Data-driven abductive inference of library specifications

Programmers often leverage data structure libraries that provide useful and reusable abstractions. Modular verification of programs that make use of these libraries naturally rely on specifications that capture important properties about how the library expects these data structures to be accessed and manipulated. However, these specifications are often missing or incomplete, making it hard for clients to be confident they are using the library safely. When library source code is also unavailable, as is often the case, the challenge to infer meaningful specifications is further exacerbated. In this paper, we present a novel data-driven abductive inference mechanism that infers specifications for library methods sufficient to enable verification of the library's clients. Our technique combines a data-driven learning-based framework to postulate candidate specifications, along with SMT-provided counterexamples to refine these candidates, taking special care to prevent generating specifications that overfit to sampled tests. The resulting specifications form a minimal set of requirements on the behavior of library implementations that ensures safety of a particular client program. Our solution thus provides a new multi-abduction procedure for precise specification inference of data structure libraries guided by client-side verification tasks. Experimental results on a wide range of realistic OCaml data structure programs demonstrate the effectiveness of the approach.

Analogical Proportions: Why They Are Useful in AI

This paper presents a survey of researches in analogical reasoning whose building block are analogical proportions which are statements of the form “a is to b as c is to d”. They have been developed in the last twenty years within an Artificial Intelligence perspective. After discussing their formal modeling with the associated inference mechanism, the paper reports the main results obtained in various AI domains ranging from computational linguistics to classification, including image processing, I.Q. tests, case based reasoning, preference learning, and formal concepts analysis. The last section discusses some new theoretical concerns, and the potential of analogical proportions in other areas such as argumentation, transfer learning, and XAI.

Keeping the Breath in Mind: Respiration, Neural Oscillations, and the Free Energy Principle

Scientific interest in the brain and body interactions has been surging in recent years. One fundamental yet underexplored aspect of brain and body interactions is the link between the respiratory and the nervous systems. In this article, we give an overview of the emerging literature on how respiration modulates neural, cognitive and emotional processes. Moreover, we present a perspective linking respiration to the free-energy principle. We frame volitional modulation of the breath as an active inference mechanism in which sensory evidence is recontextualized to alter interoceptive models. We further propose that respiration-entrained gamma oscillations may reflect the propagation of prediction errors from the sensory level up to cortical regions in order to alter higher level predictions. Accordingly, controlled breathing emerges as an easily accessible tool for emotional, cognitive, and physiological regulation.

A MLP-Hedge-Algebras Admittance Controller for Physical Human–Robot Interaction

Recently, the identification of inertia and damping matrices (IIDM) and safety issues, as well as natural cooperation, are interestingly considered to enhance the quality of the physical human–robot interaction (pHRI). To cover all of these issues, advanced admittance controllers, such as those based on fuzzy logic or hedge algebras, have been formulated and successfully applied in several industrial problems. However, the inference mechanism of those kinds of controllers causes the discreteness of the super surface describing the input–output relationship in the Cartesian coordinates. As a consequence, the quality of the safe-natural cooperation between humans and robots is negatively affected. This paper presents an alternative admittance controller for pHRI by using a combination of hedge algebras and multilayer perceptron neural network (MLP), whose purpose is to create a more accurate inference mechanism for the admittance controller. To our best knowledge, this is the first time that such a neural network is considered for the inference mechanism of hedge algebras and also the first time that such an admittance controller is used for pHRI. The proposed admittance controller is verified on a teaching task using a 6-DOF manipulator. Experimental results have shown that the proposed method provides better cooperation compared with previous methods.

Execution of Knowledge-Intensive Processes by Utilizing Ontology-Based Reasoning

Modeling and executing knowledge-intensive processes (KiPs) are challenging with state-of-the-art approaches, and the specific demands of KiPs are the subject of ongoing research. In this context, little attention has been paid to the ontology-driven combination of data-centric and semantic business process modeling, which finds additional motivation by enabling the division of labor between humans and artificial intelligence. Such approaches have characteristics that could allow support for KiPs based on the inferencing capabilities of reasoners. We confirm this as we show that reasoners can infer the executability of tasks based on a currently researched ontology- and data-driven business process model (ODD-BP model). Further support for KiPs by the proposed inference mechanism results from its ability to infer the relevance of tasks, depending on the extent to which their execution would contribute to process progress. Besides these contributions along with the execution perspective (start-to-end direction), we will also show how our approach can help to reach specific process goals by inferring the relevance of process elements regarding their support to achieve such goals (end-to-start direction). The elements with the most valuable process progress can be identified in the intersection of both, the execution and goal perspective. This paper will introduce this new approach and verifies its practicability with an evaluation of a KiP in the field of emergency call centers.

Detecting the ecological footprint of selection

The structure of communities is influenced by many processes, both ecological and evolutionary, but these processes are hard to distinguish from available data. The aim of this work is to distinguish the ecological footprint of selection from that of neutral processes that are invariant to species identity. To do this, we build on existing theory to produce a new mechanistic model of community structure incorporating ecology and evolution. We base our work on "massive eco-evolutionary synthesis simulations" (or MESS), which uses information from three biodiversity axes - species richness and abundance; population genetic diversity; and trait variation - to distinguish between processes with a mechanistic model. We added a new form of competition to MESS that explicitly compares the traits of each pair of individuals and allows us to distinguish between inter- and intra-specific competition. We find that this addition is essential to properly detect and characterise selection and it yields different results to the existing simpler model that only compares species' traits to the community mean. Neutral forces receive much less support from systems where trait data is incorporated into the inference mechanism.

Understanding Narrative-Driven Intergroup Conflict Dynamics Using Computational Cognitive Coherence Maps

Humans make sense of the world through narratives. Therefore, adversaries often use conflict-sustaining narratives to maintain dominance and delegitimize the actions of the rivals. To better understand narratives’ role and influence in such intractable conflicts, a computational framework and methodology are introduced. The computational cognitive model and its underlying inference mechanism allow analysts to simulate and analyze narratives in relation to opposing narratives. The ability to simulate the interaction of adversarial stories with a set of micronarratives shared by members of a group opens new avenues to counter conflict-sustaining narratives. The methodology and its application to a concrete conflict scenario demonstrate how to conduct simulation-driven exploratory analysis over a complex adaptive narrative space to discern how narratives are matched to unfolding events and how they can be used to facilitate favorable change.

Devising a technology for managing outsourcing IT-projects with the application of fuzzy logic

An outsourcing IT project management model has been developed. The proposed model features taking into account the specifics of project management processes at outsourcing IT companies in terms of the uncertainty of the external and internal environment of their operation. The model is based on the stage-gate project management framework with fuzzy logic tools. The proposed modification of the fuzzy inference mechanism makes it possible to refuse to save the intermediate results which reduce the load on the database and create the possibility of using semantic networks. The technology of expert consultations was demonstrated by the example of decision-making regarding the assessment of the current status of the IT projects accepted by the outsourcing company for development. Dynamic nature and cyclical management of the portfolio of IT projects involves constant monitoring of the results of implementation with an appropriate regular portfolio reforming. The model was developed to improve the efficiency of the software development sub-process and minimize the negative consequences of financial dependence on the customer. The application software developed on the basis of the model of management of outsourcing IT projects and modification of the fuzzy inference mechanism has found practical application and was implemented in the computational practice of HYS Enterprise B.V. outsourcing IT company. Testing of the program shell has shown positive results in the course of solving the tasks peculiar to concrete stages of IT project management. The proposed structure and composition of the fuzzy knowledgebase of the expert shell are quite typical in terms of IT outsourcing problems. It is expedient to use the developed model at outsourcing IT companies in the process of project portfolio management