Fusun Yaman

SHOP2: an HTN planning system

The SHOP2 planning system received one of the awards for distinguished performance in the 2002 International Planning Competition. This paper describes the features of SHOP2 which enabled it to excel in the competition, especially those aspects of SHOP2 that deal with temporal and metric planning domains.

Applications of SHOP and SHOP2

We design the simple hierarchical ordered planner (SHOP) and its successor, SHOP2, with two goals in mind: to investigate research issues in automated planning and to provide some simple, practical planning tools. SHOP and SHOP2 are based on a planning formalism called hierarchical task network planning. SHOP and SHOP2 use a search-control strategy called ordered task decomposition, which breaks tasks into subtasks and generates the plans actions in the same order that the plan executor executes them. So, throughout the planning process, the planner can tell what the state of the world at each step of the plan.

An End-to-End Workflow for Engineering of Biological Networks from High-Level Specifications

We present a workflow for the design and production of biological networks from high-level program specifications. The workflow is based on a sequence of intermediate models that incrementally translate high-level specifications into DNA samples that implement them. We identify algorithms for translating between adjacent models and implement them as a set of software tools, organized into a four-stage toolchain: Specification, Compilation, Part Assignment, and Assembly. The specification stage begins with a Boolean logic computation specified in the Proto programming language. The compilation stage uses a library of network motifs and cellular platforms, also specified in Proto, to transform the program into an optimized Abstract Genetic Regulatory Network (AGRN) that implements the programmed behavior. The part assignment stage assigns DNA parts to the AGRN, drawing the parts from a database for the target cellular platform, to create a DNA sequence implementing the AGRN. Finally, the assembly stage computes an optimized assembly plan to create the DNA sequence from available part samples, yielding a protocol for producing a sample of engineered plasmids with robotics assistance. Our workflow is the first to automate the production of biological networks from a high-level program specification. Furthermore, the workflows modular design allows the same program to be realized on different cellular platforms simply by swapping workflow configurations. We validated our workflow by specifying a small-molecule sensor-reporter program and verifying the resulting plasmids in both HEK 293 mammalian cells and in E. coli bacterial cells.

Security-aware adaptive dynamic source routing protocol

We present SADSR (security-aware adaptive DSR), a secure routing protocol for mobile ad hoc networks. SADSR authenticates the routing protocol messages using digital signatures based on asymmetric cryptography. The basic idea behind SADSR is to have multiple routes to each destination and store a local trust value for each node in the network. A trust value is assigned to each path based on trust values of the nodes which occur on that path. The paths with higher trust values are preferred for routing. We implemented our approach in ns2 simulator and compared the performance of SADSR and DSR. Our results show that in the presence of malicious nodes SADSR outperforms DSR in packet delivery ratio with an acceptable network load.

Automated Selection of Synthetic Biology Parts for Genetic Regulatory Networks

Raising the level of abstraction for synthetic biology design requires solving several challenging problems, including mapping abstract designs to DNA sequences. In this paper we present the first formalism and algorithms to address this problem. The key steps of this transformation are feature matching, signal matching, and part matching. Feature matching ensures that the mapping satisfies the regulatory relationships in the abstract design. Signal matching ensures that the expression levels of functional units are compatible. Finally, part matching finds a DNA part sequence that can implement the design. Our software tool MatchMaker implements these three steps.

Accurate predictions of genetic circuit behavior from part characterization and modular composition.

A long-standing goal of synthetic biology is to rapidly engineer new regulatory circuits from simpler devices. As circuit complexity grows, it becomes increasingly important to guide design with quantitative models, but previous efforts have been hindered by lack of predictive accuracy. To address this, we developed Empirical Quantitative Incremental Prediction (EQuIP), a new method for accurate prediction of genetic regulatory network behavior from detailed characterizations of their components. In EQuIP, precisely calibrated time-series and dosage-response assays are used to construct hybrid phenotypic/mechanistic models of regulatory processes. This hybrid method ensures that model parameters match observable phenomena, using phenotypic formulation where current hypotheses about biological mechanisms do not agree closely with experimental observations. We demonstrate EQuIPs precision at predicting distributions of cell behaviors for six transcriptional cascades and three feed-forward circuits in mammalian cells. Our cascade predictions have only 1.6-fold mean error over a 261-fold mean range of fluorescence variation, owing primarily to calibrated measurements and piecewise-linear models. Predictions for three feed-forward circuits had a 2.0-fold mean error on a 333-fold mean range, further demonstrating that EQuIP can scale to more complex systems. Such accurate predictions will foster reliable forward engineering of complex biological circuits from libraries of standardized devices.

Quantifying & minimizing attack surfaces containing moving target defenses

The cyber security exposure of resilient systems is frequently described as an attack surface. A larger surface area indicates increased exposure to threats and a higher risk of compromise. Ad-hoc addition of dynamic proactive defenses to distributed systems may inadvertently increase the attack surface. This can lead to cyber friendly fire, a condition in which adding superfluous or incorrectly configured cyber defenses unintentionally reduces security and harms mission effectiveness. Examples of cyber friendly fire include defenses which themselves expose vulnerabilities (e.g., through an unsecured admin tool), unknown interaction effects between existing and new defenses causing brittleness or unavailability, and new defenses which may provide security benefits, but cause a significant performance impact leading to mission failure through timeliness violations. This paper describes a prototype service capability for creating semantic models of attack surfaces and using those models to (1) automatically quantify and compare cost and security metrics across multiple surfaces, covering both system and defense aspects, and (2) automatically identify opportunities for minimizing attack surfaces, e.g., by removing interactions that are not required for successful mission execution.

Learning Lexicographic Preference Models

Lexicographic preference models (LPMs) are one of the simplest yet most commonly used preference representations. In this chapter, we formally define LPMs and present learning algorithms for mining these models from data. In particular, we study a greedy algorithm that produces a “best guess” LPM that is consistent with the observations and two voting-based algorithms that approximate the target using the votes of a collection of consistent LPMs. In addition to our theoretical analyses of these algorithms, we empirically evaluate their performance under different conditions. Our results show that voting algorithms outperform the greedy method when the data is noise-free. The dominance is more significant when the training data is scarce. However, the performance of the voting algorithms quickly decays with even a little noise, whereas the greedy algorithm is more robust. Inspired by this result, we adapt one of the voting methods to consider the amount of noise in an environment and empirically show that the modified voting algorithm performs as well as the greedy approach even with noisy observations. We also introduce an intuitive yet powerful learning bias to prune some of the possible LPMs. We demonstrate how this learning bias can be used with variable and model voting and show that the learning bias improves learning performance significantly, especially when the number of observations is small.

RECYCLE: Learning looping workflows from annotated traces

A workflow is a model of a process that systematically describes patterns of activity. Workflows capture a sequence of operations, their enablement conditions, and data flow dependencies among them. It is hard to design a complete and correct workflow from scratch, while it is much easier for humans to demonstrate the solution than to state the solution declaratively. This article presents RECYCLE, our approach to learning workflow models from example demonstration traces. RECYCLE captures control flow, data flow, and enablement conditions of an underlying workflow process. Unlike prior work from workflow mining and AI planning literature, (1) RECYCLE can learn from a single demonstration trace with loops, (2) RECYCLE learns both loop and conditional branch structure, and (3) RECYCLE handles data flow among actions. In this article, we describe the phases of RECYCLEs learning algorithm: substructure analysis and node abstraction. To ground the discussion, we present a simplified flight reservation system with some of the important characteristics of the real domains we worked with. We present some results from a patient transport domain.

Plan Databases: Model and Algebra

Despite the fact that thousands of applications manipulate plans, there has been no work to date on managing large databases of plans. In this paper, we first propose a formal model of plan databases. We describe important notions of consistency and coherence for such databases. We then propose a set of operators similar to the relational algebra to query such databases of plans.