Probabilistic programming languages allow a modeler to build probabilistic models using complex data structures with all the power of a programming language. We present CTPPL, an expressive probabilistic programming language for dynamic processes that models processes using continuous time. Time is a first class element in our language; the amount of time taken by a subprocess can be specified using the full power of the language. We show through examples that CTPPL can easily represent existing continuous time frameworks and makes it easy to represent new ones.
Research on agent communication languages has typically taken the speech acts paradigm as its starting point. Despite their manifest attractions, speech-act models of communication have several serious disadvantages as a foundation for communication in artificial agent systems. In particular, it has proved to be extremely difficult to give a satisfactory semantics to speech-act based agent communication languages. In part, the problem is that speech-act semantics typically make reference to the "mental states" of agents (their beliefs, desires, and intentions), and there is in general no way to attribute such attitudes to arbitrary computational agents. In addition, agent programming languages have only had their semantics formalised for abstract, stand-alone versions, neglecting aspects such as communication primitives.
Python has grown to become one of the top programming languages in the world, with more developers than ever now using it for data analysis, machine learning, DevOps, and web development. Data analysis and machine learning in particular have moved up in Python developers' priorities, according to the 2018 Python Developer survey. Today, 58 percent who use Python do so for data analysis, up from 50 percent last year, overtaking web development on 52 percent. The other rapidly rising uses for Python are machine learning and DevOps. When asked what they use Python for most, web development is the leading answer, given by 27 percent of respondents, and well ahead of the 17 percent who report data analysis as the most common use.