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

A survey of checkpointing algorithms for parallel and distributed computers

Checkpoint is defined as a designated place in a program at which normal processing is interrupted specifically to preserve the status information necessary to allow resumption of processing at a later time. Checkpointing is the process of saving the status information. This paper surveys the algorithms which have been reported in the literature for checkpointing parallel/distributed systems. It has been observed that most of the algorithms published for checkpointing in message passing systems are based on the seminal article by Chandy and Lamport. A large number of articles have been published in this area by relaxing the assumptions made in this paper and by extending it to minimise the overheads of coordination and context saving. Checkpointing for shared memory systems primarily extend cache coherence protocols to maintain a consistent memory. All of them assume that the main memory is safe for storing the context. Recently algorithms have been published for distributed shared memory systems, which extend the cache coherence protocols used in shared memory systems. They however also include methods for storing the status of distributed memory in stable storage. Most of the algorithms assume that there is no knowledge about the programs being executed. It is however felt that in development of parallel programs the user has to do a fair amount of work in distributing tasks and this information can be effectively used to simplify checkpointing and rollback recovery

Three Highly Parallel Computer Architectures and Their Suitability for Three Representative Artificial Intelligence Problems

Virtually all current Artificial Intelligence (AI) applications are designed to run on sequential (von Neumann) computer architectures. As a result, current systems do not scale up. As knowledge is added to these systems, a point is reached where their performance quickly degrades. The performance of a von Neumann machine is limited by the bandwidth between memory and processor (the von Neumann bottleneck). The bottleneck is avoided by distributing the processing power across the memory of the computer. In this scheme the memory becomes the processor (a smart memory ). This paper highlights the relationship between three representative AI application domains, namely knowledge representation, rule-based expert systems, and vision, and their parallel hardware realizations. Three machines, covering a wide range of fundamental properties of parallel processors, namely module granularity, concurrency control, and communication geometry, are reviewed: the Connection Machine (a fine-grained SIMD hypercube), DADO (a medium-grained MIMD/SIMD/MSIMD tree-machine), and the Butterfly (a coarse-grained MIMD Butterflyswitch machine)

A telecom analytics framework for dynamic quality of service management

Since the beginning of Internet, Internet Service Providers (ISP) have seen the need of giving to users? traffic different treatments defined by agree- ments between ISP and customers. This procedure, known as Quality of Service Management, has not much changed in the last years (DiffServ and Deep Pack-et Inspection have been the most chosen mechanisms). However, the incremen-tal growth of Internet users and services jointly with the application of recent Ma- chine Learning techniques, open up the possibility of going one step for-ward in the smart management of network traffic. In this paper, we first make a survey of current tools and techniques for QoS Management. Then we intro-duce clustering and classifying Machine Learning techniques for traffic charac-terization and the concept of Quality of Experience. Finally, with all these com-ponents, we present a brand new framework that will manage in a smart way Quality of Service in a telecom Big Data based scenario, both for mobile and fixed communications

Opening up Magpie via semantic services

Magpie is a suite of tools supporting a ‘zero-cost’ approach to semantic web browsing: it avoids the need for manual annotation by automatically associating an ontology-based semantic layer to web resources. An important aspect of Magpie, which differentiates it from superficially similar hypermedia systems, is that the association between items on a web page and semantic concepts is not merely a mechanism for dynamic linking, but it is the enabling condition for locating services and making them available to a user. These services can be manually activated by a user (pull services), or opportunistically triggered when the appropriate web entities are encountered during a browsing session (push services). In this paper we analyze Magpie from the perspective of building semantic web applications and we note that earlier implementations did not fulfill the criterion of “open as to services”, which is a key aspect of the emerging semantic web. For this reason, in the past twelve months we have carried out a radical redesign of Magpie, resulting in a novel architecture, which is open both with respect to ontologies and semantic web services. This new architecture goes beyond the idea of merely providing support for semantic web browsing and can be seen as a software framework for designing and implementing semantic web applications

Conversational Machine Comprehension: a Literature Review

Conversational Machine Comprehension (CMC), a research track in conversational AI, expects the machine to understand an open-domain natural language text and thereafter engage in a multi-turn conversation to answer questions related to the text. While most of the research in Machine Reading Comprehension (MRC) revolves around single-turn question answering (QA), multi-turn CMC has recently gained prominence, thanks to the advancement in natural language understanding via neural language models such as BERT and the introduction of large-scale conversational datasets such as CoQA and QuAC. The rise in interest has, however, led to a flurry of concurrent publications, each with a different yet structurally similar modeling approach and an inconsistent view of the surrounding literature. With the volume of model submissions to conversational datasets increasing every year, there exists a need to consolidate the scattered knowledge in this domain to streamline future research. This literature review attempts at providing a holistic overview of CMC with an emphasis on the common trends across recently published models, specifically in their approach to tackling conversational history. The review synthesizes a generic framework for CMC models while highlighting the differences in recent approaches and intends to serve as a compendium of CMC for future researchers.Comment: Accepted to COLING 202

Massively Parallel Simulation of Structured Connectionist Networks: An Interim Report

We map structured connectionist models of knowledge representation and reasoning onto existing general purpose massively parallel architectures with the objective of developing and implementing practical, real-time knowledge base systems. Shruti, a connectionist knowledge representation and reasoning system which attempts to model reflexive reasoning, will serve as our representative connectionist model. Efficient simulation systems for shruti are developed on the Connection Machine CM-2 - an SIMD architecture - and on the Connection Machine CM-5 - an MIMD architecture. The resulting simulators are evaluated and tested using large, random knowledge bases with up to half a million rules and facts. Though SIMD simulations on the CM-2 are reasonably fast - requiring a few seconds to tens of seconds for answering simple queries - experiments indicate that MIMD simulations are vastly superior to SIMD simulations and offer hundred- to thousand-fold speedups. This work provides new insights into the simulation of structured connectionist networks on massively parallel machines and is a step toward developing large yet efficient knowledge representation and reasoning systems