A wide variety of Artificial Intelligence (AI) techniques, from expert systems to machine learning to robotics, are needed in the field of synthetic biology. This paper describes the design-build-test engineering cycle and lists some challenges in which AI can help.

The complexity and tight integration of electromechanical systems often makes them "brittle" and hard to modify in response to changing requirements. We aim to remedy this by capturing expert knowledge as functional blueprints, an idea inspired by regulatory processes that occur in natural morphogenesis. We then apply this knowledge in an intelligent design variation tool. When a user modifies a design, our tool uses functional blueprints to modify other components in response, thereby maintaining integration and reducing the need for costly search or constraint solving. In this paper, we refine the functional blueprint concept and discuss practical issues in applying it to electromechanical systems. We then validate our approach with a case study applying our prototype tool to create variants of a miniDroid robot and by empirical evaluation of convergence dynamics of networks of functional blueprints.

We present temporal milestones for hierarchical task networks to enable the complex synchronization of tasks. A temporal milestone of a task is an intermediate event that occurs during the execution of a complex task, e.g., the start time, the end time or a milestone of any of its subtasks. Unlike landmark variables, introduced in existing work, temporal milestones respect the task abstraction boundaries and preserve structural properties enabling much more efficient reasoning. Furthermore, temporal milestones are as expressive as landmark variables. We provide analytical and empirical evidence to support these claims.

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 RECYCLE's 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.

The POIROT project is a four-year effort to develop an architecture that integrates the products of a number of targeted reasoning and learning components to produce executable representations of demonstrated web service workflow processes. To do this it combines contributions from multiple trace analysis (interpretation) and learning methods guided by a meta-control regime that reviews explicit learning hypotheses and posts new learning goals and internal learning subtasks. POIROT's meta-controller guides the activity of its components through largely distinct phases of processing from trace interpretation, to inductive learning, hypotheses combination and experimental evaluation. In this paper we discuss the impact that various kinds of inference during the trace interpretation phase can have on the quality of the learned models.

Previous algorithms for learning lexicographic preference models (LPMs) produce a "best guess" LPM that is consistent with the observations. Our approach is more democratic: we do not commit to a single LPM. Instead, we approximate the target using the votes of a collection of consistent LPMs. We present two variations of this method---variable voting and model voting---and empirically show that these democratic algorithms outperform the existing methods. 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 the learning curve significantly, especially when the number of observations is small.

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.

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.