20 research outputs found
Spatial Assembly: Generative Architecture With Reinforcement Learning, Self Play and Tree Search
With this work, we investigate the use of Reinforcement Learning (RL) for the
generation of spatial assemblies, by combining ideas from Procedural Generation
algorithms (Wave Function Collapse algorithm (WFC)) and RL for Game Solving.
WFC is a Generative Design algorithm, inspired by Constraint Solving. In WFC,
one defines a set of tiles/blocks and constraints and the algorithm generates
an assembly that satisfies these constraints. Casting the problem of generation
of spatial assemblies as a Markov Decision Process whose states transitions are
defined by WFC, we propose an algorithm that uses Reinforcement Learning and
Self-Play to learn a policy that generates assemblies that maximize objectives
set by the designer. Finally, we demonstrate the use of our Spatial Assembly
algorithm in Architecture Design.Comment: Workshop on Machine Learning for Creativity and Design at the 34rd
Conference on Neural Information Processing Systems (NeurIPS 2020
Compositional Model Conversion
This dissertation presents an initial work towards the development of a technique
to convert compositional models from one modelling paradigm to another,
by means of a pair of equivalent compositional modelling domain theories.
The mapping between model fragments of the two domain theories is
not necessarily in a one-to-one manner. It might be the case that a model
fragment in one domain theory covers parts of several model fragments in
the other domain theory. This is one of the major conversion problems that
this technique will focus on.
The compositional modelling of ecological systems is used as a testing
domain for the implemented conversion technique. For this work, system
dynamics and object-oriented representations are the two modelling
paradigms adopted. The major intention of this conversion application, implemented
in the C++ programming language, is to convert a system dynamics
model, composed through a compositional modelling technique, to
an object-oriented model. The resulting object-oriented model is expected
to reflect the same scenario, but with a different representation, compared
to the model produced within the system dynamics modelling paradigm
My doctor: dynamic scheduling and communication
This thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2016.Cataloged from PDF version of thesis report.Includes bibliographical references (page 44-46).Our proposition is to make an online system which will appoint a doctor to a patient dynamically and patients can speak with specialists over on the web. In today’s world on the off chance that somebody needs to take an arrangement to a specialist needs to go to hospital or clinic physically and then make the arrangement. This devours valuable time of the patient. Additionally, if the specialist crosses out his/her calendar, the patient does not come to know about it unless he/she goes to the center. Other than this, individuals living in significant urban areas have access to quality specialists yet have to struggle because of the absence of extra time and substantial congested road. However, individuals living in rural areas don't have legitimate medical support inside their range. To minimize the issue, we are attempting to build up an online system where patients can have quality medical administration from qualified specialists everywhere throughout the nation. Specialists will straightforwardly communicate with the patient through chat server. Furthermore, the system will be a stage for new specialists. The specialist will come to know a number of patients he needs to consult in the entire day. Our system "My Doctor" will eliminate the receptionist’s paperwork. We have utilized data compression to lessen the amount of data stored in the system. Past records are being saved into the patient's profile from where he/she can reappoint a previously consulted doctor.Ashraf Hasan SirajeeMasrur Masqub UtsashSatabdi Rani DebiB. Computer Science and Engineerin
Temporal Logic Motion Planning in Partially Unknown Environments
This thesis considers the problem of a robot with complex dynamics navigating a partially discovered environment to satisfy a temporal logic formula consisting of both a co-safety formula component and a safety formula component. We employ a multi-layered synergistic framework for planning motions to satisfy a temporal logic formula, and we combine with it an iterative replanning strategy to locally patch the robot's discretized internal representation of the workspace whenever a new obstacle is discovered. Furthermore, we introduce a notion of ``closeness'' of satisfaction of a linear temporal logic formula, defined by a metric over the states of the corresponding automaton. We employ this measure to maximize partial satisfaction of the co-safety component of the temporal logic formula when obstacles render it unsatisfiable. For the safety component of the specification, we do not allow partial satisfaction. This introduces a general division between ``soft'' and ``hard'' constraints in the temporal logic specification, a concept we illustrate in our discussion of future work.
The novel contributions of this thesis include (1) the iterative replanning strategy, (2) the support for safety formulas in the temporal logic specification, (3) the method to locally patch the discretized workspace representation, and (4) support for partial satisfaction of unsatisfiable co-safety formulas. As our experimental results show, these methods allow us to quickly compute motion plans for robots with complex dynamics to satisfy rich temporal logic formulas in partially unknown environments
Approximate model composition for explanation generation
This thesis presents a framework for the formulation of knowledge models to sup¬
port the generation of explanations for engineering systems that are represented by the
resulting models. Such models are automatically assembled from instantiated generic
component descriptions, known as modelfragments. The model fragments are of suffi¬
cient detail that generally satisfies the requirements of information content as identified
by the user asking for explanations.
Through a combination of fuzzy logic based evidence preparation, which exploits the
history of prior user preferences, and an approximate reasoning inference engine, with
a Bayesian evidence propagation mechanism, different uncertainty sources can be han¬
dled. Model fragments, each representing structural or behavioural aspects of a com¬
ponent of the domain system of interest, are organised in a library. Those fragments
that represent the same domain system component, albeit with different representation
detail, form parts of the same assumption class in the library. Selected fragments are
assembled to form an overall system model, prior to extraction of any textual infor¬
mation upon which to base the explanations. The thesis proposes and examines the
techniques that support the fragment selection mechanism and the assembly of these
fragments into models.
In particular, a Bayesian network-based model fragment selection mechanism is de¬
scribed that forms the core of the work. The network structure is manually determined
prior to any inference, based on schematic information regarding the connectivity of
the components present in the domain system under consideration. The elicitation
of network probabilities, on the other hand is completely automated using probability
elicitation heuristics. These heuristics aim to provide the information required to select
fragments which are maximally compatible with the given evidence of the fragments
preferred by the user. Given such initial evidence, an existing evidence propagation
algorithm is employed. The preparation of the evidence for the selection of certain
fragments, based on user preference, is performed by a fuzzy reasoning evidence fab¬
rication engine. This engine uses a set of fuzzy rules and standard fuzzy reasoning
mechanisms, attempting to guess the information needs of the user and suggesting the selection of fragments of sufficient detail to satisfy such needs. Once the evidence
is propagated, a single fragment is selected for each of the domain system compo¬
nents and hence, the final model of the entire system is constructed. Finally, a highly
configurable XML-based mechanism is employed to extract explanation content from
the newly formulated model and to structure the explanatory sentences for the final
explanation that will be communicated to the user.
The framework is illustratively applied to a number of domain systems and is compared
qualitatively to existing compositional modelling methodologies. A further empirical
assessment of the performance of the evidence propagation algorithm is carried out to
determine its performance limits. Performance is measured against the number of frag¬
ments that represent each of the components of a large domain system, and the amount
of connectivity permitted in the Bayesian network between the nodes that stand for
the selection or rejection of these fragments. Based on this assessment recommenda¬
tions are made as to how the framework may be optimised to cope with real world
applications
Robustness and stability in dynamic constraint satisfaction problems
Constraint programming is a paradigm wherein relations between variables are stated in the form of constraints. It is well-known that many real life problems can be modeled as Constraint Satisfaction Problems (CSPs). Much effort has been spent to increase the efficiency of algorithms for solving CSPs. However, many of these techniques assume that the set of variables, domains and constraints involved in the CSP are known and fixed when the problem is modeled. This is a strong limitation because many problems come from uncertain and dynamic environments, where both the original problem may evolve because of the environment, the user or other agents. In such situations, a solution that holds for the original problem can become invalid after changes.
There are two main approaches for dealing with these situations: reactive and proactive approaches. Using reactive approaches entails re-solving the CSP after each solution loss, which is a time consuming. That is a clear disadvantage, especially when we deal with short-term changes, where solution loss is frequent. In addition, in many applications, such as on-line planning and scheduling, the delivery time of a new solution may be too long for actions to be taken on time, so a solution loss can produce several negative effects in the modeled problem. For a task assignment production system with several machines, it could cause the shutdown of the production system, the breakage of machines, the loss of the material/object in production, etc. In a transport timetabling problem, the solution loss, due to some disruption at a point, may produce a delay that propagates through the entire schedule. In addition, all the negative effects stated above will probably entail an economic loss.
In this thesis we develop several proactive approaches. Proactive approaches use knowledge about possible future changes in order to avoid or minimize their effects. These approaches are applied before the changes occur. Thus, our approaches search for robust solutions, which have a high probability to remain valid after changes. Furthermore, some of our approaches also consider that the solutions can be easily adapted when they did not resist the changes in the original problem. Thus, these approaches search for stable solutions, which have an alternative solution that is similar to the previous one and therefore can be used in case of a value breakage.
In this context, sometimes there exists knowledge about the uncertain and dynamic environment. However in many cases, this information is unknown or hard to obtain. For this reason, for the majority of our approaches (specifically 3 of the 4 developed approaches), the only assumptions made about changes are those inherent in the structure of problems with ordered domains. Given this framework and therefore the existence of a significant order over domain values, it is reasonable to assume that the original bounds of the solution space may undergo restrictive or relaxed modifications. Note that the possibility of solution loss only exists when changes over the original bounds of the solution space are restrictive. Therefore, the main objective for searching robust solutions in this framework is to find solutions located as far away as possible from the bounds of the solution space. In order to meet this criterion, we propose several approaches that can be divided in enumeration-based techniques and a search algorithm.Climent Aunés, LI. (2013). Robustness and stability in dynamic constraint satisfaction problems [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/34785TESI