3,018 research outputs found
Probabilistic Hybrid Action Models for Predicting Concurrent Percept-driven Robot Behavior
This article develops Probabilistic Hybrid Action Models (PHAMs), a realistic
causal model for predicting the behavior generated by modern percept-driven
robot plans. PHAMs represent aspects of robot behavior that cannot be
represented by most action models used in AI planning: the temporal structure
of continuous control processes, their non-deterministic effects, several modes
of their interferences, and the achievement of triggering conditions in
closed-loop robot plans.
The main contributions of this article are: (1) PHAMs, a model of concurrent
percept-driven behavior, its formalization, and proofs that the model generates
probably, qualitatively accurate predictions; and (2) a resource-efficient
inference method for PHAMs based on sampling projections from probabilistic
action models and state descriptions. We show how PHAMs can be applied to
planning the course of action of an autonomous robot office courier based on
analytical and experimental results
Assessing fuel burn inefficiencies in oceanic airspace
Increasing the efficiency of aircraft operations offers a shorter term solution to decreasing aircraft fuel burn than fleet replacement. By estimating the current airspace inefficiency, we can get an idea of the upper limit of savings. Oceanic airspace presents a unique opportunity for savings due to increased separation differences vs. overland flight.
We assess fuel burn inefficiency by comparing estimated fuel burn for real world flights with the estimated optimal fuel burn. For computing fuel burn, we use the Base of Aircraft Data (BADA) with corrections based on research by Yoder (2005). Our fuel burn results show general agreement with Yoder’s results.
Optimal operation depends on flying 4-D trajectories that use the least amount of fuel. We decompose optimal 4-D trajectories into vertical and horizontal components and analyze the inefficiencies of each separately.
We use the concept of Specific Ground Range [Jensen, 2011], to find optimal altitudes and speeds. We combine the optimal altitudes and speeds with an aircraft proximity algorithm to find pairs of aircraft in a vertical blocking situations. To find the fuel optimal horizontal track in a wind field, we use methods from the field of Optimal Control. The original problem formulation can be transformed into a Two Point Boundary Value problem which we solve using MATLAB’s bvp4c function.
From our set of flights, we hypothesized a scenario where aircraft stack in such a way that they cannot climb to their optimal altitudes because of separations standards. Using aircraft positions we find when aircraft were within separation standards and were blocked from climbing or descending to their optimal altitude. We split our inefficiency results into a blocked and non-blocked set to see if blocking had an effect on mean inefficiency.
Our set of flights consisted of real world flights that flew through WATRS and CEP airspace regions during the month of April 2016. Using the optimal altitude for actual flight Mach profiles, we compute a mean inefficiency of 4.75% in WATRS and 4.50% in CEP, both of which are roughly 2 to 2.5 percentage points higher than studies using proprietary performance models and data. BADA overestimates optimal altitudes, leading to an overestimate in inefficiency. Inefficiency due to off-optimal speed for WATRS is 2.18% vs. 1.86% in CEP.
Blocking events result in a 2.59 percentage point increase in mean inefficiency due to off-optimal altitude in WATRS flights, and a 1.21 percentage point increase in mean inefficiency due to off-optimal altitude in CEP flights.
Using wind-optimal horizontal tracks gave a 1.24% mean inefficiency in WATRS, and a 0.41% mean inefficiency in CEP.
The results indicate that, in total, flights through WATRS and CEP have approximately the same inefficiency due to off-optimal altitudes, but that blocking effects are more prevalent in WATRS. In addition, flights through WATRS are farther from their wind-optimal horizontal tracks than flights in CEP
Rational Agents: Prioritized Goals, Goal Dynamics, and Agent Programming Languages with Declarative Goals
I introduce a specification language for modeling an agent's prioritized goals and their dynamics. I use the situation calculus along with Reiter's solution to the frame problem and predicates for describing agents' knowledge as my base formalism. I further enhance this language by introducing a new sort of infinite paths. Within this language, I discuss how to systematically specify prioritized goals and how to precisely describe the effects of actions on these goals. These actions include adoption and dropping of goals and subgoals. In this framework, an agent's intentions are formally specified as the prioritized intersection of her goals. The ``prioritized'' qualifier above means that the specification must respect the priority ordering of goals when choosing between two incompatible goals. I ensure that the agent's intentions are always consistent with each other and with her knowledge. I investigate two variants with different commitment strategies. Agents specified using the ``optimizing'' agent framework always try to optimize their intentions, while those specified in the ``committed'' agent framework will stick to their intentions even if opportunities to commit to higher priority goals arise when these goals are incompatible with their current intentions. For these, I study properties of prioritized goals and goal change. I also give a definition of subgoals, and prove properties about the goal-subgoal relationship.
As an application, I develop a model for a Simple Rational Agent Programming Language (SR-APL) with declarative goals. SR-APL is based on the ``committed agent'' variant of this rich theory, and combines elements from Belief-Desire-Intention (BDI) APLs and the situation calculus based ConGolog APL. Thus SR-APL supports prioritized goals and is grounded on a formal theory of goal change. It ensures that the agent's declarative goals and adopted plans are consistent with each other and with her knowledge. In doing this, I try to bridge the gap between agent theories and practical agent programming languages by providing a model and specification of an idealized BDI agent whose behavior is closer to what a rational agent does. I show that agents programmed in SR-APL satisfy some key rationality requirements
Distributed Planning for Self-Organizing Production Systems
Für automatisierte Produktionsanlagen gibt es einen fundamentalen Tradeoff
zwischen Effizienz und Flexibilität. In den meisten Fällen sind die Abläufe
nicht nur durch den physischen Aufbau der Produktionsanlage, sondern auch durch
die spezielle zugeschnittene Programmierung der Anlagensteuerung fest
vorgegeben. Änderungen müssen aufwändig in einer Vielzahl von Systemen
nachgezogen werden. Das macht die Herstellung kleiner Stückzahlen unrentabel.
In dieser Dissertation wird ein Ansatz entwickelt, um eine automatische
Anpassung des Verhaltens von Produktionsanlagen an wechselnde Aufträge und
Rahmenbedingungen zu erreichen. Dabei kommt das Prinzip der Selbstorganisation
durch verteilte Planung zum Einsatz. Die aufeinander aufbauenden Ergebnisse der
Dissertation sind wie folgt:
1. Es wird ein Modell von Produktionsanlagen entwickelt, dass nahtlos von der
detaillierten Betrachtung physikalischer Produktionsprozesse bis hin zu
Lieferbeziehungen zwischen Unternehmen skaliert. Im Vergleich zu
existierenden Modellen von Produktionsanlagen werden weniger limitierende
Annahmen gestellt. In diesem Sinne ist der Modellierungsansatz ein Kandidat
für eine häufig geforderte "Theorie der Produktion".
2. Für die so modellierten Szenarien wird ein Algorithmus zur Optimierung der
nebenläufigen Abläufe entwickelt. Der Algorithmus verbindet Techniken für die
kombinatorische und die kontinuierliche Optimierung: Je nach Detailgrad und
Ausgestaltung des modellierten Szenarios kann der identische Algorithmus
kombinatorische Fertigungsfeinplanung (Scheduling) vornehmen, weltweite
Lieferbeziehungen unter Einbezug von Unsicherheiten und Risiko optimieren und
physikalische Prozesse prädiktiv regeln. Dafür werden Techniken der
Monte-Carlo Baumsuche (die auch bei Deepminds Alpha Go zum Einsatz kommen)
weiterentwickelt. Durch Ausnutzung zusätzlicher Struktur in den Modellen
skaliert der Ansatz auch auf große Szenarien.
3. Der Planungsalgorithmus wird auf die verteilte Optimierung durch unabhängige
Agenten übertragen. Dafür wird die sogenannte "Nutzen-Propagation" als
Koordinations-Mechanismus entwickelt. Diese ist von der Belief-Propagation
zur Inferenz in Probabilistischen Graphischen Modellen inspiriert. Jeder
teilnehmende Agent hat einen lokalen Handlungsraum, in dem er den
Systemzustand beobachten und handelnd eingreifen kann. Die Agenten sind an
der Maximierung der Gesamtwohlfahrt über alle Agenten hinweg interessiert.
Die dafür notwendige Kooperation entsteht über den Austausch von Nachrichten
zwischen benachbarten Agenten. Die Nachrichten beschreiben den erwarteten
Nutzen für ein angenommenes Verhalten im Handlungsraum beider Agenten.
4. Es wird eine Beschreibung der wiederverwendbaren Fähigkeiten von Maschinen
und Anlagen auf Basis formaler Beschreibungslogiken entwickelt. Ausgehend von
den beschriebenen Fähigkeiten, sowie der vorliegenden Aufträge mit ihren
notwendigen Produktionsschritten, werden ausführbare Aktionen abgeleitet. Die
ausführbaren Aktionen, mit wohldefinierten Vorbedingungen und Effekten,
kapseln benötigte Parametrierungen, programmierte Abläufe und die
Synchronisation von Maschinen zur Laufzeit.
Die Ergebnisse zusammenfassend werden Grundlagen für flexible automatisierte
Produktionssysteme geschaffen -- in einer Werkshalle, aber auch über Standorte
und Organisationen verteilt -- welche die ihnen innewohnenden Freiheitsgrade
durch Planung zur Laufzeit und agentenbasierte Koordination gezielt einsetzen
können. Der Bezug zur Praxis wird durch Anwendungsbeispiele hergestellt. Die
Machbarkeit des Ansatzes wurde mit realen Maschinen im Rahmen des EU-Projekts
SkillPro und in einer Simulationsumgebung mit weiteren Szenarien demonstriert
Co-selection in R&D project portfolio management
In the study I analyze the conflicting aspects of project portfolio evolution in a firm. The evolutionary principles of variation, selection and retention are applied to the management of new product development projects. Managers select projects for prioritization. A selection rule is the prioritization rule. In biology, living creatures develop specific features for adaptation as a result of selection rules. However, the selection of specific adaptive features carries along the retention of other, even unforeseen non-adaptive features. Drawing on the evolutionary principles forwarded by Darwin I examine how they manifest in the project portfolio. I define this non-adaptive mechanism as co-selection. By analogy, in portfolio management, if the selection rule for project priority is high revenue and feasibility to global access, other features also survive when the selection rule relating to the prioritization of projects is applied. The evolution of the new product development project portfolio in the case firm displays conflicting trends in the emerging project portfolio over time. Managers pursue prioritization to decrease product development times. But, alas, in the project portfolio the prioritized projects age to a greater degree than non-prioritized projects. Managers prioritize the projects held by the focal business unit more often than those of other business units. However, ultimately the focal business unit has less than a due share of prioritized projects in the portfolio. The results of this study question the applicability of optimizing models in R&D portfolio management in the presence of co-selection. The project portfolio management literature does not provide a mechanism to account for this type of portfolio development. Co-selection provides a mechanism that explains the observed evolution. The study contributes to the conceptualization of the notion of co-selection. The study also provides empirical evidence on co-selection, a non-adaptive evolutionary mechanism to modify R&D project portfolio outcome. The findings give a better understanding of portfolio management of R&D driven new product development projects
Towards Safe Artificial General Intelligence
The field of artificial intelligence has recently experienced a
number of breakthroughs thanks to progress in deep learning and
reinforcement learning. Computer algorithms now outperform humans
at Go, Jeopardy, image classification, and lip reading, and are
becoming very competent at driving cars and interpreting natural
language. The rapid development has led many to conjecture that
artificial intelligence with greater-than-human ability on a wide
range of tasks may not be far. This in turn raises concerns
whether we know how to control such systems, in case we were to
successfully build them.
Indeed, if humanity would find itself in conflict with a system
of much greater intelligence than itself, then human society
would likely lose. One way to make sure we avoid such a conflict
is to ensure that any future AI system with potentially
greater-than-human-intelligence has goals that are aligned with
the goals of the rest of humanity. For example, it should not
wish to kill humans or steal their resources.
The main focus of this thesis will therefore be goal alignment,
i.e. how to design artificially intelligent agents with goals
coinciding with the goals of their designers. Focus will mainly
be directed towards variants of reinforcement learning, as
reinforcement learning currently seems to be the most promising
path towards powerful artificial intelligence. We identify and
categorize goal misalignment problems in reinforcement learning
agents as designed today, and give examples of how these agents
may cause catastrophes in the future. We also suggest a number of
reasonably modest modifications that can be used to avoid or
mitigate each identified misalignment problem. Finally, we also
study various choices of decision algorithms, and conditions for
when a powerful reinforcement learning system will permit us to
shut it down.
The central conclusion is that while reinforcement learning
systems as designed today are inherently unsafe to scale to human
levels of intelligence, there are ways to potentially address
many of these issues without straying too far from the currently
so successful reinforcement learning paradigm. Much work remains
in turning the high-level proposals suggested in this thesis into
practical algorithms, however
Improving Model-Based Software Synthesis: A Focus on Mathematical Structures
Computer hardware keeps increasing in complexity. Software design needs to keep up with this. The right models and abstractions empower developers to leverage the novelties of modern hardware. This thesis deals primarily with Models of Computation, as a basis for software design, in a family of methods called software synthesis.
We focus on Kahn Process Networks and dataflow applications as abstractions, both for programming and for deriving an efficient execution on heterogeneous multicores. The latter we accomplish by exploring the design space of possible mappings of computation and data to hardware resources. Mapping algorithms are not at the center of this thesis, however. Instead, we examine the mathematical structure of the mapping
space, leveraging its inherent symmetries or geometric properties to improve mapping methods in general.
This thesis thoroughly explores the process of model-based design, aiming to go beyond the more established software synthesis on dataflow applications. We starting with the problem of assessing these methods through benchmarking, and go on to formally examine the general goals of benchmarks. In this context, we also consider the role modern machine learning methods play in benchmarking.
We explore different established semantics, stretching the limits of Kahn Process Networks. We also discuss novel models, like Reactors, which are designed to be a deterministic, adaptive model with time as a first-class citizen. By investigating abstractions and transformations in the Ohua language for implicit dataflow programming, we also focus on programmability.
The focus of the thesis is in the models and methods, but we evaluate them in diverse use-cases, generally centered around Cyber-Physical Systems. These include the 5G telecommunication standard, automotive and signal processing domains. We even go beyond embedded systems and discuss use-cases in GPU programming and microservice-based architectures
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