Learning probabilistic interaction models

We live in a multi-modal world; therefore it comes as no surprise that the human brain is tailored for the integration of multi-sensory input. Inspired by the human brain, the multi-sensory data is used in Artificial Intelligence (AI) for teaching different concepts to computers. Autonomous Agents (AAs) are AI systems that sense and act autonomously in complex dynamic environments. Such agents can build up Self-Awareness (SA) by describing their experiences through multi-sensorial information with appropriate models and correlating them incrementally with the currently perceived situation to continuously expand their knowledge. This thesis proposes methods to learn such awareness models for AAs. These models include SA and situational awareness models in order to perceive and understand itself (self variables) and its surrounding environment (external variables) at the same time. An agent is considered self-aware when it can dynamically observe and understand itself and its surrounding through different proprioceptive and exteroceptive sensors which facilitate learning and maintaining a contextual representation by processing the observed multi-sensorial data. We proposed a probabilistic framework for generative and descriptive dynamic models that can lead to a computationally efficient SA system. In general, generative models facilitate the prediction of future states while descriptive models enable to select the representation that best fits the current observation. The proposed framework employs a Probabilistic Graphical Models (PGMs) such as Dynamic Bayesian Networks (DBNs) that represent a set of variables and their conditional dependencies. Once we obtain this probabilistic representation, the latter allows the agent to model interactions between itself, as observed through proprioceptive sensors, and the environment, as observed through exteroceptive sensors. In order to develop an awareness system, not only an agent needs to recognize the normal states and perform predictions accordingly, but also it is necessary to detect the abnormal states with respect to its previously learned knowledge. Therefore, there is a need to measure anomalies or irregularities in an observed situation. In this case, the agent should be aware that an abnormality (i.e., a non-stationary condition) never experienced before, is currently present. Due to our specific way of representation, which makes it possible to model multi-sensorial data into a uniform interaction model, the proposed work not only improves predictions of future events but also can be potentially used to effectuate a transfer learning process where information related to the learned model can be moved and interpreted by another body

Executable Model Synthesis and Property Validation for Message Sequence Chart Specifications

Message sequence charts (MSC’s) are a formal language for the specification of scenarios in concurrent real-time systems. The thesis addresses the synthesis of executable object-oriented design-time models from MSC specifications. The synthesis integrates with the software development process, its purpose being to automatically create working prototypes from specifications without error and create executable models on which properties may be validated. The usefulness of existing algorithms for the synthesis of ROOM (Real-Time Object Oriented Modeling) models from MSC’s has been evaluated from the perspective of an applications programmer ac-cording to various criteria. A number of new synthesis features have been proposed to address them, and applied to a telephony call management system for illustration. These include the specification and construction of hierarchical structure and behavior of ROOM actors, views, multiple containment, replication, resolution of non-determinism and automatic coordination. Generalizations and algorithms have been provided. The hierarchical actor structure, replication, FSM merging, and global coordinator algorithms have been implemented in the Mesa CASE tool. A comparison is made to other specification and modeling languages and their synthesis, such as SDL, LSC’s, and statecharts. Another application of synthesis is to generate a model with support for the automated validation of safety and liveness properties. The Mobility Management services of the GSM digital mobile telecommunications system were specified in MSC’s. A Promela model of the system was then synthesized. A number of optimizations have been proposed to reduce the complexity of the model in order to successfully perform a validation of it. Properties of the system were encoded in Linear Temporal Logic, and the Promela model was used to automatically validate a number of identified properties using the model checker Spin. A ROOM model was then synthesized from the validated MSC specification using the proposed refinement features

Data-Driven Approach based on Deep Learning and Probabilistic Models for PHY-Layer Security in AI-enabled Cognitive Radio IoT.

PhD Theses.Cognitive Radio Internet of Things (CR-IoT) has revolutionized almost every eld of life and reshaped the technological world. Several tiny devices are seamlessly connected in a CR-IoT network to perform various tasks in many applications. Nevertheless, CR-IoT su ers from malicious attacks that pulverize communication and perturb network performance. Therefore, recently it is envisaged to introduce higher-level Arti cial Intelligence (AI) by incorporating Self-Awareness (SA) capabilities into CR-IoT objects to facilitate CR-IoT networks to establish secure transmission against vicious attacks autonomously. In this context, sub-band information from the Orthogonal Frequency Division Multiplexing (OFDM) modulated transmission in the spectrum has been extracted from the radio device receiver terminal, and a generalized state vector (GS) is formed containing low dimension in-phase and quadrature components. Accordingly, a probabilistic method based on learning a switching Dynamic Bayesian Network (DBN) from OFDM transmission with no abnormalities has been proposed to statistically model signal behaviors inside the CR-IoT spectrum. A Bayesian lter, Markov Jump Particle Filter (MJPF), is implemented to perform state estimation and capture malicious attacks. Subsequently, GS containing a higher number of subcarriers has been investigated. In this connection, Variational autoencoders (VAE) is used as a deep learning technique to extract features from high dimension radio signals into low dimension latent space z, and DBN is learned based on GS containing latent space data. Afterward, to perform state estimation and capture abnormalities in a spectrum, Adapted-Markov Jump Particle Filter (A-MJPF) is deployed. The proposed method can capture anomaly that appears due to either jammer attacks in transmission or cognitive devices in a network experiencing di erent transmission sources that have not been observed previously. The performance is assessed using the receiver

Ego things : Networks Of Self-Aware Intelligent Objects.

PhD ThesesThere is an increasing demand for developing intelligence and awareness in arti cial agents in recent days to improve autonomy, robustness, and scalability, and it has been investigated in various research elds such as machine learning, robotics, software engineering, etc. Moreover, it is crucial to model such an agent's interaction with the surrounding environment and other agents to represent collaborative tasks. In this thesis, we have proposed several approaches to developing multi-modal self-awareness in agents and multi-modal collective awareness (CA) for multiple networked intelligent agents by focusing on the functionality to detect abnormal situations. The rst part of the thesis is proposed a novel approach to build selfawareness in dynamic agents to detect abnormalities based on multi-sensory data and feature selection. By considering several sensory data features, learned multiple inference models and facilitated obtaining the most distinct features for predicting future instances and detecting possible abnormalities. The proposed method can select the optimal set features to be shared in networking operations such that state prediction, decision-making, and abnormality detection processes are favored. In the second part, proposed di erent approaches for developing collective awareness in an agents network. Each agent of a network is considered an Internet of Things (IoT) node equipped with machine learning capabilities. The collective awareness aims to provide the network with updated causal knowledge of the state of execution of actions of each node performing a joint task, with particular attention to anomalies that can arise. Datadriven dynamic Bayesian models learned from multi-sensory data recorded during the normal realization of a joint task (agent network experience) are used for distributed state estimation of agents and detection of abnormalities. Moreover, the e ects of networking protocols and communications in the estimation of state and abnormalities are analyzed. Finally, the abnormality estimation is performed at the model's di erent abstraction levels and explained the models' interpretability. In this work, interpretability is the capability to use anomaly data to modify the model to make inferences accurately in the future

An interdisciplinary, cross-lingual perspective

Multiword expressions (MWEs), such as noun compounds (e.g. nickname in English, and Ohrwurm in German), complex verbs (e.g. give up in English, and aufgeben in German) and idioms (e.g. break the ice in English, and das Eis brechen in German), may be interpreted literally but often undergo meaning shifts with respect to their constituents. Theoretical, psycholinguistic as well as computational linguistic research remain puzzled by when and how MWEs receive literal vs. meaning-shifted interpretations, what the contributions of the MWE constituents are to the degree of semantic transparency (i.e., meaning compositionality) of the MWE, and how literal vs. meaning-shifted MWEs are processed and computed. This edited volume presents an interdisciplinary selection of seven papers on recent findings across linguistic, psycholinguistic, corpus-based and computational research fields and perspectives, discussing the interaction of constituent properties and MWE meanings, and how MWE constituents contribute to the processing and representation of MWEs. The collection is based on a workshop at the 2017 annual conference of the German Linguistic Society (DGfS) that took place at Saarland University in Saarbrücken, German

The role of constituents in multiword expressions

Multiword expressions (MWEs), such as noun compounds (e.g. nickname in English, and Ohrwurm in German), complex verbs (e.g. give up in English, and aufgeben in German) and idioms (e.g. break the ice in English, and das Eis brechen in German), may be interpreted literally but often undergo meaning shifts with respect to their constituents. Theoretical, psycholinguistic as well as computational linguistic research remain puzzled by when and how MWEs receive literal vs. meaning-shifted interpretations, what the contributions of the MWE constituents are to the degree of semantic transparency (i.e., meaning compositionality) of the MWE, and how literal vs. meaning-shifted MWEs are processed and computed. This edited volume presents an interdisciplinary selection of seven papers on recent findings across linguistic, psycholinguistic, corpus-based and computational research fields and perspectives, discussing the interaction of constituent properties and MWE meanings, and how MWE constituents contribute to the processing and representation of MWEs. The collection is based on a workshop at the 2017 annual conference of the German Linguistic Society (DGfS) that took place at Saarland University in Saarbrücken, Germany

Behavioral representation of military tactics for single-vehicle autonomous rotorcraft via statecharts

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2005.Includes bibliographical references (p. 113-115).Over the past several years, aerospace companies have developed unmanned helicopters suitable for integration into military operations as reconnaissance platforms. These rotorcraft, however, require ground-based human controllers varying in number based on the size and complexity of the system controlled. The automation these platforms have achieved is limited to takeoffs, landings and navigation of pre-programmed waypoints. The possibilities for further development then are vast; with growing sensor and communication capabilities, there exists potential for unmanned rotorcraft to execute the full range of aviation missions normally reserved for manned assets. However, before military planners use autonomous helicopters as robust force multipliers, research must attempt to quantify possible tactics for software architecture implementation. This paper presents a methodology for developing autonomous helicopter tactics through the review of current military doctrine, pilot interviews, and simulation testing. Several tactics suitable for unmanned helicopters are recommended with an attempt to quantify the described behaviors using statecharts. The tactics diagrammed in the statecharts, or visual models that outline transitions between states based on conditions being met or events having occurred, are tested for feasibility in scenarios constructed with a US Army simulation tool, One SemiAutomated Forces (OneSAF) Testbed Baseline 2.0 (OTB 2.0). The ensuing results point to the success of using a thorough methodology to develop autonomous tactics and using statecharts to transfer qualitative behaviors into quantifiable actions.by Mark M. Hickie.S.M

The role of constituents in multiword expressions

Multiword expressions (MWEs), such as noun compounds (e.g. nickname in English, and Ohrwurm in German), complex verbs (e.g. give up in English, and aufgeben in German) and idioms (e.g. break the ice in English, and das Eis brechen in German), may be interpreted literally but often undergo meaning shifts with respect to their constituents. Theoretical, psycholinguistic as well as computational linguistic research remain puzzled by when and how MWEs receive literal vs. meaning-shifted interpretations, what the contributions of the MWE constituents are to the degree of semantic transparency (i.e., meaning compositionality) of the MWE, and how literal vs. meaning-shifted MWEs are processed and computed. This edited volume presents an interdisciplinary selection of seven papers on recent findings across linguistic, psycholinguistic, corpus-based and computational research fields and perspectives, discussing the interaction of constituent properties and MWE meanings, and how MWE constituents contribute to the processing and representation of MWEs. The collection is based on a workshop at the 2017 annual conference of the German Linguistic Society (DGfS) that took place at Saarland University in Saarbrücken, Germany