The Knowledge Level in Cognitive Architectures: Current Limitations and Possible Developments

In this paper we identify and characterize an analysis of two problematic aspects affecting the representational level of cognitive architectures (CAs), namely: the limited size and the homogeneous typology of the encoded and processed knowledge. We argue that such aspects may constitute not only a technological problem that, in our opinion, should be addressed in order to build articial agents able to exhibit intelligent behaviours in general scenarios, but also an epistemological one, since they limit the plausibility of the comparison of the CAs' knowledge representation and processing mechanisms with those executed by humans in their everyday activities. In the final part of the paper further directions of research will be explored, trying to address current limitations and future challenges

LOGICAL AND PSYCHOLOGICAL PARTITIONING OF MIND: DEPICTING THE SAME MAP?

The aim of this paper is to demonstrate that empirically delimited structures of mind are also differentiable by means of systematic logical analysis. In the sake of this aim, the paper first summarizes Demetriou's theory of cognitive organization and growth. This theory assumes that the mind is a multistructural entity that develops across three fronts: the processing system that constrains processing potentials, a set of specialized structural systems (SSSs) that guide processing within different reality and knowledge domains, and a hypecognitive system that monitors and controls the functioning of all other systems. In the second part the paper focuses on the SSSs, which are the target of our logical analysis, and it summarizes a series of empirical studies demonstrating their autonomous operation. The third part develops the logical proof showing that each SSS involves a kernel element that cannot be reduced to standard logic or to any other SSS. The implications of this analysis for the general theory of knowledge and cognitive development are discussed in the concluding part of the paper

Computational and human-based methods for knowledge discovery over knowledge graphs

The modern world has evolved, accompanied by the huge exploitation of data and information. Daily, increasing volumes of data from various sources and formats are stored, resulting in a challenging strategy to manage and integrate them to discover new knowledge. The appropriate use of data in various sectors of society, such as education, healthcare, e-commerce, and industry, provides advantages for decision support in these areas. However, knowledge discovery becomes challenging since data may come from heterogeneous sources with important information hidden. Thus, new approaches that adapt to the new challenges of knowledge discovery in such heterogeneous data environments are required. The semantic web and knowledge graphs (KGs) are becoming increasingly relevant on the road to knowledge discovery. This thesis tackles the problem of knowledge discovery over KGs built from heterogeneous data sources. We provide a neuro-symbolic artificial intelligence system that integrates symbolic and sub-symbolic frameworks to exploit the semantics encoded in a KG and its structure. The symbolic system relies on existing approaches of deductive databases to make explicit, implicit knowledge encoded in a KG. The proposed deductive database $DS$ can derive new statements to ego networks given an abstract target prediction. Thus, $DS$ minimizes data sparsity in KGs. In addition, a sub-symbolic system relies on knowledge graph embedding (KGE) models. KGE models are commonly applied in the KG completion task to represent entities in a KG in a low-dimensional vector space. However, KGE models are known to suffer from data sparsity, and a symbolic system assists in overcoming this fact. The proposed approach discovers knowledge given a target prediction in a KG and extracts unknown implicit information related to the target prediction. As a proof of concept, we have implemented the neuro-symbolic system on top of a KG for lung cancer to predict polypharmacy treatment effectiveness. The symbolic system implements a deductive system to deduce pharmacokinetic drug-drug interactions encoded in a set of rules through the Datalog program. Additionally, the sub-symbolic system predicts treatment effectiveness using a KGE model, which preserves the KG structure. An ablation study on the components of our approach is conducted, considering state-of-the-art KGE methods. The observed results provide evidence for the benefits of the neuro-symbolic integration of our approach, where the neuro-symbolic system for an abstract target prediction exhibits improved results. The enhancement of the results occurs because the symbolic system increases the prediction capacity of the sub-symbolic system. Moreover, the proposed neuro-symbolic artificial intelligence system in Industry 4.0 (I4.0) is evaluated, demonstrating its effectiveness in determining relatedness among standards and analyzing their properties to detect unknown relations in the I4.0KG. The results achieved allow us to conclude that the proposed neuro-symbolic approach for an abstract target prediction improves the prediction capability of KGE models by minimizing data sparsity in KGs

Integration of Similarity-based and Deductive Reasoning forKnowledge Management

Many disciplines in computer science combine similarity-based and logic-based reasoning. The problem is that the disciplines combine these independently of each other. For example in Case-Based Reasoning (CBR) (Aamodt and Plaza, AI Commun. 7(1):39-59, 1994; Bergmann etal., Künstl. Intell. 23(1):5-11, 2009; Bergmann, Experience Management: Foundation, Development, Methodology and Internet-based Applications, LNAI, vol.2432, Springer, Berlin, 2002), the combination is applied in a sequential manner and not systematically as follows: a set of solutions is retrieved from a case-base using a similarity measure and then deductive reasoning is applied to adapt the retrieved solutions to a query. The aim of this dissertation (Mougouie, Ph.D. thesis, Trier University, Germany, 2009) is to integrate similarity-based and deductive reasoning in a unified manner within the context of Knowledge Management (KM

Superposition as a logical glue

The typical mathematical language systematically exploits notational and logical abuses whose resolution requires not just the knowledge of domain specific notation and conventions, but not trivial skills in the given mathematical discipline. A large part of this background knowledge is expressed in form of equalities and isomorphisms, allowing mathematicians to freely move between different incarnations of the same entity without even mentioning the transformation. Providing ITP-systems with similar capabilities seems to be a major way to improve their intelligence, and to ease the communication between the user and the machine. The present paper discusses our experience of integration of a superposition calculus within the Matita interactive prover, providing in particular a very flexible, "smart" application tactic, and a simple, innovative approach to automation.Comment: In Proceedings TYPES 2009, arXiv:1103.311