In this paper, we present a hybrid model that combines a neural conversational model and a rule-based graph dialogue system that assists users in scheduling reminders through a chat conversation. The graph based system has high precision and provides a grammatically accurate response but has a low recall. The neural conversation model can cater to a variety of requests, as it generates the responses word by word as opposed to using canned responses. The hybrid system shows significant improvements over the existing baseline system of rule based approach and caters to complex queries with a domain-restricted neural model. Restricting the conversation topic and combination of graph based retrieval system with a neural generative model makes the final system robust enough for a real world application.

The Synthesis of ACT-R and Leabra (SAL) hybrid cognitive architecture is the integration of two theories of cognitive functioning, each itself a highly integrative theory of cognition, ACT-R being predominantly a symbolic production-rule based architecture and Leabra a neural modeling architecture. The combination of the two architectures allows for richer dynamics that take advantage of neural and symbolic aspects and provides mutual constraints that promote convergence towards models that are both neurophysiologically and psychologically valid. We present a hybrid model that makes use of multi-level and multi-system integration to allow an instructed assembly task to be carried out in way that is noise and error robust. Specifically, the model shows how higher-level error recovery routines can interface with lower-level sensory, motor, and error detection processes and result in a robustness to noise and noise-induced errors. Multiple systems and processes operating at multiple levels are recruited to provide a way around the limitations of simpler systems composed of isolated modules that do not allow information to be propagated as easily. The benefits of this approach provide motivation for the adoption of a generally integrated approach to cognitive systems.

The Expressive Intelligence Studio is developing a new approach to freeform conversational interaction in playable media that combines dialogue management, natural language generation (NLG), and natural language understanding. In this paper, we present our method for dialogue generation, which has been fully implemented in a game we are developing called Talk of the Town. Eschewing a traditional NLG pipeline, we take up a novel approach that combines human language expertise with computer generativity. Specifically, this method utilizes a tool that we have developed for authoring context-free grammars (CFGs) whose productions come packaged with explicit metadata. Instead of terminally expanding top-level symbols -- the conventional way of generating from a CFG -- we employ an unusual middle-out procedure that targets mid-level symbols and traverses the grammar by both forward chaining and backward chaining, expanding symbols conditionally by testing against the current game state. In this paper, we present our method, discuss a series of associated authoring patterns, and situate our approach against the few earlier projects in this area.

The use of symbolic knowledge representation and reasoning as a way to resolve the lack of transparency of machine learning classifiers is a research area that lately attracts many researchers. In this work, we use knowledge graphs as the underlying framework providing the terminology for representing explanations for the operation of a machine learning classifier. In particular, given a description of the application domain of the classifier in the form of a knowledge graph, we introduce a novel method for extracting and representing black-box explanations of its operation, in the form of first-order logic rules expressed in the terminology of the knowledge graph.

Extracting information from unstructured text documents is a demanding task, since these documents can have a broad variety of different layouts and a non-trivial reading order, like it is the case for multi-column documents or nested tables. Additionally, many business documents are received in paper form, meaning that the textual contents need to be digitized before further analysis. Nonetheless, automatic detection and capturing of crucial document information like the sender address would boost many companies' processing efficiency. In this work we propose a hybrid approach that combines deep learning with reasoning for finding and extracting addresses from unstructured text documents. We use a visual deep learning model to detect the boundaries of possible address regions on the scanned document images and validate these results by analyzing the containing text using domain knowledge represented as a rule based system.

RuleML RR 2022 aims to bring together rigorous researchers and inventive practitioners, interested in the foundations and applications of rules and reasoning. It provides a forum for stimulating cooperation and cross-fertilization between different communities focused on the research, development, and applications of rule-based systems. We are looking for high-quality papers related to theoretical advances, novel technologies, and artificial intelligence applications that involve rule-based representation and reasoning.

The utilization of digital agents to support crucial decision making is increasing in many industrial scenarios. However, trust in suggestions made by these agents is hard to achieve, though essential for profiting from their application, resulting in a need for explanations for both the decision making process as well as the model itself. For many systems, such as common deep learning black-box models, achieving at least some explainability requires complex post-processing, while other systems profit from being, to a reasonable extent, inherently interpretable. In this paper we propose an easily interpretable rule-based learning system specifically designed and thus especially suited for these scenarios and compare it on a set of regression problems against XCSF, a prominent rule-based learning system with a long research history. One key advantage of our system is that the rules' conditions and which rules compose a solution to the problem are evolved separately. We utilise independent rule fitnesses which allows users to specifically tailor their model structure to fit the given requirements for explainability. We find that the results of SupRB2's evaluation are comparable to XCSF's while allowing easier control of model structure and showing a substantially smaller sensitivity to random seeds and data splits. This increased control aids in subsequently providing explanations for both the training and the final structure of the model.

Due to the fast pace of life and online communications, the prevalence of English and the QWERTY keyboard, people tend to forgo using diacritics, make typographical errors (typos) when typing. Restoring diacritics and correcting spelling is important for proper language use and disambiguation of texts for both humans and downstream algorithms. However, both of these problems are typically addressed separately, i.e., state-of-the-art diacritics restoration methods do not tolerate other typos. In this work, we tackle both problems at once by employing newly-developed ByT5 byte-level transformer models. Our simultaneous diacritics restoration and typos correction approach demonstrates near state-of-the-art performance in 13 languages, reaching >96% of the alpha-word accuracy. We also perform diacritics restoration alone on 12 benchmark datasets with the additional one for the Lithuanian language. The experimental investigation proves that our approach is able to achieve comparable results (>98%) to previously reported despite being trained on fewer data. Our approach is also able to restore diacritics in words not seen during training with >76% accuracy. We also show the accuracies to further improve with longer training. All this shows a great real-world application potential of our suggested methods to more data, languages, and error classes.

While artificial-intelligence-based methods suffer from lack of transparency, rule-based methods dominate in safety-critical systems. Yet, the latter cannot compete with the first ones in robustness to multiple requirements, for instance, simultaneously addressing safety, comfort, and efficiency. Hence, to benefit from both methods they must be joined in a single system. This paper proposes a decision making and control framework, which profits from advantages of both the rule- and machine-learning-based techniques while compensating for their disadvantages. The proposed method embodies two controllers operating in parallel, called Safety and Learned. A rule-based switching logic selects one of the actions transmitted from both controllers. The Safety controller is prioritized every time, when the Learned one does not meet the safety constraint, and also directly participates in the safe Learned controller training. Decision making and control in autonomous driving is chosen as the system case study, where an autonomous vehicle learns a multi-task policy to safely cross an unprotected intersection. Multiple requirements (i.e., safety, efficiency, and comfort) are set for vehicle operation. A numerical simulation is performed for the proposed framework validation, where its ability to satisfy the requirements and robustness to changing environment is successfully demonstrated.

This article was published as a part of the Data Science Blogathon. I hope everyone is doing well. This pandemic provides us with more opportunities to learn new topics through the work-from-home concept, allowing us to devote more time to doing so. This prompted me to consider some mundane but intriguing topics. Yes, we will learn about Unsupervised Machine Learning algorithms in this article, specifically the Associated Rule-based – Apriori algorithm.