53 research outputs found
On deducing causality in metabolic networks
© 2008 Bodei et al.; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution Licens
Proceedings of the IJCAI-09 Workshop on Nonmonotonic Reasoning, Action and Change
Copyright in each article is held by the authors.
Please contact the authors directly for permission to reprint or use this material in any form for any purpose.The biennial workshop on Nonmonotonic Reasoning, Action
and Change (NRAC) has an active and loyal community.
Since its inception in 1995, the workshop has been held seven
times in conjunction with IJCAI, and has experienced growing
success. We hope to build on this success again this eighth
year with an interesting and fruitful day of discussion.
The areas of reasoning about action, non-monotonic reasoning
and belief revision are among the most active research
areas in Knowledge Representation, with rich inter-connections
and practical applications including robotics, agentsystems,
commonsense reasoning and the semantic web.
This workshop provides a unique opportunity for researchers
from all three fields to be brought together at a single forum
with the prime objectives of communicating important recent
advances in each field and the exchange of ideas. As these
fundamental areas mature it is vital that researchers maintain
a dialog through which they can cooperatively explore
common links. The goal of this workshop is to work against
the natural tendency of such rapidly advancing fields to drift
apart into isolated islands of specialization.
This year, we have accepted ten papers authored by a diverse
international community. Each paper has been subject
to careful peer review on the basis of innovation, significance
and relevance to NRAC. The high quality selection of work
could not have been achieved without the invaluable help of
the international Program Committee.
A highlight of the workshop will be our invited speaker
Professor Hector Geffner from ICREA and UPF in Barcelona,
Spain, discussing representation and inference in modern
planning. Hector Geffner is a world leader in planning,
reasoning, and knowledge representation; in addition to his
many important publications, he is a Fellow of the AAAI, an
associate editor of the Journal of Artificial Intelligence Research
and won an ACM Distinguished Dissertation Award
in 1990
Proceedings of the 11th Workshop on Nonmonotonic Reasoning
These are the proceedings of the 11th Nonmonotonic Reasoning Workshop. The aim of this series is to bring together active researchers in the broad area of nonmonotonic reasoning, including belief revision, reasoning about actions, planning, logic programming, argumentation, causality, probabilistic and possibilistic approaches to KR, and other related topics. As part of the program of the 11th workshop, we have assessed the status of the field and discussed issues such as: Significant recent achievements in the theory and automation of NMR; Critical short and long term goals for NMR; Emerging new research directions in NMR; Practical applications of NMR; Significance of NMR to knowledge representation and AI in general
Efficient instance and hypothesis space revision in Meta-Interpretive Learning
Inductive Logic Programming (ILP) is a form of Machine Learning. The goal of ILP is to induce hypotheses, as logic programs, that generalise training examples. ILP is characterised by a high expressivity, generalisation ability and interpretability. Meta-Interpretive Learning (MIL) is a state-of-the-art sub-field of ILP. However, current MIL approaches have limited efficiency: the sample and learning complexity respectively are polynomial and exponential in the number of clauses. My thesis is that improvements over the sample and learning complexity can be achieved in MIL through instance and hypothesis space revision. Specifically, we investigate 1) methods that revise the instance space, 2) methods that revise the hypothesis space and 3) methods that revise both the instance and the hypothesis spaces for achieving more efficient MIL.
First, we introduce a method for building training sets with active learning in Bayesian MIL. Instances are selected maximising the entropy. We demonstrate this method can reduce the sample complexity and supports efficient learning of agent strategies. Second, we introduce a new method for revising the MIL hypothesis space with predicate invention. Our method generates predicates bottom-up from the background knowledge related to the training examples. We demonstrate this method is complete and can reduce the learning and sample complexity. Finally, we introduce a new MIL system called MIGO for learning optimal two-player game strategies. MIGO learns from playing: its training sets are built from the sequence of actions it chooses. Moreover, MIGO revises its hypothesis space with Dependent Learning: it first solves simpler tasks and can reuse any learned solution for solving more complex tasks. We demonstrate MIGO significantly outperforms both classical and deep reinforcement learning. The methods presented in this thesis open exciting perspectives for efficiently learning theories with MIL in a wide range of applications including robotics, modelling of agent strategies and game playing.Open Acces
Graph Structures for Knowledge Representation and Reasoning
This open access book constitutes the thoroughly refereed post-conference proceedings of the 6th International Workshop on Graph Structures for Knowledge Representation and Reasoning, GKR 2020, held virtually in September 2020, associated with ECAI 2020, the 24th European Conference on Artificial Intelligence. The 7 revised full papers presented together with 2 invited contributions were reviewed and selected from 9 submissions. The contributions address various issues for knowledge representation and reasoning and the common graph-theoretic background, which allows to bridge the gap between the different communities
At the Biological Modeling and Simulation Frontier
We provide a rationale for and describe examples of synthetic modeling and simulation (M&S) of biological systems. We explain how synthetic methods are distinct from familiar inductive methods. Synthetic M&S is a means to better understand the mechanisms that generate normal and disease-related phenomena observed in research, and how compounds of interest interact with them to alter phenomena. An objective is to build better, working hypotheses of plausible mechanisms. A synthetic model is an extant hypothesis: execution produces an observable mechanism and phenomena. Mobile objects representing compounds carry information enabling components to distinguish between them and react accordingly when different compounds are studied simultaneously. We argue that the familiar inductive approaches contribute to the general inefficiencies being experienced by pharmaceutical R&D, and that use of synthetic approaches accelerates and improves R&D decision-making and thus the drug development process. A reason is that synthetic models encourage and facilitate abductive scientific reasoning, a primary means of knowledge creation and creative cognition. When synthetic models are executed, we observe different aspects of knowledge in action from different perspectives. These models can be tuned to reflect differences in experimental conditions and individuals, making translational research more concrete while moving us closer to personalized medicine
An integrative workflow to study large-scale biochemical networks
I propose an integrative workflow to study large-scale biochemical networks by combining omics data, network structure and dynamical analysis to unravel disease mechanisms. Using the workflow, I identified core regulatory networks from the E2F1 network underlying EMT in bladder and breast cancer and detected disease signatures and drug targets, which were experimentally validated. Further, I developed a hybrid modeling framework that combines ODE- with logical-models to analyze the dynamics of large-scale non-linear systems. This thesis is a contribution to interdisciplinary cancer research
Biomedical applications of belief networks
Biomedicine is an area in which computers have long been expected to play a significant
role. Although many of the early claims have proved unrealistic, computers are gradually
becoming accepted in the biomedical, clinical and research environment. Within these
application areas, expert systems appear to have met with the most resistance, especially
when applied to image interpretation.In order to improve the acceptance of computerised decision support systems it is
necessary to provide the information needed to make rational judgements concerning
the inferences the system has made. This entails an explanation of what inferences
were made, how the inferences were made and how the results of the inference are to
be interpreted. Furthermore there must be a consistent approach to the combining of
information from low level computational processes through to high level expert analyses.nformation from low level computational processes through to high level expert analyses.
Until recently ad hoc formalisms were seen as the only tractable approach to reasoning
under uncertainty. A review of some of these formalisms suggests that they are less
than ideal for the purposes of decision making. Belief networks provide a tractable way
of utilising probability theory as an inference formalism by combining the theoretical
consistency of probability for inference and decision making, with the ability to use the
knowledge of domain experts.nowledge of domain experts.
The potential of belief networks in biomedical applications has already been recogÂŹ
nised and there has been substantial research into the use of belief networks for medical
diagnosis and methods for handling large, interconnected networks. In this thesis the use
of belief networks is extended to include detailed image model matching to show how,
in principle, feature measurement can be undertaken in a fully probabilistic way. The
belief networks employed are usually cyclic and have strong influences between adjacent
nodes, so new techniques for probabilistic updating based on a model of the matching
process have been developed.An object-orientated inference shell called FLAPNet has been implemented and used
to apply the belief network formalism to two application domains. The first application is
model-based matching in fetal ultrasound images. The imaging modality and biological
variation in the subject make model matching a highly uncertain process. A dynamic,
deformable model, similar to active contour models, is used. A belief network combines
constraints derived from local evidence in the image, with global constraints derived from
trained models, to control the iterative refinement of an initial model cue.In the second application a belief network is used for the incremental aggregation of
evidence occurring during the classification of objects on a cervical smear slide as part of
an automated pre-screening system. A belief network provides both an explicit domain
model and a mechanism for the incremental aggregation of evidence, two attributes
important in pre-screening systems.Overall it is argued that belief networks combine the necessary quantitative features
required of a decision support system with desirable qualitative features that will lead
to improved acceptability of expert systems in the biomedical domain
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