Technology assessment of advanced automation for space missions

Six general classes of technology requirements derived during the mission definition phase of the study were identified as having maximum importance and urgency, including autonomous world model based information systems, learning and hypothesis formation, natural language and other man-machine communication, space manufacturing, teleoperators and robot systems, and computer science and technology

Composing features by managing inconsistent requirements

One approach to system development is to decompose the requirements into features and specify the individual features before composing them. A major limitation of deferring feature composition is that inconsistency between the solutions to individual features may not be uncovered early in the development, leading to unwanted feature interactions. Syntactic inconsistencies arising from the way software artefacts are described can be addressed by the use of explicit, shared, domain knowledge. However, behavioural inconsistencies are more challenging: they may occur within the requirements associated with two or more features as well as at the level of individual features. Whilst approaches exist that address behavioural inconsistencies at design time, these are overrestrictive in ruling out all possible conflicts and may weaken the requirements further than is desirable. In this paper, we present a lightweight approach to dealing with behavioural inconsistencies at run-time. Requirement Composition operators are introduced that specify a run-time prioritisation to be used on occurrence of a feature interaction. This prioritisation can be static or dynamic. Dynamic prioritisation favours some requirement according to some run-time criterion, for example, the extent to which it is already generating behaviour

Human-Intelligence and Machine-Intelligence Decision Governance Formal Ontology

Since the beginning of the human race, decision making and rational thinking played a pivotal role for mankind to either exist and succeed or fail and become extinct. Self-awareness, cognitive thinking, creativity, and emotional magnitude allowed us to advance civilization and to take further steps toward achieving previously unreachable goals. From the invention of wheels to rockets and telegraph to satellite, all technological ventures went through many upgrades and updates. Recently, increasing computer CPU power and memory capacity contributed to smarter and faster computing appliances that, in turn, have accelerated the integration into and use of artificial intelligence (AI) in organizational processes and everyday life. Artificial intelligence can now be found in a wide range of organizational systems including healthcare and medical diagnosis, automated stock trading, robotic production, telecommunications, space explorations, and homeland security. Self-driving cars and drones are just the latest extensions of AI. This thrust of AI into organizations and daily life rests on the AI community’s unstated assumption of its ability to completely replicate human learning and intelligence in AI. Unfortunately, even today the AI community is not close to completely coding and emulating human intelligence into machines. Despite the revolution of digital and technology in the applications level, there has been little to no research in addressing the question of decision making governance in human-intelligent and machine-intelligent (HI-MI) systems. There also exists no foundational, core reference, or domain ontologies for HI-MI decision governance systems. Further, in absence of an expert reference base or body of knowledge (BoK) integrated with an ontological framework, decision makers must rely on best practices or standards that differ from organization to organization and government to government, contributing to systems failure in complex mission critical situations. It is still debatable whether and when human or machine decision capacity should govern or when a joint human-intelligence and machine-intelligence (HI-MI) decision capacity is required in any given decision situation. To address this deficiency, this research establishes a formal, top level foundational ontology of HI-MI decision governance in parallel with a grounded theory based body of knowledge which forms the theoretical foundation of a systemic HI-MI decision governance framework

Advanced automation for space missions: Technical summary

Several representative missions which would require extensive applications of machine intelligence were identified and analyzed. The technologies which must be developed to accomplish these types of missions are discussed. These technologies include man-machine communication, space manufacturing, teleoperators, and robot systems

A neural-symbolic system for temporal reasoning with application to model verification and learning

The effective integration of knowledge representation, reasoning and learning into a robust computational model is one of the key challenges in Computer Science and Artificial Intelligence. In particular, temporal models have been fundamental in describing the behaviour of Computational and Neural-Symbolic Systems. Furthermore, knowledge acquisition of correct descriptions of the desired system’s behaviour is a complex task in several domains. Several efforts have been directed towards the development of tools that are capable of learning, describing and evolving software models. This thesis contributes to two major areas of Computer Science, namely Artificial Intelligence (AI) and Software Engineering. Under an AI perspective, we present a novel neural-symbolic computational model capable of representing and learning temporal knowledge in recurrent networks. The model works in integrated fashion. It enables the effective representation of temporal knowledge, the adaptation of temporal models to a set of desirable system properties and effective learning from examples, which in turn can lead to symbolic temporal knowledge extraction from the corresponding trained neural networks. The model is sound, from a theoretical standpoint, but is also tested in a number of case studies. An extension to the framework is shown to tackle aspects of verification and adaptation under the SE perspective. As regards verification, we make use of established techniques for model checking, which allow the verification of properties described as temporal models and return counter-examples whenever the properties are not satisfied. Our neural-symbolic framework is then extended to deal with different sources of information. This includes the translation of model descriptions into the neural structure, the evolution of such descriptions by the application of learning of counter examples, and also the learning of new models from simple observation of their behaviour. In summary, we believe the thesis describes a principled methodology for temporal knowledge representation, learning and extraction, shedding new light on predictive temporal models, not only from a theoretical standpoint, but also with respect to a potentially large number of applications in AI, Neural Computation and Software Engineering, where temporal knowledge plays a fundamental role

AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments

This report considers the application of Articial Intelligence (AI) techniques to the problem of misuse detection and misuse localisation within telecommunications environments. A broad survey of techniques is provided, that covers inter alia rule based systems, model-based systems, case based reasoning, pattern matching, clustering and feature extraction, articial neural networks, genetic algorithms, arti cial immune systems, agent based systems, data mining and a variety of hybrid approaches. The report then considers the central issue of event correlation, that is at the heart of many misuse detection and localisation systems. The notion of being able to infer misuse by the correlation of individual temporally distributed events within a multiple data stream environment is explored, and a range of techniques, covering model based approaches, `programmed' AI and machine learning paradigms. It is found that, in general, correlation is best achieved via rule based approaches, but that these suffer from a number of drawbacks, such as the difculty of developing and maintaining an appropriate knowledge base, and the lack of ability to generalise from known misuses to new unseen misuses. Two distinct approaches are evident. One attempts to encode knowledge of known misuses, typically within rules, and use this to screen events. This approach cannot generally detect misuses for which it has not been programmed, i.e. it is prone to issuing false negatives. The other attempts to `learn' the features of event patterns that constitute normal behaviour, and, by observing patterns that do not match expected behaviour, detect when a misuse has occurred. This approach is prone to issuing false positives, i.e. inferring misuse from innocent patterns of behaviour that the system was not trained to recognise. Contemporary approaches are seen to favour hybridisation, often combining detection or localisation mechanisms for both abnormal and normal behaviour, the former to capture known cases of misuse, the latter to capture unknown cases. In some systems, these mechanisms even work together to update each other to increase detection rates and lower false positive rates. It is concluded that hybridisation offers the most promising future direction, but that a rule or state based component is likely to remain, being the most natural approach to the correlation of complex events. The challenge, then, is to mitigate the weaknesses of canonical programmed systems such that learning, generalisation and adaptation are more readily facilitated

Developing Solution Business : Effectual Service-Dominant Logic Approach

This thesis examines solution business development from the perspective of service-dominant (S-D) logic and effectuation theory. A case company in maritime transportation industry provided insights into their solution business development. Since the case company’s data gathered in a Delphi study served as a starting point for the study, a modular abductive methodology was adopted to investigate the development of the company’s solution business over the years. Furthermore, ex-post and ex-ante event-based qualitative analysis of the case company was utilized in conjunction with theoretical literature review to develop a conceptual model of solution business development. This thesis presents a conceptual model for solution business development, which suggests that companies should focus on identifying their means and resources, cocreate value propositions, sell solutions that are within their affordable loss limits, and develop solution platform for solution deliveries. Each of the steps in the model are linked to existing literature related to solution business, S-D logic and effectuation. Thus, the model provides multiple approaches and tools that are highlighted in conjunction with the steps for practitioners to implement on their journey towards solution business. Furthermore, practical insights are presented to assist with formulating solution offerings. In addition, the study highlights the influence of various stakeholders in the maritime transportation industry – customers, classification societies, and even competitors – who all need to be taken into account in the ecosystem when developing solution business.Tämä tutkielma tarkastelee ratkaisuliiketoiminnan kehittämistä palvelulogiikka- (S-D logic) ja effektuaatio- (effectuation) kirjallisuuden näkökulmasta. Tutkielma sai alkunsa meriteollisuuden kohdeyrityksestä, joka oli onnistuneesti kehittänyt ratkaisuliiketoimintaansa. Tämä tarina johti päätökseen käyttää abduktiivista ja modulaarista lähestymistapaa yrityksen ratkaisuliiketoiminnan kehittämisen tarkastelussa. Tarkastelun tarkoituksena on luoda ratkaisuliiketoiminnan kehittämisen prosessimalli ja tarjota käytännön neuvoja ratkaisuliiketoiminnan kehittämiseen meriteollisuudessa. Tutkimuksen aineisto pohjautuu Delphi-tutkimukseen yrityksen kriittisistä tapahtumista menneisyydessä ja tulevaisuudessa. Aineisto analysoitiin kvalitatiivisesti yhdessä teoreettisen kirjallisuuden kanssa. Tämä tutkielma esittää ratkaisuliiketoiminnan kehittämisen prosessimallin, joka ohjaa yrityksiä keskittymään resursseihinsa, luomaan yhdessä arvolupauksia, myymään ratkaisuja hyväksytyin riskein ja kehittämään ratkaisuliiketoiminta-alustaa ratkaisujen toimittamiseen. Prosessimallin jokainen osavaihe on linkitetty ratkaisuliiketoiminta-, palvelulogiikka- ja effektuaatiokirjallisuuteen. Prosessimallin lisäksi tutkielma tarjoaa työkaluja jokaiseen osavaiheeseen, joita työntekijät ja yritykset voivat hyödyntää kehittäessään ratkaisuliiketoimintaa. Tämä tutkielma esittää myös käytännön neuvoja ratkaisuliiketoiminnan kehittämiseen, kuten yrityksen syvällinen ymmärrys käytettävissä olevista resursseista ratkaisuja kehitettäessä. Lisäksi tutkielma kuvaa eri sidosryhmien tärkeyttä meriteollisuudessa. Ekosysteemin eri toimijat – kuten asiakkaat, luokituslaitokset ja kilpailijat – tulisi kaikki huomioida ratkaisuliiketoimintaa kehitettäessä