SASO 2018

Trust in Organizations: Frontiers of Theory and Research

• Although its importance is readily apparent, the contours of trust in collective contexts are much less obvious. The decision to trust in collective settings is different from, and in many respects more problematic than, decisions about trust that arise in other social contexts. Because of the size and structural complexity of large organizations, for example, individuals do not have the opportunity to engage in the sort of incremental and repeated exchanges that have been shown to facilitate the development of trust in more intimate settings, such as dyadic relationships

Keynote 4 – Heiko Hamann (chair R.P. Würtz, room: FBK – Sala Stringa)

• Florarobotica
• Micro and macro behaviors. He’s talking about ants, but I’m interested in UIs. What interfaces/communication channels cause an intelligent agent to perform constructive/destructive/etc behaviors
• Swarm Robotics: A Formal Approach
• How do you make decisions on the macroscopic scale?
• Fast and sloppy vs. slow and accurate. What about ‘hardware acceleration?
• Kilobot
• Voter models
• Majority rule is faster diffusion than voter model, but less accurate.
• Mixing models does affect the results. Is this explore/exploit as well?
• Swarm performance over density is a left-skewed normal distribution? Is this because they occupy physical space?
• Hmmm. Density is a function of dimension
• Density adaptation. Is it true that people like density to a point? And that online always “feels” different from actual density? What does popularity ranking vs other ranking do to human behavior?
• subCULTron
• generic, scalable and decentralized fault detection for robot swarms
  • Robot swarms are large-scale multirobot systems with decentralized control which means that each robot acts based only on local perception and on local coordination with neighboring robots. The decentralized approach to control confers number of potential benefits. In particular, inherent scalability and robustness are often highlighted as key distinguishing features of robot swarms compared with systems that rely on traditional approaches to multirobot coordination. It has, however, been shown that swarm robotics systems are not always fault tolerant. To realize the robustness potential of robot swarms, it is thus essential to give systems the capacity to actively detect and accommodate faults. In this paper, we present a generic fault-detection system for robot swarms. We show how robots with limited and imperfect sensing capabilities are able to observe and classify the behavior of one another. In order to achieve this, the underlying classifier is an immune system-inspired algorithm that learns to distinguish between normal behavior and abnormal behavior online. Through a series of experiments, we systematically assess the performance of our approach in a detailed simulation environment. In particular, we analyze our system’s capacity to correctly detect robots with faults, false positive rates, performance in a foraging task in which each robot exhibits a composite behavior, and performance under perturbations of the task environment. Results show that our generic fault-detection system is robust, that it is able to detect faults in a timely manner, and that it achieves a low false positive rate. The developed fault-detection system has the potential to enable long-term autonomy for robust multirobot systems, thus increasing the usefulness of robots for a diverse repertoire of upcoming applications in the area of distributed intelligent automation.

SISSY

• Question – Latent space optimization of fitness test?
• Self Improving System Integration – Status and Challenges after 5 years of SISSY
• Interwoven systems
  • Heterogenity of contained subsystems
  • real time demands
  • mutual influences
  • hidden systems
• Human-in-the-loop social dimension (how this affects people?)
• There is no full authority or full knowledge in a SISSY system

Satisfy: Towards a self-learning analyzer for time series forecasting in self-improving systems

• Hyperparameter tuning!
• Add a meta-learning component (Forecasting module)
• AutoWEKA (CASHO) – automatically defined search space <— this works!
• Clustering on the data to try different algorithms on clusters
• Need to spend some time looking at random forest. It seems to be high value for lower cost
• Sante-fe institute time-series contest (set A) (set B)

Adaptive Coordination to Complete Mission Goals
Charles Walter, Sarra Alqahtani, and Rose Gamble

• Self
  • Reflective
  • Changes locally
  • knows its reflective
  • knows its changed and how
• Partial order causal link
• Cosmo robots
• 

• What Reasonable Guarantees Can We Make for a SISSY System? Kirstie Bellman
  • Trustworthy and knowable
  • Guarantees

Hierarchical Self-Awareness and Authority for Scalable Self-Integrating Systems
Ada Diaconescu, Barry Porter, Roberto Rodrigues Filho and Evangelos Pournaras

• Hierarchy: Not authority and control, Not distribution. Do mean a multilevel system of different levels of abstraction with feedback loops between them
• Levels of abstraction allow availability through loss of irrelevant information
• Executes at different time scales
• Abstraction goes up, feedback goes down
• 
• 

Security Issues in Self-improving System Integration – Challenges and Solution Strategies Henner Heck, Bernhard Sick and Sven Tomforde

• Hardly manageable system structures
• Capabilities
  • Mutual influence detection
  • Mutual dependency detection
  • Emergence detection
  • Self-reflection
• Am I approaching the optimum?
• Am I degraded?
• Additional attack vectors targeting
  • self reflection
  • trust
  • mutual influence
  • adaptation

Improving Security and Interoperability of Interwoven Systems through Rigorous Selective Encapsulation of Critical Physical Resources
Phyllis Nelson

• DHS critical infrastructure sectors

Outriggers and Training Wheels for Cooperating Systems  – Christopher Landauer

• Kreitman’s Theorem?
• Training wheels are constraints on a system that prevent catastrophic failures.
• 
• Efficiency and robustness are direct competitors

A Concept for Proactive Knowledge Construction in Self-Learning Autonomous Systems Anthony Stein, Sven Tomforde, Ada Diaconescu, Jörg Hähner and Christian Müller-Schloer <- nice paper?

• Reactive knowledge creation = trial and error
• Proactive behavior = new knowledge created before it is needed
• MLOC architectures (Multi-Layer Observer/Controller)
• Drifting distributions leads to changes in the fitness function
• Suggested NNMF or actually tensor factorization as a way of filling in the tensor

Aspects of Measuring and Evaluating the Integration Status of a (Sub-)System at Runtime
Christian Gruhl, Sven Tomforde, and Bernhard Sick

• Entity and system-based evaluation

Coopetitive Soft Gating Ensemble
Jens Schreiber, Maarten Bieshaar, André Gensler, Bernhard Sick and Stephan Deist

• Ensemble estimators?
• Combinations of weighting and gating to do optimized selection of the best estimator from a collection of estimators that work in particular situations.
• 

Levels of Networked Self-awareness
Lukas Esterle (chair for next years’ SASO)  and John N.A. Brown

• Development sequence for individuals
  • Self aware
  • Interaction aware
  • Goal aware
  • Time aware
• Network awareness
  • Add meta-self aware?
• Some thoughts?
  • A social network model for the development of a ‘Theory of Mind’
  • Actor-network theory

SASO 2018

IEEE (f*) proceedings list (conference should show up here)

Keynote Gábor Vásárhelyi – CollMot Robotics, Budapest, Hungary (chair A. Montresor, room: FBK – Sala Stringa)

• Multi-level hierarchy in pigeons (gps), wild horses (drone video)
• Tight turns with pigeons rearrange order. Gentle turns maintain structure
• Universal rules of collective motion
• evolution is the best optimizer that they have found
• Hierarchy aids emergent optimization
• Drones are exploding in youth. Why? Fashion? Like AI in the ’70’s. Will it then crash?
• Salable in size and speed
• Tolerate noise, delay, and error tolerant
• Controllable meta-unit
• Drones need to be
  • Extensable
  • real-time
  • sensors
  • communication
  • simulation for dev and debugging
  • algorithms
• Enemies
  • delay
  • noise
  • inertia,
  • finite acceleration
  • communication errors
  • environment?
• Communication delays bring self-exited oscillation (repulsion-attraction temporal imbalance)
• Evolution is the second-best optimization function for all problems
• Solving decentralized traffic control. A sort of inverse flocking issue
• Anisotropic repulsion
• Selective alignment
• Publications
• Smaller and lighter (more maneuverable) flocks are more egalitarian. Heavier, smaller groups have hierarchies because there can be trust.

Rule-based utility-driven???
Sona Ghahremani, Christian Medeiros Adriano and Holger Giese

• Another hyperparameter approach. This one seems quite good
• Handles
  • Non-linearities
  • Uncertainty from dynamic archetecture
  • Black box models
• How to select the right model for a class of utility functions prior to deployment
• Graph grammar rules
• mRUBiS
  • mRUBiS as an exemplar for model-based architectural self-adaptation is now available at GitHub. It provides a simulator and architectural runtime model of mRUBiS to develop, evaluate, and compare self-adaptation solutions.

Model-Driven Elasticity Control for Multi-Server Queues Under Traffic Surges in Cloud Environments
Venkat Tadakamalla and Daniel Menasce

A Temporal Model for Interactive Diagnosis of Adaptive Systems
Ludovic Mouline, Amine Benelallam, Francois Fouquet, Johann Bourcier and Olivier Barais

• Might be a way of evaluating fitness tests?
• At least keep track of the last N (or whole history?) of chromosome so that the principal components can be analyzed.
• By comparing the states over time, we could find the states adjacent to problem states?

Towards Generic Adaptive Monitoring
Thomas Brand and Holger Giese

• Goal adaptation (fitness test adaptation?)

Implementing Feedback for ABM Meta-Modeling
Karan Budhraja and Tim Oates

PINCH: Self-Organized Context Neighborhoods for Smart Environments
Chenguang Liu, Christine Julien and Amy L. Murphy

• Neighborhood mesh networks
• everything is stored in a small beacon that is cooperatively shared in the neighborhood

A QoS-aware Adaptive Mobility Handling Approach for LoRa-based IoT Systems
Robbe Berrevoets and Danny Weyns

• LoRa communication

Reins-MAC: Firefly Inspired Communication Scheduling for Reliable Low-Power Wireless
Matteo Ceriotti and Amy L. Murphy

Marco Barbina – Leonardo SpA – Italy (chair E. Di Nitto, room: FBK – Sala Stringa)

• Sensors on UAVs
  • Electro-optic
  • Radar-based
  • Hyper-spectral(?)
• Sensors are not the problem, bandwidth is
• Selecting the relevant information
• Attention – which parts of the signal are important
  • Autoencoder NN – it’s like dropout?
  • Split domain stacked cnn. The front half (encoder) NN encodes the data and transmits the “compressed” data from the hidden layer to the back half of the NN.

Goal-aware Team Affiliation in Collectives of Autonomous Robots
Lukas Esterle

• Teamwork in nature
• Online multi-task k-assignment
• stampedes!
• Observer/follower == explore/exploit
• They did use the multi-armed bandit framework as a learning system.
  • Optimism in the Face of Uncertainty: the UCB1 Algorithm
• Real world implementation is intruder detection and response

Optimizing Transitions Between Abstract ABM Demonstrations (video presentation)
Brian Seipp, Karan Budhraja and Tim Oates

• What is the optimal path that connects two behaviors
• Traverse a behavior coordinate space
• Getting from a start image to a goal image. The image can “mean” many things. Agents provide the mapping?
• Intermediate states are shown
• Noise added to guarantee convergence?

Self-organized Resource Allocation for Reconfigurable Robot Ensembles
Julian Hanke, Oliver Kosak, Alexander Schiendorfer and Wolfgang Reif

• Search – determine dangers
• Continual observe
• React
• This is a fitness space analysis based on optimal configuration of robots for a given task. It’s kind of a distributed traveling salesman problem combined with a market and a bidder as a way of merging the distributed solution. I wonder if it is computationally better than a GA solution. It could be a way of looking at hyperparameter tuning?
• Not clear that it can handle a floating point value, like fuel level
• The goal is for the allocation to be fast

Panel: The Future of Autonomic Computing and Self-* Systems:

• Kristie Bellman – some success but in isolated, hard to define areas. Build foundation of principals that can be used to frame development
• Ada Diaconescu – Efficiency-oriented or creativity-oriented self* systems
• Simon Dobson – We have turned a lot of fields into computation. But – AI told us nothing about how people play Go. We’re trying to build, not describe
• Xiaohui (Helen) Gu – AIOps, bridging the gap between academia and industry
• Arif Merchant – (Google) Missing holistic, end-to-end solutions. First and follow-on principals.
• Danny Weyns – Be less ad-hoc and more scientific. Address threats to validity. Solid foundations.
• I talked a bit about the continuum from the low-hanging fruit of autonomic systems to the difficult task of creating self-aware systems.

A Macro-Level Order Metric for Self-Organizing Adaptive Systems
David King and Gilbert Peterson

• critical and stable states, based on local and global entropy
• I think that he is talking about “personalized” entropy rather than “local” entropy.

A Self-Organized Learning Model for Anomalies Detection: Application to Elderly People
Nicolas Verstaevel, Carole Bernon, Jean-Pierre Georgé and Marie-Pierre Gleizes

• Real time detection of abnormal behaviors by using feedback from the medical staff.
• Detect anomalies through a linear regression of disparity values
• Is this another hyperparameter system? It could be looking for sensitivity of the system to hyperparameter

Risk-based Testing of Self-Adaptive Systems using Run-Time Predictions
André Reichstaller and Alexander Knapp

• Uses machine learning to explore the space that includes errors. It is a way of finding major(?) features in the state space

Play with Generated Adversarial Networks (GANs) in your browser!

• First, we’re not visualizing anything as complex as generating realistic images. Instead, we’re showing a GAN that learns a distribution of points in just two dimensions. There’s no real application of something this simple, but it’s much easier to show the system’s mechanics. For one thing, probability distributions in plain old 2D (x,y) space are much easier to visualize than distributions in the space of high-resolution images.

SASO 2018

This works: eduroam wireless network Setup for Windows 10: wi-fi-eduroam-windows-10-en

Keynote 1 Paula ??? Privacy

• Privacy is a right of a human being that must be protected
• Privacy as soft paternalism Nudges for Privacy and Security ACM 2017
• Mangrove societies
• USACM EUACM ethics statements
• “big data protection ecosystems that shall involve developers, businesses, regulators and individuals in order to provide ‘ future-oriented regulation’, accountable controllers’, ‘privacy conscious engineering’, and ’empowered individuals'”.
• Lifting humans to be actors in the digital world by becoming autonomous systems that interact “au pair” with the rest of the digital world
• Floridi – Soft ethics in the governance of the digital. Philosophy and Technology March 2018
• Soft ethics is associated to individuals and hard ethics to systems, i.e. autonomous cars
• The two need to combine when an individual and a system interact

David Kurka Disobedience as a mechanism of change

• The Iron Law of Oligarchy
• The social concept of disobedience
• Phase plots ???
• All the work is currently based on global understandings of fairness? Future work is will look at subjective values within populations

Shuyue Hu –

• Coordination games induce social and individual interest
• Iterated prisoner’s dilemma but no strategy. Agents can decide to C or D based on the last round

Jeremy Pitt & Josiah Ober (Stanford)- Democracy by design

• Elinor Ostrom
• SimDemopolis (Specification and implementation)
• Brute facts and institutional facts
• Immutable and mutable rules
• Gini index keeps popping up. Use this math to reformat stampede/flock/nomad chart?

A Multi-Agent Elasticity Management Based On Multi-Tenant Debt Exchanges
Carlos Mera-Gómez, Francisco Ramírez, Rami Bahsoon and Rajkumar Buyya

• Stable matching approach (2012 Nobel economics)
• Burlap reinforcement learning
  • The Brown-UMBC Reinforcement Learning and Planning (BURLAP) java code library is for the use and development of single or multi-agent planning and learning algorithms and domains to accompany them. BURLAP uses a highly flexible system for defining states and and actions of nearly any kind of form, supporting discrete continuous, and relational domains. Planning and learning algorithms range from classic forward search planning to value function-based stochastic planning and learning algorithms. Also included is a set of analysis tools such as a common framework for the visualization of domains and agent performance in various domains.

Forecasting Models for Self-Adaptive Cloud Applications: A Comparative Study
Vladimir Podolskiy, Anshul Jindal, Michael Gerndt and Yury Oleynik

• Time series prediction algorithms
  • ARIMA –
  • GARCH – variance in the error term
  • Support Vector Regression
  • Singular Spectrum Analysys
• Interval Accuracy score