In this article, we study scale-free delayed regulated state/output synchronization for homogeneous and heterogeneous networks of multi-agent systems (MAS) subject to unknown, nonuniform, arbitrarily large communication delays. A delay transformation is utilized transform the original MAS a new system without states. The proposed dynamic protocols are developed non-introspective introspective M...

We present Wasserstein introspective neural networks (WINN) that are both a generator and a discriminator within a single model. WINN provides a significant improvement over the recent introspective neural networks (INN) method by enhancing INN’s generative modeling capability. WINN has three interesting properties: (1) A mathematical connection between the formulation of the INN algorithm and ...

In the static analysis of functional programs, pushdown flow analysis and abstract garbage collection skirt just inside the boundaries of soundness and decidability. This work illuminates and conquers the theoretical challenges that stand in the way of combining the power of these techniques. Pushdown flow analysis grants unbounded yet computable polyvariance to the analysis of return-flow in h...

Core autonomic behaviours, namely self-healing, selftuning, and self-adaptation, are now prevalent features of modern software architectures. Current implementations have typically relied on static rule and goal oriented approaches that respond to operational triggers to provide self-adaptive behaviour. This paper builds on the introspective nature of the previous work of the authors, namely th...

Case-based reasoning research on indexing and retrieval focuses primarily on developing specific retrieval criteria, rather than on developing mechanisms by which such criteria can be learned as needed. This paper presents a framework for learning to refine indexing criteria by introspective reasoning. In our approach, a self-model of desired system performance is used to determine when and how...

Many current AI systems assume that the reasoning mechanisms used to manipulate their knowledge may be xed ahead of time by the designer. This assumption may break down in complex domains. The focus of this research is developing a model of introspective reasoning and learning to enable a system to improve its own reasoning as well as its domain knowledge. Our model is based on the proposal of ...

Except for various ad hoc (and sometimes quite successful) systems, this de facto manifesto calling for the study of introspective systems did not give rise to what may be called "a general architecture for declarative and/or reflective machine learning". Some recent research taking place under the label of "goal-driven learning" signals however a renewed interest in these very basic issues: " ...

Introspection is a general term covering the ability of an agent to reflect upon the workings of his own cognitive functions. In this paper we wi l l be concerned wi th developing an explanatory theory of a particular type of introspection: a robot agent's knowledge of his own beliefs. The development is both descriptive, in the sense of being able to capture introspective behavior as it exist;...

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