Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets - Wever, Marcel Dominik; van Rooijen, Lorijn; Hamann, Heiko (Evolutionary Computation)
In software engineering, the imprecise requirements of a user are transformed to a formal requirements specification during the requirements elicitation process. This process is usually guided by requirements engineers interviewing the user. We want to partially automate this first step of the software engineering process in order to enable users to specify a desired software system on their own. With our approach, users are only asked to provide exemplary behavioral descriptions. The problem of synthesizing a requirements specification from examples can partially be reduced to the problem of grammatical inference, to which we apply an active coevolutionary learning approach. However, this approach would usually require many feedback queries to be sent to the user. In this work, we extend and generalize our active learning approach to receive knowledge from multiple oracles, also known as proactive learning. The ‘user oracle’ represents input received from the user and the ‘knowledge oracle’ represents available, formalized domain knowledge. We call our two-oracle approach the ‘first apply knowledge then query’ (FAKT/Q) algorithm. We compare FAKT/Q to the active learning approach and provide an extensive benchmark evaluation. As result we find that the number of required user queries is reduced and the inference process is sped up significantly. Finally, with so-called On-The-Fly Markets, we present a motivation and an application of our approach where such knowledge is available.

Pick, Pack, & Survive: Charging Robots in a Modern Warehouse based on Online Connected Dominating Sets - Hamann, Heiko; Markarian, Christine; Meyer auf der Heide, Friedhelm; Wahby, Mostafa (Ninth International Conference on Fun with Algorithms (FUN))

From User Demand to Software Service: Using Machine Learning to Automate the Requirements Specification Process - van Rooijen, Lorijn; Bäumer, Frederik Simon; Platenius, Marie Christin; Geierhos, Michaela; Hamann, Heiko; Engels, Gregor (2017 IEEE 25th International Requirements Engineering Conference Workshops (REW))
Bridging the gap between informal, imprecise, and vague user requirements descriptions and precise formalized specifications is the main task of requirements engineering. Techniques such as interviews or story telling are used when requirements engineers try to identify a user's needs. The requirements specification process is typically done in a dialogue between users, domain experts, and requirements engineers. In our research, we aim at automating the specification of requirements. The idea is to distinguish between untrained users and trained users, and to exploit domain knowledge learned from previous runs of our system. We let untrained users provide unstructured natural language descriptions, while we allow trained users to provide examples of behavioral descriptions. In both cases, our goal is to synthesize formal requirements models similar to statecharts. From requirements specification processes with trained users, behavioral ontologies are learned which are later used to support the requirements specification process for untrained users. Our research method is original in combining natural language processing and search-based techniques for the synthesis of requirements specifications. Our work is embedded in a larger project that aims at automating the whole software development and deployment process in envisioned future software service markets.
Keywords: Software; Unified modeling language; Requirements engineering; Ontologies; Search problems; Natural languages

An Evolutionary Robotics Approach to the Control of Plant Growth and Motion: Modeling Plants and Crossing the Reality Gap - Wahby, Mostafa; Hofstadler, Daniel Nicolas; Heinrich, Mary Katherine; Zahadat, Payam; Hamann, Heiko (Proc. of the 10th International Conference on Self-Adaptive and Self-Organizing Systems)
The self-organizing bio-hybrid collaboration ofrobots and natural plants allows for a variety of interestingapplications. As an example we investigate how robots can beused to control the growth and motion of a natural plant, using LEDs to provide stimuli. We follow an evolutionaryrobotics approach where task performance is determined bymonitoring the plant's reaction. First, we do initial plantexperiments with simple, predetermined controllers. Then weuse image sampling data as a model of the dynamics ofthe plant tip xy position. Second, we use this approach toevolve robot controllers in simulation. The task is to makethe plant approach three predetermined, distinct points in anxy-plane. Finally, we test the evolved controllers in real plantexperiments and find that we cross the reality gap successfully. We shortly describe how we have extended from plant tipto many points on the plant, for a model of the plant stemdynamics. Future work will extend to two-axes image samplingfor a 3-d approach.

Self-Organized Construction with Continuous Building Material: Higher Flexibility Based on Braided Structures - Heinrich, Mary Katherine; Wahby, Mostafa; Divband Soorati, Mohammad; Hofstadler, Daniel Nicolas; Zahadat, Payam; Ayres, Phil; Stoy, Kasper; Hamann, Heiko (Proc. of the 1st International Workshop on Self-Organising Construction (SOCO))

Hybrid Societies: Challenges and Perspectives in the Design of Collective Behavior in Self-organizing Systems - Hamann, Heiko; Khaluf, Yara; Botev, Jean; Divband Soorati, Mohammad; Ferrante, Eliseo; Kosak, Oliver; Montanier, Jean-Marc; Mostaghim, Sanaz; Redpath, Richard; Timmis, Jon; Veenstra, Frank; Wahby, Mostafa; et al. (Frontiers in Robotics and AI)

Robot Self-Assembly as Adaptive Growth Process: Collective Selection of Seed Position and Self-Organizing Tree-Structures - Hamann, Heiko; Divband Soorati, Mohammad (IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2016))

Collective Decision with 100 Kilobots: Speed vs Accuracy in Binary Discrimination Problems - Dorigo, Marco; Hamann, Heiko; Valentini, Gabriele; Ferrante, Eliseo (Journal of Autonomous Agents and Multi-Agent Systems)

Population Coding: A New Design Paradigm for Embodied Distributed Systems - Hamann, Heiko; Valentini, Gabriele; Dorigo, Marco (10th Int. Conf. on Swarm Intelligence, ANTS 2016)

Robot Gardens: An Augmented Reality Prototype for Plant-Robot Biohybrid Systems - von Mammen, Sebastian; Hamann, Heiko; Heider, Michael (ACM Symposium on Virtual Reality Software and Technology (VRST))

Inspiration-Triggered Search: Towards Higher Complexities by Mimicking Creative Processes - Rybář, Milan; Hamann, Heiko (Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2016))

On the Definition of Self-organizing Systems: Relevance of Positive/Negative Feedback and Fluctuations - Khaluf, Yara; Hamann, Heiko (ANTS 2016)

Collective Perception of Environmental Features in a Robot Swarm - Valentini, Gabriele; Brambilla, Davide; Hamann, Heiko; Dorigo, Marco (10th Int. Conf. on Swarm Intelligence, ANTS 2016)

Patterns for Constructing Mutation Operators: Limiting the Search Space in a Software Engineering Application - Kühne, Thomas; Hamann, Heiko; Arifulina, Svetlana; Engels, Gregor (Proceedings of the 19th European Conference on Genetic Programming (EuroGP 2016))
We apply methods of genetic programming to a general problem from software engineering, namely example-based generation of specifications. In particular, we focus on model transformation by example. The definition and implementation of model transformations is a task frequently carried out by domain experts, hence, a (semi-)automatic approach is desirable. This application is challenging because the underlying search space has rich semantics, is high-dimensional, and unstructured. Hence, a computationally brute-force approach would be unscalable and potentially infeasible. To address that problem, we develop a sophisticated approach of designing complex mutation operators. We define ‘patterns’ for constructing mutation operators and report a successful case study. Furthermore, the code of the evolved model transformation is required to have high maintainability and extensibility, that is, the code should be easily readable by domain experts. We report an evaluation of this approach in a software engineering case study.

On the Tradeoff between Hardware Protection and Optimization Success: A Case Study in Onboard Evolutionary Robotics for Autonomous Parallel Parking - Wahby, Mostafa; Hamann, Heiko (Applications of Evolutionary Computation (EvoApplications 2015))

Flora Robotica - Mixed Societies of Symbiotic Robot-Plant Bio-Hybrids - Hamann, Heiko; Wahby, Mostafa; Schmickl, Thomas; Zahadat, Payam; Hofstadler, Daniel; Stoy, Kasper; Risi, Sebastian; Faina, Andres; Veenstra, Frank; Kernbach, Serge; Kuksin, Igor; Kernbach, Olga; et al. (Proceedings of the 2015 IEEE Symposium on Artificial Life (IEEE ALIFE'15))
Besides the life-as-it-could-be driver of artificial life research there is also the concept of extending natural life by creating hybrids or mixed societies that are built from natural and artificial components. In this paper we motivate and present the research program of the project flora robotica. Our objective is to develop and to investigate closely linked symbiotic relationships between robots and natural plants and to explore the potentials of a plant-robot society able to produce architectural artifacts and living spaces. These robot-plant bio-hybrids create synergies that allow for new functions of plants and robots. They also create novel design opportunities for an architecture that fuses the design and construction phase. The bio-hybrid is an example of mixed societies between 'hard' artificial and 'wet' natural life, which enables an interaction between natural and artificial ecologies. They form an embodied, self-organizing, and distributed cognitive system which is supposed to grow and develop over long periods of time resulting in the creation of meaningful architectural structures. A key idea is to assign equal roles to robots and plants in order to create a highly integrated, symbiotic system. Besides the gain of knowledge, this project has the objective to create a bio-hybrid system with a defined function and application -- growing architectural artifacts.

Lessons from Speciation Dynamics: How to Generate Selective Pressure Towards Diversity - Hamann, Heiko (Artificial Life)
Recent approaches in evolutionary robotics (ER) propose to generate behavioral diversity in order to evolve desired behaviors more easily. These approaches require the definition of a behavioral distance, which often includes task-specific features and hence a priori knowledge. Alternative methods, which do not explicitly force selective pressure towards diversity (SPTD) but still generate it, are known from the field of artificial life, such as in artificial ecologies (AEs). In this study, we investigate how SPTD is generated without task-specific behavioral features or other forms of a priori knowledge and detect how methods of generating SPTD can be transferred from the domain of AE to ER. A promising finding is that in both types of systems, in systems from ER that generate behavioral diversity and also in the investigated speciation model, selective pressure is generated towards unpopulated regions of search space. In a simple case study we investigate the practical implications of these findings and point to options for transferring the idea of self-organizing SPTD in AEs to the domain of ER.

Revisiting BEECLUST: Aggregation of Swarm Robots with Adaptiveness to Different Light Settings - Wahby, Mostafa; Weinhold, Alexander; Hamann, Heiko (Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS))
Aggregation is a crucial task in swarm robotics to ensure cooperation. We investigate the task of aggregation on an area specified indirectly by certain environmental features, here it is a light distribution. We extend the original BEECLUST algorithm, that implements an aggregation behavior, to an adaptive variant that automatically adapts to any light conditions. We compare these two control algorithms in a number of swarm robot experiments with different light conditions. The improved, adaptive variant is found to be significantly better in the tested setup.

Evolution of Controllers for Robot-Plant Bio-Hybdrids: A Simple Case Study Using a Model of Plant Growth and Motion - Wahby, Mostafa; Divband Soorati, Mohammad; von Mammen, Sebastian; Hamann, Heiko (Proceedings. 25. Computational Intelligence Workshop)

The Effect of Fitness Function Design on Performance in Evolutionary Robotics: The Influence of a Priori Knowledge - Hamann, Heiko; Divband Soorati, Mohammad (Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2015))
Fitness function design is known to be a critical feature of the evolutionary-robotics approach. Potentially, the complexity of evolving a successful controller for a given task can be reduced by integrating a priori knowledge into the fitness function which complicates the comparability of studies in evolutionary robotics. Still, there are only few publications that study the actual effects of different fitness functions on the robot's performance. In this paper, we follow the fitness function classification of Nelson et al. (2009) and investigate a selection of four classes of fitness functions that require different degrees of a priori knowledge. The robot controllers are evolved in simulation using NEAT and we investigate different tasks including obstacle avoidance and (periodic) goal homing. The best evolved controllers were then post-evaluated by examining their potential for adaptation, determining their convergence rates, and using cross-comparisons based on the different fitness function classes. The results confirm that the integration of more a priori knowledge can simplify a task and show that more attention should be paid to fitness function classes when comparing different studies.

Evolving Collective Behaviors With Diverse But Predictable Sensor States - Hamann, Heiko; Schmickl, Thomas; Zahadat, Payam (13th European Conference on Artificial Life (ECAL 2015))

Probabilistic Modeling of Swarming Systems - Hamann, Heiko; Correll, Nikolaus; Kacprzyk, Janusz; Pedrycz, Witold (Springer Handbook of Computational Intelligence)

Dependability in Swarm Robotics: Error Detection and Correction - Ding, Hongli; Hamann, Heiko (First International Symposium on Swarm Behavior and Bio-Inspired Robotics (SWARM 2015))

Potential of Heterogeneity in Collective Behaviors: A Case Study on Heterogeneous Swarms - Hamann, Heiko; Schmickl, Thomas; Kengyel, Daniela; Zahadat, Payam; Radspieler, Gerald; Wotawa, Franz (Principles and Practice of Multi-Agent Systems (PRIMA 2015))

Time-variant feedback processes in collective decision-making systems: influence and effect of dynamic neighborhood sizes - Valentini, Gabriele; Hamann, Heiko (Swarm Intelligence)

Efficient Decision-Making in a Self-Organizing Robot Swarm: On the Speed Versus Accuracy Trade-Off - Dorigo, Marco; Hamann, Heiko; Valentini, Gabriele (Proceedings of the 14th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS 2015))

Self-organized collective decisions in a robot swarm - Dorigo, Marco; Hamann, Heiko; Valentini, Gabriele (AAAI-15 Video Proceedings)

The common stomach: Organizing task allocation in wasp societies - Hamann, Heiko; Karsai, Istvan; Schmickl, Thomas; Hilbun, Allison (Symposium on Biomathematics and Ecology: Education and Research)

Swarm in a Fly Bottle: Feedback-Based Analysis of Self-organizing Temporary Lock-ins - Hamann, Heiko; Valentini, Gabriele (Ninth Int. Conf. on Swarm Intelligence (ANTS 2014))

Analysis of Swarm Behaviors Based on an Inversion of the Fluctuation Theorem - Hamann, Heiko; Schmickl, Thomas; Crailsheim, Karl (Artificial Life)
A grand challenge in the field of artificial life is to find a general theory of emergent self-organizing systems. In swarm systems most of the observed complexity is based on motion of simple entities. Similarly, statistical mechanics focuses on collective properties induced by the motion of many interacting particles. In this article we apply methods from statistical mechanics to swarm systems. We try to explain the emergent behavior of a simulated swarm by applying methods based on the fluctuation theorem. Empirical results indicate that swarms are able to produce negative entropy within an isolated subsystem due to frozen accidents. Individuals of a swarm are able to locally detect fluctuations of the global entropy measure and store them, if they are negative entropy productions. By accumulating these stored fluctuations over time the swarm as a whole is producing negative entropy and the system ends up in an ordered state. We claim that this indicates the existence of an inverted fluctuation theorem for emergent self-organizing dissipative systems. This approach bears the potential of general applicability.

Self-Organized Collective Decision Making: The Weighted Voter Model - Dorigo, Marco; Hamann, Heiko; Valentini, Gabriele; Lomuscio, Alessio; Scerri, Paul; Bazzan, Ana; Huhns, Michael (Proceedings of the 13th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS 2014))
Collective decision making in self-organized systems is challenging because it relies on local perception and local communication. Globally defined qualities such as consensus time and decision accuracy are both difficult to predict and difficult to guarantee. We present the weighted voter model which implements a self-organized collective decision making process. We provide an ODE model, a master equation model (numerically solved by the Gillespie algorithm), and agent-based simulations of the proposed decision-making strategy. This set of models enables us to investigate the system behavior in the thermodynamic limit and to investigate finite-size effects due to random fluctuations. Based on our results, we give minimum requirements to guarantee consensus on the optimal decision, a minimum swarm size to guarantee a certain accuracy, and we show that the proposed approach scales with system size and is robust to noise.

Evolving Prediction Machines: Collective Behaviors Based on Minimal Surprisal - Hamann, Heiko (Int. Conf. on Genetic and Evolutionary Computation (GECCO 2014))

Sorting in Swarm Robots Using Communication-Based Cluster Size Estimation - Birattari, Mauro; Dorigo, Marco; Hamann, Heiko; Garnier, Simon; Montes de Oca, Marco; Solnon, Christine; Stuetzle, Thomas; Ding, Hongli (Ninth Int. Conf. on Swarm Intelligence (ANTS 2014))

Derivation of a Micro-Macro Link for Collective Decision-Making Systems: Uncover Network Features Based on Drift Measurements - Khaluf, Yara; Dorigo, Marco; Hamann, Heiko; Valentini, Gabriele; Bartz-Beielstein, T. (13th International Conference on Parallel Problem Solving from Nature (PPSN 2014))

Evolution of Collective Behaviors by Minimizing Surprise - Hamann, Heiko; Sayama, Hiroki; Rieffel, John; Risi, Sebastian; Doursat, Rene; Lipson, Hod (14th Int. Conf. on the Synthesis and Simulation of Living Systems (ALIFE 2014))

A Swarm Robotics Approach to Task Allocation Under Soft Deadlines and Negligible Switching Costs - Cervera, Enric; Khaluf, Yara; Birattari, Mauro; Hamann, Heiko; Pobil, Angel P. del; Chinellato, Eris; Martinez-Martin, Ester; Hallam, John; Morales, Antonio (Simulation of Adaptive Behavior (SAB 2014))

Automatic Reconstruction of Polygonal Room Models from 3D Point Clouds - Mertsching, Bärbel; Divband Soorati, Mohammad; Kotthauser, Tobias (IEEE International Conference on Robotics and Biomimetics (ROBIO))

Time delay implies cost on task switching: A model to investigate the efficiency of task partitioning - Hamann, Heiko; Karsai, Istvan; Schmickl, Thomas (Bulletin of Mathematical Biology)

Algorithmic Requirements for Swarm Intelligence in Differently Coupled Collective Systems - Hamann, Heiko; Schmickl, Thomas; Stradner, Jürgen; Crailsheim, Karl; Thenius, Ronald; Zahadat, Payam (Chaos, Solitons & Fractals)

Virtual Spatiality in Agent Controllers: Encoding Compartmentalization - Hamann, Heiko; Schmickl, Thomas; Stradner, Jürgen; Schwarzer, Christopher; Michiels, Nico K.; Esparcia-Alcazar, Anna Isabel (Applications of Evolutionary Computation - 16th European Conference (EvoApplications 2013))

A Reductionist Approach to Hypothesis-Catching for the Analysis of Self-Organizing Decision-Making Systems - Hamann, Heiko (7th IEEE Int. Conf. on Self-Adaptive and Self-Organizing Systems (SASO 2013))

Speciation Dynamics: Generating Selective Pressure Towards Diversity - Hamann, Heiko; Lio, Pietro; Miglino, Orazio; Nicosia, Giuseppe; Nolfi, Stefano; Pavone, Mario (12th European Conference on Artificial Life (ECAL 2013))

Towards Swarm Calculus: Urn Models of Collective Decisions and Universal Properties of Swarm Performance - Hamann, Heiko (Swarm Intelligence)

Impact of Neuron Models and Network Structure on Evolving Modular Robot Neural Network Controllers - Hamann, Heiko; Stradner, Jürgen; Bredeche, Nicolas; Cazenille, Leo (14th Annual Genetic and Evolutionary Computation Conference, GECCO 2012)
This paper investigates the properties required to evolve Artificial Neural Networks for distributed control in modular robotics, which typically involves non-linear dynamics and complex interactions in the sensori-motor space. We investigate the relation between macro-scale properties (such as modularity and regularity) and micro-scale properties in Neural Network controllers. We show how neurons capable of multiplicative-like arithmetic operations may increase the performance of controllers in several ways whenever challenging control problems with non-linear dynamics are involved. This paper provides evidence that performance and robustness of evolved controllers can be improved by a combination of carefully chosen micro- and macro-scale neural network properties.

Towards Morphological Flexibility: Modular Robotics and Bio-inspired Control - Hamann, Heiko; Schmickl, Thomas; Stradner, Jürgen (Austrian Robotics Workshop (Operational Programme Slovenia-Austria))
As a contribution to the efforts towards robotic systems of higher flexibility we present our concept of morphologically dynamic robots. Within the projects SYMBRION and REPLICATOR, that focus on modular robotics, we have developed bio-inspired control techniques to achieve new concepts of dynamic, autonomous morphological structures. We propose three modes of coupling between robot modules: swarm, team, and organism mode. We demonstrate our concepts along with simple robot experiments.

On-line, On-board Evolution of Reaction-Diffusion Control for Self-Adaptation - Hamann, Heiko; Schmickl, Thomas; Stradner, Jürgen; Crailsheim, Karl; Zahadat, Payam; Adami, Christoph; Bryson, David M.; Ofria, Charles; Pennock, Robert T. (Alife XIII)

Self-organized pattern formation in a swarm system as a transient phenomenon of non-linear dynamics - Hamann, Heiko; Schmickl, Thomas; Crailsheim, Karl (Mathematical and Computer Modelling of Dynamical Systems)

A Hormone-Based Controller for Evaluation-Minimal Evolution in Decentrally Controlled Systems - Hamann, Heiko; Schmickl, Thomas; Crailsheim, Karl (Artificial Life)
One of the main challenges in automatic controller synthesis is to develop methods that can successfully be applied for complex tasks. The difficulty is increased even more in the case of settings with multiple interacting agents. We apply the artificial homeostatic hormone system (AHHS) approach, which is inspired by the signaling network of unicellular organisms, to control a system of several independently acting agents decentrally. The approach is designed for evaluation-minimal, artificial evolution in order to be applicable to complex modular robotics scenarios. The performance of AHHS controllers is compared with neuroevolution of augmenting topologies (NEAT) in the coupled inverted pendulums benchmark. AHHS controllers are found to be better for multimodular settings. We analyze the evolved controllers with regard to the usage of sensory inputs and the emerging oscillations, and we give a nonlinear dynamics interpretation. The generalization of evolved controllers to initial conditions far from the original conditions is investigated and found to be good. Similarly, the performance of controllers scales well even with module numbers different from the original domain the controller was evolved for. Two reference implementations of a similar controller approach are reported and shown to have shortcomings. We discuss the related work and conclude by summarizing the main contributions of our work.

Analysis of emergent symmetry breaking in collective decision making - Hamann, Heiko; Schmickl, Thomas; Wörn, Heinz; Crailsheim, Karl (Neural Computing and Applications)

Towards Swarm Calculus: Universal Properties of Swarm Performance and Collective Decisions - Hamann, Heiko; Engelbrecht, Andreas; Birattari, Mauro; Dorigo, Marco; Blum, Christian; Stuetzle, Thomas; Christensen, Anders Lyhne; Gross, Roderich (Swarm Intelligence: 8th International Conference, ANTS 2012)