A visual language for representing and explaining strategies in game theory

Domain-Specific Languages

2009

Self Cite

Abstract. Experimental game theory is an increasingly important research tool in many fields, providing insight into strategic behavior through simulation and experimentation on game theoretic models. Unfortunately, despite relying heavily on automation, this approach has not been well supported by tools. Here we present our continuing work on Hagl, a domain-specific language embedded in Haskell, intended to drastically reduce the development time of such experiments and support a highly explorative research style.In this paper we present a fundamental redesign of the underlying game representation in Hagl. These changes allow us to better utilize domain knowledge by allowing different classes of games to be represented differently, exploiting existing domain representations and algorithms. In particular, we show how this supports analytical extensions to Hagl, and makes strategies for state-based games vastly simpler and more efficient.

Section: Discussionmentioning

confidence: 99%

Varying Domain Representations in Hagl

Walkingshaw

Domain-Specific Languages

2009

Self Cite

“…We have previously investigated the notion of explainability as a design criterion for languages in [9]. This was based on a visual language for expressing strategies in game theory.…”

Section: Related Workmentioning

confidence: 99%

Explanations for Regular Expressions

Fundamental Approaches to Software Engineering

Gopinath

2012

Self Cite

Abstract. Regular expressions are widely used, but they are inherently hard to understand and (re)use, which is primarily due to the lack of abstraction mechanisms that causes regular expressions to grow large very quickly. The problems with understandability and usability are further compounded by the viscosity, redundancy, and terseness of the notation. As a consequence, many different regular expressions for the same problem are floating around, many of them erroneous, making it quite difficult to find and use the right regular expression for a particular problem. Due to the ubiquitous use of regular expressions, the lack of understandability and usability becomes a serious software engineering problem. In this paper we present a range of independent, complementary representations that can serve as explanations of regular expressions. We provide methods to compute those representations, and we describe how these methods and the constructed explanations can be employed in a variety of usage scenarios. In addition to aiding understanding, some of the representations can also help identify faults in regular expressions. Our evaluation shows that our methods are widely applicable and can thus have a significant impact in improving the practice of software engineering.

“…Showing this expanded notation everywhere would make the visual notation too noisy, so we envision it as part of the interaction capabilities of these explanations to expand individual operations into such a more detailed representation. We have employed a very similar strategy successfully in another visual language [8]. The notation can also be the basis for a visual explanation programming language in which explanations can be assembled on the fly using distributions and generators and then modified further through filters and groupings.…”

Section: Visual Explanations Of Probabilistic Reasoningmentioning

confidence: 99%

“…Finally, the idea of elevating explainability as a design criterion for languages was first proposed in [8] where we have presented a visual language for expressing game strategies. A major guiding principle for the design of the visual notation was the traceabilty of game results, that is, how well the strategies could be explained by relating them to actual game traces.…”

Section: Related Workmentioning

confidence: 99%

A DSL for Explaining Probabilistic Reasoning

Domain-Specific Languages

Walkingshaw

2009

Self Cite

Abstract. We propose a new focus in language design where languages provide constructs that not only describe the computation of results, but also produce explanations of how and why those results were obtained. We posit that if users are to understand computations produced by a language, that language should provide explanations to the user.As an example of such an explanation-oriented language we present a domain-specific language for explaining probabilistic reasoning, a domain that is not well understood by non-experts. We show the design of the DSL in several steps. Based on a story-telling metaphor of explanations, we identify generic constructs for building stories out of events, and obtaining explanations by applying stories to specific examples. These generic constructs are then adapted to the particular explanation domain of probabilistic reasoning. Finally, we develop a visual notation for explaining probabilistic reasoning.