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On the Logic of Delegation - Relating Theory and Practice.
In Fabio Paglieri, Luca Tummolini, Rino Falcone, Maria Miceli, editors, The Goals of Cognition: Essays in honour of Cristiano Castelfranchi, pages 467–496. College Publications. ISBN: 978-1848900943.
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Research with collaborative robotic systems has much to gain by leveraging concepts and ideas from the areas of multi-agent systems and the social sciences. In this paper we propose an approach to formalizing and grounding important aspects of collaboration in a collaborative system shell for robotic systems. This is done primarily in terms of the concept of delegation, where delegation will be instantiated as a speech act. The formal characterization of the delegation speech act is based on a preformal theory of delegation proposed by Falcone and Castelfranchi. We show how the delegation speech act can in fact be used to formally ground an abstract characterization of delegation into a FIPA-compliant implementation in an agent-oriented language such as JADE, as part of a collaborative system shell for robotic systems. The collaborative system shell has been developed as a prototype and used in collaborative missions with multiple unmanned aerial vehicle systems.
A Distributed Task Specification Language for Mixed-Initiative Delegation.
In Nirmit Desai, Alan Liu, Michael Winikoff, editors, Principles and Practice of Multi-Agent Systems: 13th International Conference, PRIMA 2010, Kolkata, India, November 12-15, 2010, Revised Selected Papers, pages 42–57. In series: Lecture Notes in Computer Science #7057. Springer Berlin/Heidelberg. ISBN: 978-3-642-25919-7, e-978-3-642-25920-3.
In the next decades, practically viable robotic/agent systems are going to be mixed-initiative in nature. Humans will request help from such systems and such systems will request help from humans in achieving the complex mission tasks required. Pragmatically, one requires a distributed task specification language to define tasks and a suitable data structure which satisfies the specification and can be used flexibly by collaborative multi-agent/robotic systems. This paper defines such a task specification language and an abstract data structure called Task Specification Trees which has many of the requisite properties required for mixed-initiative problem solving and adjustable autonomy in a distributed context. A prototype system has been implemented for this delegation framework and has been used practically with collaborative unmanned aircraft systems.
Complex Task Allocation in Mixed-Initiative Delegation: A UAV Case Study.
In Nirmit Desai, Alan Liu, Michael Winikoff, editors, Principles and Practice of Multi-Agent Systems: 13th International Conference, PRIMA 2010, Kolkata, India, November 12-15, 2010, Revised Selected Papers, pages 288–303. In series: Lecture Notes in Computer Science #7057. Springer Berlin/Heidelberg. ISBN: 978-3-642-25919-7, e-978-3-642-25920-3.
Unmanned aircraft systems (UASs) are now becoming technologically mature enough to be integrated into civil society. An essential issue is principled mixed-initiative interaction between UASs and human operators. Two central problems are to specify the structure and requirements of complex tasks and to assign platforms to these tasks. We have previously proposed Task Specification Trees (TSTs) as a highly expressive specification language for complex multi-agent tasks that supports mixed-initiative delegation and adjustable autonomy. The main contribution of this paper is a sound and complete distributed heuristic search algorithm for allocating the individual tasks in a TST to platforms. The allocation also instantiates the parameters of the tasks such that all the constraints of the TST are satisfied. Constraints are used to model dependencies between tasks, resource usage as well as temporal and spatial requirements on complex tasks. Finally, we discuss a concrete case study with a team of unmanned aerial vehicles assisting in a challenging emergency situation.
A Delegation-Based Architecture for Collaborative Robotics.
In Danny Weyns and Marie-Pierre Gleizes, editors, Agent-Oriented Software Engineering XI: 11th International Workshop, AOSE 2010, Toronto, Canada, May 10-11, 2010, Revised Selected Papers, pages 205–247. In series: Lecture Notes in Computer Science #6788. Springer Berlin/Heidelberg. ISBN: 978-3-642-22635-9.
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Collaborative robotic systems have much to gain by leveraging results from the area of multi-agent systems and in particular agent-oriented software engineering. Agent-oriented software engineering has much to gain by using collaborative robotic systems as a testbed. In this article, we propose and specify a formally grounded generic collaborative system shell for robotic systems and human operated ground control systems. Collaboration is formalized in terms of the concept of delegation and delegation is instantiated as a speech act. Task Specification Trees are introduced as both a formal and pragmatic characterization of tasks and tasks are recursively delegated through a delegation process implemented in the collaborative system shell. The delegation speech act is formally grounded in the implementation using Task Specification Trees, task allocation via auctions and distributed constraint problem solving. The system is implemented as a prototype on Unmanned Aerial Vehicle systems and a case study targeting emergency service applications is presented.
Positioning Unmanned Aerial Vehicles As Communication Relays for Surveillance Tasks.
In J. Trinkle, Y. Matsuoka and J.A. Castellanos, editors, Robotics: Science and Systems V, pages 257–264. MIT Press. ISBN: 978-0-262-51463-7.
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When unmanned aerial vehicles (UAVs) are used to survey distant targets, it is important to transmit sensor information back to a base station. As this communication often requires high uninterrupted bandwidth, the surveying UAV often needs afree line-of-sight to the base station, which can be problematic in urban or mountainous areas. Communication ranges may also belimited, especially for smaller UAVs. Though both problems can be solved through the use of relay chains consisting of one or more intermediate relay UAVs, this leads to a new problem: Where should relays be placed for optimum performance? We present two new algorithms capable of generating such relay chains, one being a dual ascent algorithm and the other a modification of the Bellman-Ford algorithm. As the priorities between the numberof hops in the relay chain and the cost of the chain may vary, wecalculate chains of different lengths and costs and let the ground operator choose between them. Several different formulations for edge costs are presented. In our test cases, both algorithms are substantially faster than an optimized version of the original Bellman-Ford algorithm, which is used for comparison.
Temporal Action Logics.
In V. Lifschitz, F. van Harmelen, and F. Porter, editors, Handbook of Knowledge Representation, pages 709–757. In series: Foundations of Artificial Intelligence #3. Elsevier. ISBN: 978-0-444-52211-5.
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The study of frameworks and formalisms for reasoning about action and change [67, 58, 61, 65, 70, 3, 57] has been central to the knowledge representation field almost from the inception of Artificial Intelligence as a general field of research [52, 56]. The phrase â€śTemporal Action Logicsâ€ť represents a class of logics for reasoning about action and change that evolved from Sandewallâ€™s book on Features and Fluents  and owes much to this ambitious project. There are essentially three major parts to Sandewallâ€™s work. He first developed a narrative-based logical framework for specifying agent behavior in terms of action scenarios. The logical framework is state-based and uses explicit time structures. He then developed a formal framework for assessing the correctness (soundness and completeness) of logics for reasoning about action and change relative to a set of well-defined intended conclusions, where reasoning problems were classified according to their ontological or epistemological characteristics. Finally, he proposed a number of logics defined semantically in terms of definitions of preferential entailment1 and assessed their correctness using his assessment framework.
Using Contextually Closed Queries for Local Closed-World Reasoning in Rough Knowledge Databases.
In Rough-Neural Computing: Techniques for Computing with Words, pages 219–250. In series: Cognitive Technologies #??. Springer. ISBN: 978-3-540-43059-9.
Approximation Transducers and Trees: A Technique for Combining Rough and Crisp Knowledge.
In Rough-Neural Computing: Techniques for Computing with Words, pages 189–218. In series: Cognitive Technologies #??. Springer.
This chapter proposes a framework for specifying, constructing, and managing aparticular class of approximate knowledge structures for use with intelligent artifacts rangingfrom simpler devices such as personal digital assistants (PDAs) to more complex ones suchas unmanned aerial vehicles (UAVs). This chapter introduces the notion of an approximationtransducer, which takes approximate relations as input and generates a (possibly moreabstract) approximate relation as output by combining the approximate input relations witha crisp local logical theory representing dependencies between input and output relations.Approximation transducers can be combined to produce approximation trees, which representcomplex approximate knowledge structures characterized by the properties of elaborationtolerance, groundedness in the application domain, modularity, and context dependency.Approximation trees are grounded through the use of primitive concepts generated with supervisedlearning techniques. Changes in definitions of primitive concepts or in the locallogical theories used by transducers result in changes in the knowledge stored in approximationtrees by increasing or decreasing precision in the knowledge qualitatively. Intuitionsand techniques from rough set theory are used to define approximate relations where eachhas an upper and a lower approximation. The constituent components in a rough set havecorrespondences in a logical language used to relate crisp and approximate knowledge. Theinference mechanism associated with the use of approximation trees is based on a generalizationof deductive databases that we call rough relational databases. Approximation trees andqueries to them are characterized in terms of rough relational databases and queries to them.By placing certain syntactic restrictions on the local theories used in transducers, the computationalprocesses used in the query/answering and generation mechanism for approximationtrees remain in PTIME.
Preferential Action Semantics.
In Handbook of Defeasible Reasoning and Uncertainty Management Systems, volume 7: Agent-Based Defeasible Control in Dynamic Environments. In series: Handbook of Defeasible Reasoning and Uncertainty Management Systems #7. Kluwer. ISBN: 978-1-4020-0834-4.
A study in modal embeddings of NML3.
In Partiality, Modality, and Nonmonotonicity, Studies in Logic, Language and Information., pages 145–168. CSLI Publications.
Circumscribing features and fluents.
In Dov M. Gabbay and Hans JĂĽrgen Ohlbach, editors, Temporal Logic: First International Conference, ICTL'94 Bonn, Germany, July 11â€“14, 1994 Proceedings, pages 82–100. In series: Lecture Notes in Computer Science #827. Springer Berlin/Heidelberg. ISBN: 3-540-58241-X, 0-387-58241-X.
Sandewall has recently proposed a systematic approach to the representation of knowledge about dynamical systems that includes a general framework in which to assess the range of applicability of existing and new logics for action and change and to provide a means of studying whether and in what sense the logics of action and change are relevant for intelligent agents. As part of the framework, a number of logics of preferential entailment are introduced and assessed for particular classes of action scenario descriptions. This paper provides syntactic characterizations of several of these relations of preferential entailment in terms of standard FOPC and circumscription axioms. The intent is to simplify the process of comparison with existing formalisms which use more traditional techniques and to provide a basis for studying the feasibility of compiling particular classes of problems into logic programs.
Nonmonotonicity, fuzziness, and multi-values.
In Fuzzy Logic: State of the Art. Series D: System Theory, Knowledge Engineering and Problem Soving.. In series: Volume 12 #12. Kluwer Academic Publishers.
Distinguishing between facts and default assumptions.
In Non-Monotonic Reasoning and Partial Semantics. Ellis Horwood Workshops.. Ellis Horwood Ltd..
A non-monotonic fuzzy logic.
In Fuzzy Logic for the Management of Uncertainty, pages 171–190. John Wiley & Sons. ISBN: 0-471-54799-9.