represented as matrices in ℝ . Using these representations we prove that the underlying semantics of a normal logic program is captured through matrix multiplication and a differentiable operation. As supported and stable models of a normal logic program can now be seen as fixed points in a continuous space, non-monotonic deduction can be performed using an optimisation process such as Newton's method. We report the results of several experiments using synthetically generated programs that demonstrate the feasibility of the approach and highlight how different parameter values can affect the behaviour of the system. N N×N
Ricca F, Russo A, 2020, Introduction to the 36th International Conference on Logic Programming Special Issue I, THEORY AND PRACTICE OF LOGIC PROGRAMMING, Vol: 20, Pages: 587-592, ISSN: 1471-0684
Casale G, Artac M, van den Heuvel W-J, et al., 2020, RADON: Rational decomposition and orchestration for serverless computing, SICS Software-Intensive Cyber-Physical Systems, Vol: 35, Pages: 77-87, ISSN: 2524-8529
Emerging serverless computing technologies, such as function as a service (FaaS), enable developers to virtualize the internal logic of an application, simplifying the management of cloud-native services and allowing cost savings through billing and scaling at the level of individual functions. Serverless computing is therefore rapidly shifting the attention of software vendors to the challenge of developing cloud applications deployable on FaaS platforms.In this vision paper, we present the research agendaof the RADON project (http://radon-h2020.eu),which aims to develop a model-driven DevOps framework for creating and managing applications based onserverless computing. RADON applications will consist of fine-grained and independent microservices that canefficiently and optimally exploit FaaS and containertechnologies. Our methodology strives to tackle complexity in designing such applications, including the solution of optimal decomposition, the reuse of serverlessfunctions as well as the abstraction and actuation ofevent processing chains, while avoiding cloud vendorlock-in through models.
Lobo J, Bertino E, Russos A, 2020, On security policy migrations, Pages: 179-188
There has been over the past decade a rapid change towards computational environments that are comprised of large and diverse sets of devices, many of them mobile, which can connect in flexible and context-dependent ways. Examples range from networks where we can have communications between powerful cloud centers, to the myriad of simple sensor devices on the IoT. As the management of these dynamic environments becomes ever more complex, we want to propose policy migrations as a methodology to simplify the management of security policies by re-utilizing and re-deploying existing policies as the systems change. We are interested in understanding the challenges raised answering the following question: given a security policy that is being enforced in a particular source computational device, what does it entail to migrate this policy to be enforced in a different target device? Because of the differences between devices and because these devices cannot be seen in isolation but in the context where they are deployed, the meaning of the policy enforced in the source device needs to be re-interpreted and implemented in the context of the target device. The aim of the paper is to present a formal framework to evaluate the appropriateness of the migration.
Gomoluch P, Alrajeh D, Russo A, et al., 2020, Learning neural search policies for classical planning, International Conference on Automated Planning and Scheduling (ICAPS) 2020, Publisher: AAAI, Pages: 522-530, ISSN: 2334-0835
Heuristic forward search is currently the dominant paradigmin classical planning. Forward search algorithms typicallyrely on a single, relatively simple variation of best-first searchand remain fixed throughout the process of solving a plan-ning problem. Existing work combining multiple search tech-niques usually aims at supporting best-first search with anadditional exploratory mechanism, triggered using a hand-crafted criterion. A notable exception is very recent workwhich combines various search techniques using a trainablepolicy. That approach, however, is confined to a discrete ac-tion space comprising several fixed subroutines.In this paper, we introduce a parametrized search algorithmtemplate which combines various search techniques withina single routine. The template’s parameter space defines aninfinite space of search algorithms, including, among others,BFS, local and random search. We then propose a neural ar-chitecture for designating the values of the search parametersgiven the state of the search. This enables expressing neuralsearch policies that change the values of the parameters asthe search progresses. The policies can be learned automat-ically, with the objective of maximizing the planner’s per-formance on a given distribution of planning problems. Weconsider a training setting based on a stochastic optimizationalgorithm known as thecross-entropy method(CEM). Exper-imental evaluation of our approach shows that it is capable offinding effective distribution-specific search policies, outper-forming the relevant baselines.
Law M, Russo A, Bertino E, et al., 2020, FastLAS: scalable inductive logic programming incorporating domain-specific optimisation criteria, The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-2020), Publisher: Association for the Advancement of ArtificialIntelligence, Pages: 2877-2885
Inductive Logic Programming (ILP) systems aim to find a setof logical rules, called a hypothesis, that explain a set of ex-amples. In cases where many such hypotheses exist, ILP sys-tems often bias towards shorter solutions, leading to highlygeneral rules being learned. In some application domains likesecurity and access control policies, this bias may not be de-sirable, as when data is sparse more specific rules that guaran-tee tighter security should be preferred. This paper presents anew general notion of ascoring functionover hypotheses thatallows a user to express domain-specific optimisation criteria.This is incorporated into a new ILP system, calledFastLAS,that takes as input a learning task and a customised scoringfunction, and computes an optimal solution with respect tothe given scoring function. We evaluate the accuracy of Fast-LAS over real-world datasets for access control policies andshow that varying the scoring function allows a user to tar-get domain-specific performance metrics. We also compareFastLAS to state-of-the-art ILP systems, using the standardILP bias for shorter solutions, and demonstrate that FastLASis significantly faster and more scalable.
Tuckey D, Broda K, Russo A, 2020, Towards Structure Learning under the Credal Semantics, ISSN: 1613-0073
We present the Credal-FOIL system for structure learning of probabilistic logic programs under the credal semantics. The credal semantics is a generalisation of the distribution semantics based on the answer set semantics. Our learning approach takes a set of examples that are atoms with target lower and upper bounds probabilities and a background knowledge that can have negative loops. We define accuracy in this setting and learn a set of normal rules without loops that maximises this notion of accuracy. We showcase the system on two proof-of-concept examples.
Furelos-Blanco D, Law M, Jonsson A, et al., 2020, Induction of Subgoal Automata for Reinforcement Learning, 34th AAAI Conference on Artificial Intelligence / 32nd Innovative Applications of Artificial Intelligence Conference / 10th AAAI Symposium on Educational Advances in Artificial Intelligence, Publisher: ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE, Pages: 3890-3897, ISSN: 2159-5399
Verma DC, Bertino E, Russo A, et al., 2020, Policy Based Ensembles for Multi Domain Operations, Conference on Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II Part of SPIE Defense + Commercial Sensing Conference, Publisher: SPIE-INT SOC OPTICAL ENGINEERING, ISSN: 0277-786X
Aspis Y, Broda K, Russo A, et al., 2020, Stable and Supported Semantics in Continuous Vector Spaces, 17th International Conference on Principles of Knowledge Representation and Reasoning (KR and R), Publisher: IJCAI-INT JOINT CONF ARTIF INTELL, Pages: 59-68
White G, Cunnington D, Law M, et al., 2019, A comparison between statistical and symbolic learning approaches for generative policy models, Pages: 1314-1321
Generative Policy Models (GPMs) have been proposed as a method for future autonomous decision making in a distributed, collaborative environment. To learn a GPM, previous policy examples that contain policy features and the corresponding policy decisions are used. Recently, GPMs have been constructed using both symbolic and statistical learning algorithms. In either case, the goal of the learning process is to create a model across a wide range of contexts from which specific policies may be generated in a given context. Empirically, we expect each learning approach to provide certain advantages over the other. This paper assesses the relative performance of each learning approach in order to examine these advantages and disadvantages. Several carefully prepared data sets are used to train a variety of models across different learning algorithms, where models for each learning algorithm are trained with varying amounts of labelled examples. The performance of each model is evaluated across a variety of metrics which indicates the strength of each learning algorithm for the different scenarios presented and the amount of training data provided. Finally, future research directions are outlined to fully realise GPMs in a distributed, collaborative environment.
Cunnington D, Manotas I, Law M, et al., 2019, A generative policy model for connected and autonomous vehicles, 2019 IEEE Intelligent Transportation Systems Conference - ITSC, Publisher: IEEE
Artificial Intelligence is rapidly enhancing human capability by providing support and guidance on a wide variety of tasks. However, one of the main challenges for autonomous systems is effectively managing the decisions and interactions between multiple entities in a dynamic environment. Policies are frequently used in cyber-physical systems to define target goals and constraints, such as maximising security whilst preventing communication to unauthorised systems. In this paper we introduce an approach for learning high-level policy models for future Connected and Autonomous Vehicles (CAVs). Since CAVs are required to operate in complex, safety-critical environments with a wide range of varying contextual conditions, high-level policies can help systems achieve their goals whilst adhering to varying environmental constraints. We present a Generative Policy Model (GPM) that enables a CAV to observe, learn, and adapt high-level policy models using local knowledge shared by related entities in the environment such as other CAVs, when reliable communication to traditional policy management systems may not be available. Within the proposed CAV GPM architecture, we utilise a novel context-free grammar bounded by a set of context-sensitive annotations called Answer Set Grammars (ASGs) and perform an evaluation of CAV policy generation in varying contexts. We also release the CAVPolicy dataset of annotated policies to enable future research in this area.
Russo A, Schuerr A, Wehrheim H, 2019, Editorial, FORMAL ASPECTS OF COMPUTING, Vol: 31, Pages: 457-458, ISSN: 0934-5043
Bertino E, White G, Lobo J, et al., 2019, Generative policies for coalition systems - a symbolic learning framework, 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS), Publisher: IEEE
Policy systems are critical for managing missions and collaborative activities carried out by coalitions involving different organizations. Conventional policy-based management approaches are not suitable for next-generation coalitions that will involve not only humans, but also autonomous computing devices and systems. It is critical that those parties be able to generate and customize policies based on contexts and activities. This paper introduces a novel approach for the autonomic generation of policies by autonomous parties. The framework combines context free grammars, answer set programs, and inductionbased learning. It allows a party to generate its own policies, based on a grammar and some semantic constraints, by learning from examples. The paper also outlines initial experiments in the use of such a symbolic approach and outlines relevant research challenges, ranging from explainability to quality assessment of policies.
Russo A, Schuerr A, 2019, Model-based software quality assurance tools and techniques presented at FASE 2018, INTERNATIONAL JOURNAL ON SOFTWARE TOOLS FOR TECHNOLOGY TRANSFER, Vol: 22, Pages: 1-2, ISSN: 1433-2779
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.