How to Do Things With Words

The Theory of Communicative Action, Vol. 1, Reason and the Rationalization of Society

Governing the Commons

Convention: a philosophical study

Argumentation in artificial intelligence

In this paper, we address the issue of uncertainty in event recognition by extending the Event Calculus with probabilistic reasoning. Markov Logic Networks are a natural candidate for our logic-based formalism. However, the temporal semantics of Event Calculus introduce a number of challenges for the proposed model. We show how and under what assumptions we can overcome these problems. Additionally, we demonstrate the advantages of the probabilistic Event Calculus through examples and experiments in the domain of activity recognition, using a publicly available dataset of video surveillance.

/pdf/probabilistic-event-calculus-based-on-markov-logic-networks-1wfsvr847z.pdf

Probabilistic event calculus based on Markov logic networks

https://www.doc.ic.ac.uk/research/technicalreports/2006/DTR06-13.pdf

An executable specification of a formal argumentation protocol

Complex event recognition (CER) refers to the detection of events in Big Data streams. The paper presents a summary of the most prominent models and algorithms for CER, and discusses the main conceptual links and the differences between them.

Complex Event Recognition Languages: Tutorial

We propose a formal semantics for the protocol diagrams (interaction patterns) of AUML (Agent Unified Modelling Language). We connect this proposal with a general framework for defining the semantics of ACLs (Agent Communication Languages). We then show that protocol diagrams should be parameterised with observable commitments: additional specification of the expected outcomes and normative positions resulting from the use of the protocol. A complete axiomatisation of a contract-net protocol is given, and animated to show how the agents comply with expected replies and respecting the norms. We conclude that this approach to `socialising' interaction between agents is important for developing open agent systems and potentially useful in standardisation.

Interaction patterns and observable commitments in a multi-agent trading scenario

The concept of event processing is established as a generic computational paradigm in various application fields, ranging from data processing in Web environments, over maritime and transport, to finance and medicine. Events report on state changes of a system and its environment. Complex Event Recognition (CER) in turn, refers to the identification of complex/composite events of interest, which are collections of simple events that satisfy some pattern, thereby providing the opportunity for reactive and proactive measures. Examples include the recognition of attacks in computer network nodes, human activities on video content, emerging stories and trends on the Social Web, traffic and transport incidents in smart cities, fraud in electronic marketplaces, cardiac arrhythmias, and epidemic spread. In each scenario, CER allows to make sense of Big event Data streams and react accordingly. The goal of this tutorial is to provide a step-by-step guide for realizing CER in the Big Data era. To do so, it elaborates on major challenges and describes algorithmic toolkits for optimized manipulation of event streams characterized by high volume, velocity and/or lack of veracity, placing emphasis on distributed CER over potentially heterogeneous (data variety) event sources. Finally, we highlight future research directions in the field.

/pdf/complex-event-recognition-in-the-big-data-era-47lm76hpza.pdf

Alexander Artikis

Papers

Probabilistic event calculus based on Markov logic networks

An executable specification of a formal argumentation protocol

Complex Event Recognition Languages: Tutorial

Interaction patterns and observable commitments in a multi-agent trading scenario

Complex event recognition in the big data era