cc-Golog: Towards More Realistic Logic-Based Robot Controllers

High-level robot controllers in realistic domains typically deal with processes which operate concurrently, change the world continuously, and where the execution of actions is event-driven as in ``charge the batteries as soon as the voltage level is low''. While non-logic-based robot control languages are well suited to express such scenarios, they fare poorly when it comes to projecting, in a conspicuous way, how the world evolves when actions are executed. On the other hand, a logic-based control language like \congolog, based on the situation calculus, is well-suited for the latter. However, it has problems expressing event-driven behavior. In this paper, we show how these problems can be overcome by first extending the situation calculus to support continuous change and event-driven behavior and then presenting \ccgolog, a variant of \congolog which is based on the extended situation calculus. One benefit of \ccgolog is that it narrows the gap in expressiveness compared to non-logic-based control languages while preserving a semantically well-founded projection mechanism

Challenging the Computational Metaphor: Implications for How We Think

This paper explores the role of the traditional computational metaphor in our thinking as computer scientists, its influence on epistemological styles, and its implications for our understanding of cognition. It proposes to replace the conventional metaphor--a sequence of steps--with the notion of a community of interacting entities, and examines the ramifications of such a shift on these various ways in which we think

Logic-Based Specification Languages for Intelligent Software Agents

The research field of Agent-Oriented Software Engineering (AOSE) aims to find abstractions, languages, methodologies and toolkits for modeling, verifying, validating and prototyping complex applications conceptualized as Multiagent Systems (MASs). A very lively research sub-field studies how formal methods can be used for AOSE. This paper presents a detailed survey of six logic-based executable agent specification languages that have been chosen for their potential to be integrated in our ARPEGGIO project, an open framework for specifying and prototyping a MAS. The six languages are ConGoLog, Agent-0, the IMPACT agent programming language, DyLog, Concurrent METATEM and Ehhf. For each executable language, the logic foundations are described and an example of use is shown. A comparison of the six languages and a survey of similar approaches complete the paper, together with considerations of the advantages of using logic-based languages in MAS modeling and prototyping.Comment: 67 pages, 1 table, 1 figure. Accepted for publication by the Journal "Theory and Practice of Logic Programming", volume 4, Maurice Bruynooghe Editor-in-Chie

Team-level programming of drone sensor networks

Autonomous drones are a powerful new breed of mobile sensing platform that can greatly extend the capabilities of traditional sensing systems. Unfortunately, it is still non-trivial to coordinate multiple drones to perform a task collaboratively. We present a novel programming model called team-level programming that can express collaborative sensing tasks without exposing the complexity of managing multiple drones, such as concurrent programming, parallel execution, scaling, and failure recovering. We create the Voltron programming system to explore the concept of team-level programming in active sensing applications. Voltron offers programming constructs to create the illusion of a simple sequential execution model while still maximizing opportunities to dynamically re-task the drones as needed. We implement Voltron by targeting a popular aerial drone platform, and evaluate the resulting system using a combination of real deployments, user studies, and emulation. Our results indicate that Voltron enables simpler code and produces marginal overhead in terms of CPU, memory, and network utilization. In addition, it greatly facilitates implementing correct and complete collaborative drone applications, compared to existing drone programming systems

Automated assembly of large space structures using an expert system executive

NASA LaRC has developed a unique testbed for investigating the practical problems associated with the assembly of large space structures using robotic manipulators. The testbed is an interdisciplinary effort which considers the full spectrum of assembly problems from the design of mechanisms to the development of software. This paper will describe the automated structures assembly testbed and its operation, detail the expert system executive and its development, and discuss the planned system evolution. Emphasis will be placed on the expert system development of the program executive. The executive program must be capable of directing and reliably performing complex assembly tasks with the flexibility to recover from realistic system errors. By employing an expert system, information pertaining to the operation of the system was encapsulated concisely within a knowledge base. This lead to a substantial reduction in code, increased flexibility, eased software upgrades, and realized a savings in software maintenance costs

Inferring Robot Task Plans from Human Team Meetings: A Generative Modeling Approach with Logic-Based Prior

We aim to reduce the burden of programming and deploying autonomous systems to work in concert with people in time-critical domains, such as military field operations and disaster response. Deployment plans for these operations are frequently negotiated on-the-fly by teams of human planners. A human operator then translates the agreed upon plan into machine instructions for the robots. We present an algorithm that reduces this translation burden by inferring the final plan from a processed form of the human team's planning conversation. Our approach combines probabilistic generative modeling with logical plan validation used to compute a highly structured prior over possible plans. This hybrid approach enables us to overcome the challenge of performing inference over the large solution space with only a small amount of noisy data from the team planning session. We validate the algorithm through human subject experimentation and show we are able to infer a human team's final plan with 83% accuracy on average. We also describe a robot demonstration in which two people plan and execute a first-response collaborative task with a PR2 robot. To the best of our knowledge, this is the first work that integrates a logical planning technique within a generative model to perform plan inference.Comment: Appears in Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence (AAAI-13

Formally Integrating Real-Time Specification: A Research Proposal

To date, research in reasoning about timing properties of real-time programs has considered specification and implementation as separate issues. Specification uses formal methods; it abstracts out program execution, defining a specification that is independent of any machine-specific details (see [I, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14] for examples). In this manner, it describes only the high-level timing requirements of processes in the system, and dependencies between them. One then typically attempts to prove the mutual consistency of these timing constraints, or to determine whether the constraints maintain a safety property critical to system correctness. However, since the model has abstracted out machine-specific details, these correctness proofs either assume very optimistic operating environment (such as a one to one assignment of processes to processors), or make very pessimistic assumptions (such as that all interleavings of process executions are possible). Since neither of these assumptions will hold in practice, these predictions about the behavior of the system may not be accurate. The implementation level captures this operating environment: a real- time system is characterized by such things as process schedulers, devices and local clocks. However, advances here have been primarily in scheduling theory (examples of which are [15, 16]) and language design (examples of which are [15, 16, 17, 18,19,20]). Unfortunately, since formal models have not been used at this level, proofs of time-related properties cannot be made. To construct these proofs, we must show that an implementation is correct with respect to a specification; timing properties that can be shown to hold about the specification will therefore be known to hold for the implementation. We therefore need to represent the implementation formally so as to prove that the implementation satisfies the specification. The proof of satisfaction requires a well-defined formal mapping between the implementation and specification models. We therefore propose to develop an integrated bi-level approach to the problem of reasoning about timing properties of real-time programs. At the specification level, we will use the Timed Acceptances model, a logically sound and complete axiom system which we have recently developed [21]. Using this model, the effect of interaction among time dependent processes can be precisely specified and then analyzed. We will then develop a formal implementation model (similar to the specification model) which captures operational behaviors: for example, the assignment of processes to processors, assumptions about scheduling and clock synchronization, and the different treatment of execution and wait times. A mapping will then be formulated between these two layers. The bulk of our proposed work will be to formulate the implementation layer and define a mapping between it and the specification layer. We also need to continue work on the Timed Acceptances model to facilitate its use as a specification model, and to provide hooks for mappings between the two layers. The rest of this proposal is organized as follows. The next section overviews related work in formal specification models. Section 3 describes our current specification model and proposed enhancements. We also detail the proposed implementation model, and required properties of the mappings between the two models. Section 4 provides a summary of the proposed research, and a yearly plan