arXiv subjects

Jonathan Lawry

Publications and source records attributed to Jonathan Lawry.

25 records · Page 2Linked to original sources

arXiv2017

A Fuzzy Approach to Qualification in Design Exploration for Autonomous Robots and Systems

Autonomous robots must operate in complex and changing environments subject to requirements on their behaviour. Verifying absolute satisfaction (true or false) of these requirements is challenging. Instead, we analyse requirements that admit flexible degrees of satisfaction. We analyse vague requirements using fuzzy logic, and probabilistic requirements using model checking. The resulting analysis method provides a partial ordering of system designs, identifying trade-offs between different requirements in terms of the degrees to which they are satisfied. A case study involving a home care robot interacting with a human is used to demonstrate the approach.

cs.SE↗

arXiv2016

Exploiting Vagueness for Multi-Agent Consensus

A framework for consensus modelling is introduced using Kleene's three valued logic as a means to express vagueness in agents' beliefs. Explicitly borderline cases are inherent to propositions involving vague concepts where sentences of a propositional language may be absolutely true, absolutely false or borderline. By exploiting these intermediate truth values, we can allow agents to adopt a more vague interpretation of underlying concepts in order to weaken their beliefs and reduce the levels of inconsistency, so as to achieve consensus. We consider a consensus combination operation which results in agents adopting the borderline truth value as a shared viewpoint if they are in direct conflict. Simulation experiments are presented which show that applying this operator to agents chosen at random (subject to a consistency threshold) from a population, with initially diverse opinions, results in convergence to a smaller set of more precise shared beliefs. Furthermore, if the choice of agents for combination is dependent on the payoff of their beliefs, this acting as a proxy for performance or usefulness, then the system converges to beliefs which, on average, have higher payoff.

cs.MA↗

arXiv2016

Formal Specification and Analysis of Autonomous Systems under Partial Compliance

The widespread adoption of autonomous systems depends on providing guarantees of safety and functional correctness, at both design time and runtime. Information about the extent to which functional requirements can be met in combination with non-functional requirements (NFRs) -- i.e. requirements that can be partially complied with -- , under dynamic and uncertain environments, provides opportunities to enhance the safety and functional correctness of systems at design time. We present a technique to formally define system attributes that can change or be changed to deal with dynamic and uncertain environments (denominated weakened specifications) as a partially ordered lattice, and to automatically explore the system under different specifications, using probabilistic model checking, to find the likelihood of satisfying a requirement. The resulting probabilities form boundaries of "optimal specifications", analogous to Pareto frontiers in multi-objective optimization, informing the designer about the system's capabilities, such as resilience or robustness, when changing its attributes to deal with dynamic and uncertain environments. We illustrate the proposed technique through a domestic robotic assistant example.

cs.RO↗

arXiv2016

Concept Generation in Language Evolution

This thesis investigates the generation of new concepts from combinations of existing concepts as a language evolves. We give a method for combining concepts, and will be investigating the utility of composite concepts in language evolution and thence the utility of concept generation.

cs.AI↗

arXiv2016

A Label Semantics Approach to Linguistic Hedges

We introduce a model for the linguistic hedges `very' and `quite' within the label semantics framework, and combined with the prototype and conceptual spaces theories of concepts. The proposed model emerges naturally from the representational framework we use and as such, has a clear semantic grounding. We give generalisations of these hedge models and show that they can be composed with themselves and with other functions, going on to examine their behaviour in the limit of composition.

cs.AI↗

arXiv2016

The Utility of Hedged Assertions in the Emergence of Shared Categorical Labels

We investigate the emergence of shared concepts in a community of language users using a multi-agent simulation. We extend results showing that negated assertions are of use in developing shared categories, to include assertions modified by linguistic hedges. Results show that using hedged assertions positively affects the emergence of shared categories in two distinct ways. Firstly, using contraction hedges like `very' gives better convergence over time. Secondly, using expansion hedges such as `quite' reduces concept overlap. However, both these improvements come at a cost of slower speed of development.

cs.AI↗

arXiv2016

Emerging Dimension Weights in a Conceptual Spaces Model of Concept Combination

We investigate the generation of new concepts from combinations of properties as an artificial language develops. To do so, we have developed a new framework for conjunctive concept combination. This framework gives a semantic grounding to the weighted sum approach to concept combination seen in the literature. We implement the framework in a multi-agent simulation of language evolution and show that shared combination weights emerge. The expected value and the variance of these weights across agents may be predicted from the distribution of elements in the conceptual space, as determined by the underlying environment, together with the rate at which agents adopt others' concepts. When this rate is smaller, the agents are able to converge to weights with lower variance. However, the time taken to converge to a steady state distribution of weights is longer.

cs.AI↗