IJCAI-ECAI Workshop “Interactions between Analogical Reasoning and Machine Learning” (IARML 2022)

International audienceAnalogical reasoning is a remarkable capability of human reasoning, used to solve hard reasoning tasks. It consists in transferring knowledge from a source domain to a different, but somewhat similar, target domain by relying simultaneously on similarities and dissimilarities. In particular, analogical proportions, i.e., statements of the form “A is to B as C is to D", are the basis of analogical inference. Analogical reasoning is pertaining to case-based reasoning and it has contributed to multiple machine learning tasks such as classification, decision making, and automatic translation with competitive results. Moreover, analogical extrapolation can support dataset augmentation (analogical extension) for model learning,especially in environments with few labeled examples. Conversely, advanced neural techniques, such as representation learning, enabled efficient approaches to detecting and solving analogies in domains where symbolic approaches had shown their limits. However, recent approaches using deep learning architectures remain task and domain specific, and strongly rely on ad-hoc representations of objects, i.e., tailor made embeddings.The first workshop Interactions between Analogical Reasoning and Machine Learning (IARML) was hosted by the 31st International Joint Conference on Artificial Intelligence and the 25th European Conference on Artificial Intelligence (IJCAI-ECAI 2022). It brought together AI researchers at the cross roads of machine learning, cognitive sciences and knowledge representation and reasoning, who are interested by the various applications of analogical reasoning in machine learning or, conversely, of machine learning techniques to improve analogical reasoning. The IARML workshop aims to bridge gaps between different AI communities, including case-based reasoning, deep learning and neuro-symbolic machine learning. The workshop welcomed submissions of research papers on all topics at the intersection of analogical reasoning and machine learning. The submissions were subjected to a strict double-blind reviewing process that resulted in the selection of six original contributions and two invited talks, in addition to the two plenary keynote talks

Case-based reasoning combined with statistics for diagnostics and prognosis

Many approaches used for diagnostics today are based on a precise model. This excludes diagnostics of many complex types of machinery that cannot be modelled and simulated easily or without great effort. Our aim is to show that by including human experience it is possible to diagnose complex machinery when there is no or limited models or simulations available. This also enables diagnostics in a dynamic application where conditions change and new cases are often added. In fact every new solved case increases the diagnostic power of the system. We present a number of successful projects where we have used feature extraction together with case-based reasoning to diagnose faults in industrial robots, welding, cutting machinery and we also present our latest project for diagnosing transmissions by combining Case-Based Reasoning (CBR) with statistics. We view the fault diagnosis process as three consecutive steps. In the first step, sensor fault signals from machines and/or input from human operators are collected. Then, the second step consists of extracting relevant fault features. In the final diagnosis/prognosis step, status and faults are identified and classified. We view prognosis as a special case of diagnosis where the prognosis module predicts a stream of future features

The Challenge of Unifying Semantic and Syntactic Inference Restrictions

While syntactic inference restrictions don't play an important role for SAT, they are an essential reasoning technique for more expressive logics, such as first-order logic, or fragments thereof. In particular, they can result in short proofs or model representations. On the other hand, semantically guided inference systems enjoy important properties, such as the generation of solely non-redundant clauses. I discuss to what extend the two paradigms may be unifiable

A Narrative Approach to Human-Robot Interaction Prototyping for Companion Robots

© 2020 Kheng Lee Koay et al., published by De Gruyter This work is licensed under the Creative Commons Attribution 4.0 International License. https://creativecommons.org/licenses/by/4.0/This paper presents a proof of concept prototype study for domestic home robot companions, using a narrative-based methodology based on the principles of immersive engagement and fictional enquiry, creating scenarios which are inter-connected through a coherent narrative arc, to encourage participant immersion within a realistic setting. The aim was to ground human interactions with this technology in a coherent, meaningful experience. Nine participants interacted with a robotic agent in a smart home environment twice a week over a month, with each interaction framed within a greater narrative arc. Participant responses, both to the scenarios and the robotic agents used within them are discussed, suggesting that the prototyping methodology was successful in conveying a meaningful interaction experience.Peer reviewe

A distributed alerting service for open digital library software

Alerting for Digital Libraries (DL) is an important and useful feature for the library users. To date, two independent services and a few publisher-hosted proprietary services have been developed. Here, we address the problem of integrating alerting as functionality into open source software for distributed digital libraries. DL software is one application out of many that constitute so-called meta-software: software where its installation determines the properties of the actual running system (here: the Digital Library system). For this type of application, existing alerting solutions are insufficient; new ways have to be found for supporting a fragmented network of distributed digital library servers. We propose the design and usage of a distributed Directory Service. This paper also introduces our hybrid approach using two networks and a combination of different distributed routing strategies for event filtering

Resilience in Numerical Methods: A Position on Fault Models and Methodologies

Future extreme-scale computer systems may expose silent data corruption (SDC) to applications, in order to save energy or increase performance. However, resilience research struggles to come up with useful abstract programming models for reasoning about SDC. Existing work randomly flips bits in running applications, but this only shows average-case behavior for a low-level, artificial hardware model. Algorithm developers need to understand worst-case behavior with the higher-level data types they actually use, in order to make their algorithms more resilient. Also, we know so little about how SDC may manifest in future hardware, that it seems premature to draw conclusions about the average case. We argue instead that numerical algorithms can benefit from a numerical unreliability fault model, where faults manifest as unbounded perturbations to floating-point data. Algorithms can use inexpensive "sanity" checks that bound or exclude error in the results of computations. Given a selective reliability programming model that requires reliability only when and where needed, such checks can make algorithms reliable despite unbounded faults. Sanity checks, and in general a healthy skepticism about the correctness of subroutines, are wise even if hardware is perfectly reliable.Comment: Position Pape