Planning as Optimization: Dynamically Discovering Optimal Configurations for Runtime Situations

The large number of possible configurations of modern software-based systems, combined with the large number of possible environmental situations of such systems, prohibits enumerating all adaptation options at design time and necessitates planning at run time to dynamically identify an appropriate configuration for a situation. While numerous planning techniques exist, they typically assume a detailed state-based model of the system and that the situations that warrant adaptations are known. Both of these assumptions can be violated in complex, real-world systems. As a result, adaptation planning must rely on simple models that capture what can be changed (input parameters) and observed in the system and environment (output and context parameters). We therefore propose planning as optimization: the use of optimization strategies to discover optimal system configurations at runtime for each distinct situation that is also dynamically identified at runtime. We apply our approach to CrowdNav, an open-source traffic routing system with the characteristics of a real-world system. We identify situations via clustering and conduct an empirical study that compares Bayesian optimization and two types of evolutionary optimization (NSGA-II and novelty search) in CrowdNav

Structure of Gαi1 Bound to a GDP-Selective Peptide Provides Insight into Guanine Nucleotide Exchange

Heterotrimeric G-proteins are molecular switches that regulate numerous signaling pathways involved in cellular physiology. This characteristic is achieved by the adoption of two principal states: an inactive, GDP-bound and an active, GTP-bound state. Under basal conditions G-proteins exist in the inactive GDP-bound state, thus nucleotide exchange is crucial to the onset of signaling. Despite our understanding of G-protein signaling pathways, the mechanism of nucleotide exchange remains elusive. We employed phage display technology to identify nucleotide-state-dependent Gα binding peptides. Herein, we report a GDP-selective Gα-binding peptide, KB-752, that enhances spontaneous nucleotide exchange of Gαi subunits. Structural determination of the Gαi1/peptide complex reveals unique changes in the Gα switch regions predicted to enhance nucleotide exchange by creating a GDP dissociation route. Our results cast light onto a potential mechanism by which Gα subunits adopt a conformation suitable for nucleotide exchange

Genetic Improvement @ ICSE 2020

Following Prof. Mark Harman of Facebook's keynote and formal presentations (which are recorded in the proceedings) there was a wide ranging discussion at the eighth international Genetic Improvement workshop, GI-2020 @ ICSE (held as part of the International Conference on Software En- gineering on Friday 3rd July 2020). Topics included industry take up, human factors, explainabiloity (explainability, jus- tifyability, exploitability) and GI benchmarks. We also con- trast various recent online approaches (e.g. SBST 2020) to holding virtual computer science conferences and workshops via the WWW on the Internet without face to face interac- tion. Finally we speculate on how the Coronavirus Covid-19 Pandemic will a ect research next year and into the future

MUSE: A Genetic Algorithm for Musical Chord Progression Generation

PURPOSE: Foundational to our understanding and enjoyment of music is the intersection of harmony and movement. This intersection manifests as chord progressions which themselves underscore the rhythm and melody of a piece. In musical compositions, these progressions often follow a set of rules and patterns which are themselves frequently broken for the sake of novelty. PROCEDURES: In this work, we developed a genetic algorithm which learns these rules and patterns (and how to break them) from a dataset of 890 songs from various periods of the Billboard Top 100 rankings. OUTCOME: The algorithm learned to generate increasingly valid, yet interesting chord progressions via penalties based on both conditional probabilities extracted from the aforementioned dataset and weights applied to the characteristics from which the penalty is derived. Additionally, the beginning and end of a progression may be seeded (either in totality or for a percentage of generated patterns) such that the algorithm will generate a bridging progression to connect the seeded points. IMPACT: To this end, the algorithm proposed chord progressions and supplied vectors of computer-aided algorithmic composition. To demonstrate the validity of the system, we present a subset of generated progressions that both conform to known musical patterns and contain interesting deviations

Towards Run-Time Search for Real-World Multi-Agent Systems

Multi-agent systems (MAS) may encounter uncertainties in the form of unexpected environmental conditions, sub-optimal system configurations, and unplanned interactions between autonomous agents. The number of combinations of such uncertainties may be innumerable, however run-time testing may reduce the issues impacting such a system. We posit that search heuristics can augment a run-time testing process, in-situ, for a MAS. To support our position we discuss our in-progress experimental testbed to realize this goal and highlight challenges we anticipate for this domain

Towards Self-Adaptive Game Logic

Self-adaptive systems (SAS) can reconfigure at run time in response to changing situations to express acceptable behaviors in the face of uncertainty. With respect to game design, such situations may include user input, emergent behaviors, performance concerns, and combinations thereof. Typically an SAS is modeled as a feedback loop that functions within an existing system, with operations including monitoring, analyzing, planning, and executing (i.e., MAPE-K) to enable online reconfiguration. This paper presents a conceptual approach for extending software engineering artifacts to be self-adaptive within the context of game design. We have modified a game developed for creative coding education to include a MAPE-K self-adaptive feedback loop, comprising run-time adaptation capabilities and the software artifacts required to support adaptation

RDMSim: An Exemplar for Evaluation and Comparison of Decision-Making Techniques for Self-Adaptation

Decision-making for self-adaptation approaches need to address different challenges, including the quantification of the uncertainty of events that cannot be foreseen in advance and their effects, and dealing with conflicting objectives that inherently involve multi-objective decision making (e.g., avoiding costs vs. providing reliable service). To enable researchers to evaluate and compare decision-making techniques for self-adaptation, we present the RDMSim exemplar. RDMSim enables researchers to evaluate and compare techniques for decision-making under environmental uncertainty that support self-adaptation. The focus of the exemplar is on the domain problem related to Remote Data Mirroring, which gives opportunity to face the challenges described above. RDMSim provides probe and effector components for easy integration with external adaptation managers, which are associated with decision-making techniques and based on the MAPE-K loop. Specifically, the paper presents (i) RDMSim, a simulator for real-world experimentation, (ii) a set of realistic simulation scenarios that can be used for experimentation and comparison purposes, (iii) data for the sake of comparison