56 research outputs found
Evolving neural networks to follow trajectories of arbitrary complexity
Many experiments have been performed that use evolutionary algorithms for learning the topology and connection weights of a neural network that controls a robot or virtual agent. These experiments are not only performed to better understand basic biological principles, but also with the hope that with further progress of the methods, they will become competitive for automatically creating robot behaviors of interest. However, current methods are limited with respect to the (Kolmogorov) complexity of evolved behavior. Using the evolution of robot trajectories as an example, we show that by adding four features, namely (1) freezing of previously evolved structure, (2) temporal scaffolding, (3) a homogeneous transfer function for output nodes, and (4) mutations that create new pathways to outputs, to standard methods for the evolution of neural networks, we can achieve an approximately linear growth of the complexity of behavior over thousands of generations. Overall, evolved complexity is up to two orders of magnitude over that achieved by standard methods in the experiments reported here, with the major limiting factor for further growth being the available run time. Thus, the set of methods proposed here promises to be a useful addition to various current neuroevolution methods
A Framework Using Tangible Interaction for Automatically Capturing and Embedding Design Intent in Parametric Models
The objective of this research is to address some of the challenges of parametric design associated with defining a model’s frameworks using mathematics and computer programming. This work proposes a tactile-based approach to automate the generation of such information. A design-based research method is implemented for this work, which involves developing research prototypes consisting of Tangible User-Interfaces (TUIs) to demonstrate and test the digital-physical workflow. Five prototypes were created each generating a type of information for setting up parametric models, including; linear and polynomial mathematical equations, algorithmic rules and seed configurations for a Cellular Automata (CA) component, geometric transformations (single and compound), and Non-Uniform Rational Basis Spline (NURBS) objects. During the progress of the work, prototypes were improved to include a higher level of automation by performing multiple and more complex modeling tasks.
This research includes two levels of evaluation. The first is system correctness, which tests the prototypes for translating tangible interaction with design objects into modeling information. The second is a qualitative comparison between the developed method and the conventional parametric modeling approach using graph-based and/or text-based programming applications. The results of the research have shown the plausibility of the workflow and its potential benefits for parametric modeling practice and education. This work provides a proof-of-concept for a novel approach that translates design intents into mathematical and algorithmic modeling information for
establishing parametric frameworks. The outcomes of this research include; detailed workflows describing algorithmic procedures for interpreting analog data, TUI specifications, and an overall theoretical framework of the method
Behavior Trees in Robotics and AI: An Introduction
A Behavior Tree (BT) is a way to structure the switching between different
tasks in an autonomous agent, such as a robot or a virtual entity in a computer
game. BTs are a very efficient way of creating complex systems that are both
modular and reactive. These properties are crucial in many applications, which
has led to the spread of BT from computer game programming to many branches of
AI and Robotics. In this book, we will first give an introduction to BTs, then
we describe how BTs relate to, and in many cases generalize, earlier switching
structures. These ideas are then used as a foundation for a set of efficient
and easy to use design principles. Properties such as safety, robustness, and
efficiency are important for an autonomous system, and we describe a set of
tools for formally analyzing these using a state space description of BTs. With
the new analysis tools, we can formalize the descriptions of how BTs generalize
earlier approaches. We also show the use of BTs in automated planning and
machine learning. Finally, we describe an extended set of tools to capture the
behavior of Stochastic BTs, where the outcomes of actions are described by
probabilities. These tools enable the computation of both success probabilities
and time to completion
Reward is not Necessary: How to Create a Compositional Self-Preserving Agent for Life-Long Learning
We introduce a physiological model-based agent as proof-of-principle that it
is possible to define a flexible self-preserving system that does not use a
reward signal or reward-maximization as an objective. We achieve this by
introducing the Self-Preserving Agent (SPA) with a physiological structure
where the system can get trapped in an absorbing state if the agent does not
solve and execute goal-directed polices. Our agent is defined using new class
of Bellman equations called Operator Bellman Equations (OBEs), for encoding
jointly non-stationary non-Markovian tasks formalized as a Temporal Goal Markov
Decision Process (TGMDP). OBEs produce optimal goal-conditioned spatiotemporal
transition operators that map an initial state-time to the final state-times of
a policy used to complete a goal, and can also be used to forecast future
states in multiple dynamic physiological state-spaces. SPA is equipped with an
intrinsic motivation function called the valence function, which quantifies the
changes in empowerment (the channel capacity of a transition operator) after
following a policy. Because empowerment is a function of a transition operator,
there is a natural synergism between empowerment and OBEs: the OBEs create
hierarchical transition operators, and the valence function can evaluate
hierarchical empowerment change defined on these operators. The valence
function can then be used for goal selection, wherein the agent chooses a
policy sequence that realizes goal states which produce maximum empowerment
gain. In doing so, the agent will seek freedom and avoid internal death-states
that undermine its ability to control both external and internal states in the
future, thereby exhibiting the capacity of predictive and anticipatory
self-preservation. We also compare SPA to Multi-objective RL, and discuss its
capacity for symbolic reasoning and life-long learning.Comment: 54 page
Combining SOA and BPM Technologies for Cross-System Process Automation
This paper summarizes the results of an industry case study that introduced a cross-system business process automation solution based on a combination of SOA and BPM standard technologies (i.e., BPMN, BPEL, WSDL). Besides discussing major weaknesses of the existing, custom-built, solution and comparing them against experiences with the developed prototype, the paper presents a course of action for transforming the current solution into the proposed solution. This includes a general approach, consisting of four distinct steps, as well as specific action items that are to be performed for every step. The discussion also covers language and tool support and challenges arising from the transformation
Competent Program Evolution, Doctoral Dissertation, December 2006
Heuristic optimization methods are adaptive when they sample problem solutions based on knowledge of the search space gathered from past sampling. Recently, competent evolutionary optimization methods have been developed that adapt via probabilistic modeling of the search space. However, their effectiveness requires the existence of a compact problem decomposition in terms of prespecified solution parameters. How can we use these techniques to effectively and reliably solve program learning problems, given that program spaces will rarely have compact decompositions? One method is to manually build a problem-specific representation that is more tractable than the general space. But can this process be automated? My thesis is that the properties of programs and program spaces can be leveraged as inductive bias to reduce the burden of manual representation-building, leading to competent program evolution. The central contributions of this dissertation are a synthesis of the requirements for competent program evolution, and the design of a procedure, meta-optimizing semantic evolutionary search (MOSES), that meets these requirements. In support of my thesis, experimental results are provided to analyze and verify the effectiveness of MOSES, demonstrating scalability and real-world applicability
(I) A Declarative Framework for ERP Systems(II) Reactors: A Data-Driven Programming Model for Distributed Applications
To those who can be swayed by argument and those who know they do not have all the answers This dissertation is a collection of six adapted research papers pertaining to two areas of research. (I) A Declarative Framework for ERP Systems: • POETS: Process-Oriented Event-driven Transaction Systems. The paper describes an ontological analysis of a small segment of the enterprise domain, namely the general ledger and accounts receivable. The result is an event-based approach to designing ERP systems and an abstract-level sketch of the architecture. • Compositional Specification of Commercial Contracts. The paper de-scribes the design, multiple semantics, and use of a domain-specific lan-guage (DSL) for modeling commercial contracts. • SMAWL: A SMAll Workflow Language Based on CCS. The paper show
The Essence of Software Engineering
Software Engineering; Software Development; Software Processes; Software Architectures; Software Managemen
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