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Statistical Relational Artificial Intelligence: From Distributions through Actions to Optimization

KUNSTLICHE INTELLIGENZ 2015年第4期29卷 363-368页

作者： Kersting, Kristian Natarajan, Sriraam TU Dortmund Univ Dortmund Germany Indiana Univ Bloomington IN USA

Statistical Relational AI-the science and engineering of making intelligent machines acting in noisy worlds composed of objects and relations among the objects-is currently motivating a lot of new AI research and has tremendous theoretical and practical implications. Theoretically, combining logic and probability in a unified representation and building general-purpose reasoning tools for it has been the dream of AI, dating back to the late 1980s. Practically, successful statistical relational AI tools enable new applications in several large, complex realworld domains including those involving big data, natural text, social networks, the web, medicine and robotics, among others. Such domains are often characterized by rich relational structure and large amounts of uncertainty. logic helps to faithfully model the former while probability helps to effectively manage the latter. Our intention here is to give a brief (and necessarily incomplete) overview and invitation to the emerging field of Statistical Relational AI from the perspective of acting optimally and learning to act.

关键词： inductive logic programming logical Query Markov Decision Process Probabilistic Graphical Model Situation Calculus

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DockStar: a novel ILP-based integrative method for structural modeling of multimolecular protein complexes

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BIOINFORMATICS 2015年第17期31卷 2801-2807页

作者： Amir, Naama Cohen, Dan Wolfson, Haim J. Tel Aviv Univ Blavatnik Sch Comp Sci IL-69978 Tel Aviv Israel

Motivation: Atomic resolution modeling of large multimolecular assemblies is a key task in Structural Cell Biology. Experimental techniques can provide atomic resolution structures of single proteins and small complexes, or low resolution data of large multimolecular complexes. Results: We present a novel integrative computational modeling method, which integrates both low and high resolution experimental data. The algorithm accepts as input atomic resolution structures of the individual subunits obtained from X-ray, NMR or homology modeling, and interaction data between the subunits obtained from mass spectrometry. The optimal assembly of the individual subunits is formulated as an Integer Linear programming task. The method was tested on several representative complexes, both in the bound and unbound cases. It placed correctly most of the subunits of multimolecular complexes of up to 16 subunits and significantly outperformed the CombDock and Haddock multimolecular docking methods.

关键词： inductive logic programming PROTEIN structure STRUCTURAL models MOLECULAR self-assembly CYTOLOGY DOCKING of molecules

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Teaching the Teacher: Tutoring SimStudent Leads to More Effective Cognitive Tutor Authoring

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INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION 2015年第1期25卷 1-34页

作者： Matsuda, Noboru Cohen, William W. Koedinger, Kenneth R. Carnegie Mellon Univ Human Comp Interact Inst 5000 Forbes Ave Pittsburgh PA 15213 USA Carnegie Mellon Univ Machine Learning Dept Pittsburgh PA 15213 USA

SimStudent is a machine-learning agent initially developed to help novice authors to create cognitive tutors without heavy programming. Integrated into an existing suite of software tools called Cognitive Tutor Authoring Tools (CTAT), SimStudent helps authors to create an expert model for a cognitive tutor by tutoring SimStudent on how to solve problems. There are two different ways to author an expert model with SimStudent. In the context of Authoring by Tutoring, the author interactively tutors SimStudent by posing problems to SimStudent, providing feedback on the steps performed by SimStudent, and also demonstrating steps as a response to SimStudent's hint requests when SimStudent cannot perform steps correctly. In the context of Authoring by Demonstration, the author demonstrates solution steps, and SimStudent attempts to induce underlying domain principles by generalizing those worked-out examples. We conducted evaluation studies to investigate which authoring strategy better facilitates authoring and found two key results. First, the expert model generated with Authoring by Tutoring is better and has higher accuracy while maintaining the same level of completeness than the one generated with Authoring by Demonstration. The reason for this better accuracy is that the expert model generated by tutoring benefits from negative feedback provided for SimStudent's incorrect production applications. Second, authoring by Tutoring requires less time than Authoring by Demonstration. This enhanced authoring efficiency is partially because (a) when Authoring by Demonstration, the author needs to test the quality of the expert model, whereas the formative assessment of the expert model is done naturally by observing SimStudent's performance when Authoring by Tutoring, and (b) the number of steps that need to be demonstrated during tutoring decreases as learning progresses.

关键词： Intelligent authoring system Machine learning programming by demonstration inductive logic programming Cognitive tutor authoring tools SimStudent

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A Variant of Connected Dominating Set in Unit Disk Graphs for Applications in Communication Networks

A Variant of Connected Dominating Set in Unit Disk Graphs fo...

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IEEE INTERNATIONAL CONFERENCE ON ELECTRO/INFORMATION TECHNOLOGY (EIT)

作者： Djenouri, Djamel Bagaa, Miloud CERIST Res Ctr Algiers Algeria

ISBN: (纸本)9781479988020

This paper considers a variant of the connected dominating set (CDS) problem in a unit disk graph G = (V, E). The considered problem consists in minimizing the number of CDS vertices that belong to a subset V' subset of V. As far as we know, this problem has not been treated in the literature. Nevertheless, its resolution would be useful in many communication network applications, such as the selection of relay nodes in heterogenous wireless ad hoc networks where only a subset of powerful nodes (e.g., energy or memory rich nodes) may form the network backbone act as relays, or where it is preferable to select relays from these nodes and minimize the number of non-powerful nodes that act as relays. Replacement of non-powerful nodes might be necessary either at the initialization (after deployment), or during the network lifetime, which justifies the need to minimize their number. The problem is first modeled and reduced to the minimum weighted connected dominating set (WCDS) problem in a vertex weighted graph, and then it is resolved by taking advantage of the simple form of the weight function using integer linear programming (ILP). A heuristic is also proposed for large scale resolution. Simulation results confirms closeness of the proposed heuristic to the optimal solution obtained by the ILP, and scalability of the heuristic.

关键词： networks(communications) relay Ganglia network backbone Mobile ad hoc networks WEIGHTING FUNCTIONS Heuristics inductive logic programming Telecommunications disks Intimacy initialization

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A Cloud-based Architecture for Network Attack Signature Learning 7

A Cloud-based Architecture for Network Attack Signature Lear...

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7th International Conference on New Technologies, Mobility and Security (NTMS)

作者： Hamdi, Omessaad Mbaye, Maissa Krief, Francine Univ Carthage SUPCOM Carthage Tunisia Gaston Berger Univ LANI St Louis Senegal Univ Bordeaux LaBRI Bordeaux France

ISBN: (纸本)9781479987849

Intrusion Detection System (IDS) is an essential component of the network security infrastructure. It detects malicious activities by monitoring network traffic. There are two main classes of IDS: the anomaly-based IDS and signature-based IDS. An important challenge, for signature-based IDS, is automating attack signature writing from traffic logs, which can be very hard to be established for human administrator. In this paper, we propose a solution addressing this challenge. We propose cloud-based signature learning service using inductive logic programming (ILP). Learning service generates rule describing properties shared by packets labelled as malicious and that do not cover normal packets. The system uses a background knowledge composed of predicates used to describe network attack signature. The cloud architecture of our IDS enables it to have specialized nodes. Preliminary experimentations show that the proposed system is able to reproduce automatically SNORT signature.

关键词： cloud computing inductive logic programming Intrusion Detection System attack signature

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A Multi-Relational Model for Depression Relapse in Patients with Bipolar Disorder 15

A Multi-Relational Model for Depression Relapse in Patients ...

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15th World Congress on Health and Biomedical Informatics (MEDINFO)

作者： Salvini, Rogerio Dias, Rodrigo da Silva Lafer, Beny Dutra, Ines Univ Fed Goias Inst Informat Goiania Go Brazil Univ Sao Paulo Med Sch Bipolar Res Program Dept & Inst Psychiat Sao Paulo Brazil Univ Porto CRACS Porto Portugal Univ Porto INESC TEC Porto LA Porto Portugal Univ Wisconsin Madison Madison WI USA

ISBN: (纸本)9781614995647;9781614995630

Bipolar Disorder (BD) is a chronic and disabling disease that usually appears around 20 to 30 years old. Patients who suffer with BD may struggle for years to achieve a correct diagnosis, and only 50% of them generally receive adequate treatment. In this work we apply a machine learning technique called inductive logic programming (ILP) in order to model relapse and no-relapse patients in a first attempt in this area to improve diagnosis and optimize psychiatrists' time spent with patients. We use ILP because it is well suited for our multi-relational dataset and because a human can easily interpret the logical rules produced. Our classifiers can predict relapse cases with 92% Recall and no-relapse cases with 73% Recall. The rules and variable theories generated by ILP reproduce some findings from the scientific literature. The generated multi-relational models can be directly interpreted by clinicians and researchers, and also open space to research biological mechanisms and interventions.

关键词： Bipolar Disorder Depression Relapse Multi-relational Model inductive logic programming Machine Learning

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Learning Multi-Valued Biological Models with Delayed Influence from Time-Series Observations 14

Learning Multi-Valued Biological Models with Delayed Influen...

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IEEE 14th International Conference on Machine Learning and Applications ICMLA

作者： Ribeiro, Tony Magnin, Morgan Inoue, Katsumi Sakama, Chiaki SOKENDAI Chiyoda Ku 2-1-2 Hitotsubashi Tokyo 1018430 Japan Ecole Cent Nantes Inst Rech Commun & Cybernet Nantes IRCCyN F-44321 Nantes France Natl Inst Informat Chiyoda Ku Tokyo 1018430 Japan Wakayama Univ Wakayama 6408510 Japan

ISBN: (纸本)9781509002870

Delayed effects are important in modeling biological systems, and timed Boolean networks have been proposed for such a framework. Yet it is not an easy task to design such Boolean models with delays precisely. Recently, an attempt to learn timed Boolean networks has been made in [1] in the framework of learning state transition rules from time-series data. However, this approach still has two limitations: (1) The maximum delay has to be given as input to the algorithm;(2) The possible value of each state is assumed to be Boolean, i.e., two-valued. In this paper, we extend the previous learning mechanism to overcome these limitations. We propose an algorithm to learn multi-valued biological models with delayed influence by automatically tuning the delay. The delay is determined so as to minimally explain the necessary influences. The merits of our approach is then verified on benchmarks coming from the DREAM4 challenge.

关键词： dynamical systems multi-valued models Boolean networks delay Markov(k) learning from interpretation transition inductive logic programming

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inductive Learning Using Constraint-Driven Bias 1

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24th International Conference on inductive logic programming (ILP)

作者： Athakravi, Duangtida Alrajeh, Dalal Broda, Krysia Russo, Alessandra Satoh, Ken Univ London Imperial Coll Sci Technol & Med London England Natl Inst Informat Tokyo Japan

ISBN: (数字)9783319237084

ISBN: (纸本)9783319237084;9783319237077

Heuristics such as the Occam Razor's principle have played a significant role in reducing the search for solutions of a learning task, by giving preference to most compressed hypotheses. For some application domains, however, these heuristics may become too weak and lead to solutions that are irrelevant or inapplicable. This is particularly the case when hypotheses ought to conform, within the scope of a given language bias, to precise domain-dependent structures. In this paper we introduce a notion of inductive learning through constraint-driven bias that addresses this problem. Specifically, we propose a notion of learning task in which the hypothesis space, induced by its mode declaration, is further constrained by domain-specific denials, and acceptable hypotheses are (brave inductive) solutions that conform with the given domain-specific constraints. We provide an implementation of this new learning task by extending the ASPAL learning approach and leveraging on its meta-level representation of hypothesis space to compute acceptable hypotheses. We demonstrate the usefulness of this new notion of learning by applying it to two class of problems - automated revision of software system goals models and learning of stratified normal programs.

关键词： inductive logic programming Answer set programming Meta-level constraints

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Looking at the Bottom and the Top: a Hybrid logical Relational Learning System Based on Answer Sets 4

Looking at the Bottom and the Top: a Hybrid Logical Relation...

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4th Brazilian Conference on Intelligent Systems (BRACIS)

作者： Guimaraes, Victor Paes, Aline UFF Dept Comp Sci Rio De Janeiro Brazil

ISBN: (纸本)9781509000166

Traditional machine learning algorithms require a dataset composed of homogeneous objects, randomly sampled from a single relation. However, real world tasks such as link prediction and entity resolution, require the representation of multiple relations, heterogeneous and structured data. inductive logic programming (ILP) is a subarea of machine learning that induces structured hypotheses from multi-relational examples and background knowledge (BK) represented as logical clauses. With a few exceptions, most of the systems developed in ILP induce Horn-clauses and uses Prolog as their baseline inference engine. However, the recent development of efficient Answer Set programming solvers points out that these can be a viable option to be the reasoning component of ILP systems, especially to address nonmonotonic reasoning. In this paper, we present dASBoT, a system that is capable of inducing extended normal rules mined from answer sets yielded from the examples and the BK. We show empirical evidence that dASBoT can support the task of relational identification by learning rules in three link prediction and two entity resolution tasks.

关键词： Answer Set programming inductive logic programming Relational Machine Learning Extended logic Programs

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Characterizing Predicate Arity and Spatial Structure for inductive Learning of Game Rules 13

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13th European Conference on Computer Vision (ECCV)

作者： Dwibedi, Debidatta Mukerjee, Amitabha Indian Inst Technol Kanpur 208016 Uttar Pradesh India

ISBN: (纸本)9783319161815;9783319161808

Where do the predicates in a game ontology come from? We use RGBD vision to learn a) the spatial structure of a board, and b) the number of parameters in a move or transition. These are used to define state-transition predicates for a logical description of each game state. Given a set of videos for a game, we use an improved 3D multi-object tracking to obtain the positions of each piece in games such as 4-peg solitaire or Towers of Hanoi. The spatial positions occupied by pieces over the entire game is clustered, revealing the structure of the board. Each frame is represented as a Semantic Graph with edges encoding spatial relations between pieces. Changes in the graphs between game states reveal the structure of a "move". Knowledge from spatial structure and semantic graphs is mapped to FOL descriptions of the moves and used in an inductive logic framework to infer the valid moves and other rules of the game. Discovered predicate structures and induced rules are demonstrated for several games with varying board layouts and move structures.

关键词： Predicate discovery Spatial structure discovery Game rule learning Semantic graphs Multi-object tracking Vision-based ontology discovery inductive logic programming Kinect

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