A Probabilistic Logic Programming Event Calculus

We present a system for recognising human activity given a symbolic representation of video content. The input of our system is a set of time-stamped short-term activities (STA) detected on video frames. The output is a set of recognised long-term activities (LTA), which are pre-defined temporal combinations of STA. The constraints on the STA that, if satisfied, lead to the recognition of a LTA, have been expressed using a dialect of the Event Calculus. In order to handle the uncertainty that naturally occurs in human activity recognition, we adapted this dialect to a state-of-the-art probabilistic logic programming framework. We present a detailed evaluation and comparison of the crisp and probabilistic approaches through experimentation on a benchmark dataset of human surveillance videos.Comment: Accepted for publication in the Theory and Practice of Logic Programming (TPLP) journa

Genetic Programming for Smart Phone Personalisation

Personalisation in smart phones requires adaptability to dynamic context based on user mobility, application usage and sensor inputs. Current personalisation approaches, which rely on static logic that is developed a priori, do not provide sufficient adaptability to dynamic and unexpected context. This paper proposes genetic programming (GP), which can evolve program logic in realtime, as an online learning method to deal with the highly dynamic context in smart phone personalisation. We introduce the concept of collaborative smart phone personalisation through the GP Island Model, in order to exploit shared context among co-located phone users and reduce convergence time. We implement these concepts on real smartphones to demonstrate the capability of personalisation through GP and to explore the benefits of the Island Model. Our empirical evaluations on two example applications confirm that the Island Model can reduce convergence time by up to two-thirds over standalone GP personalisation.Comment: 43 pages, 11 figure

Neurons and symbols: a manifesto

We discuss the purpose of neural-symbolic integration including its principles, mechanisms and applications. We outline a cognitive computational model for neural-symbolic integration, position the model in the broader context of multi-agent systems, machine learning and automated reasoning, and list some of the challenges for the area of neural-symbolic computation to achieve the promise of effective integration of robust learning and expressive reasoning under uncertainty

Design and implementation of a finite domain constraint logic programming system based on PROLOG with coroutining

Many problems in different areas such as Operations Research, Hardware Design, and Artificial Intelligence can be regarded as constraint solving problems (CSPs). Logic programming offers a convenient way of representing CSPs due to its relational, declarative and nondeterministic form. Unfortunately, standard logic programming languages such as PROLOG tend to be inefficient for solving CSPs, since what could be called constraints in PROLOG is used only in a passive a posteriori manner, leading to symptoms such as late recognition of failure, unnecessary and unintelligent backtracking and multiple computation of the same solutions. There have been intensive research efforts in order to remedy this. One of them, which has caught increasing attention over the past few years, is the Constraint Logic Programming approach: By integrating a domain concept for logic variables and consistency techniques such as forward-checking or looking-ahead into PROLOG, the search space can be restricted in an a priori manner. Thus, a more efficient control strategy can be achieved, preserving the 'clean' dual PROLOG semantics. In this issue, I will present a horizontal compilation approach towards a CLP system maintaining constraints whose variables are ranging over finite domains. Horizontal compilations often referred to as optimizing transformation techniques in other context. A PROLOG system providing a delay mechanism is used in order to achieve the control behaviour described above. The major subtasks of my work are -Design and integration of a domain concept into logic programming, which allows direct access to and manipulation of possible values of logic variables. -Thorough implementation of a forward-checking control strategy in SEPIA. -Design and prototypical implementation of a looking-ahead algorithm. -Summary of the main theoretical results underlying to domains and consistency techniques in logic programming. -Consideration and prototypical implementation of first-fail heuristics. -Embedding these topics into a preprocessor, which transforms FIDO programs into SEPIA programs realizing the advanced control strategies. The general framework of this work is the FIDO lab within the ARC-TEC project, which explores several approaches towards integrating finite domain consistency techniques into logic programming

Use Cases for Abnormal Behaviour Detection in Smart Homes

While people have many ideas about how a smart home should react to particular behaviours from their inhabitant, there seems to have been relatively little attempt to organise this systematically. In this paper, we attempt to rectify this in consideration of context awareness and novelty detection for a smart home that monitors its inhabitant for illness and unexpected behaviour. We do this through the concept of the Use Case, which is used in software engineering to specify the behaviour of a system. We describe a set of scenarios and the possible outputs that the smart home could give and introduce the SHMUC Repository of Smart Home Use Cases. Based on this, we can consider how probabilistic and logic-based reasoning systems would produce different capabilities

A Boxology of Design Patterns for Hybrid Learning and Reasoning Systems

We propose a set of compositional design patterns to describe a large variety of systems that combine statistical techniques from machine learning with symbolic techniques from knowledge representation. As in other areas of computer science (knowledge engineering, software engineering, ontology engineering, process mining and others), such design patterns help to systematize the literature, clarify which combinations of techniques serve which purposes, and encourage re-use of software components. We have validated our set of compositional design patterns against a large body of recent literature.Comment: 12 pages,55 reference

Emergent requirements for supporting introductory programming

The problems associated with learning and teaching first year University Computer Science (CS1) programming classes are summarized showing that various support tools and techniques have been developed and evaluated. From this review of applicable support the paper derives ten requirements that a support tool should have in order to improve CS1 student success rate with respect to learning and understanding