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  1.  91
    Verifying Time, Memory and Communication Bounds in Systems of Reasoning Agents.Natasha Alechina, Brian Logan, Hoang Nga Nguyen & Abdur Rakib - 2009 - Synthese 169 (2):385-403.
    We present a framework for verifying systems composed of heterogeneous reasoning agents, in which each agent may have differing knowledge and inferential capabilities, and where the resources each agent is prepared to commit to a goal (time, memory and communication bandwidth) are bounded. The framework allows us to investigate, for example, whether a goal can be achieved if a particular agent, perhaps possessing key information or inferential capabilities, is unable (or unwilling) to contribute more than a given portion of its (...)
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  2.  53
    Belief Ascription Under Bounded Resources.Natasha Alechina & Brian Logan - 2010 - Synthese 173 (2):179 - 197.
    There exists a considerable body of work on epistemic logics for resource-bounded reasoners. In this paper, we concentrate on a less studied aspect of resource-bounded reasoning, namely, on the ascription of beliefs and inference rules by the agents to each other. We present a formal model of a system of bounded reasoners which reason about each other’s beliefs, and investigate the problem of belief ascription in a resource-bounded setting. We show that for agents whose computational resources and memory are bounded, (...)
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  3.  91
    Preference-Based Belief Revision for Rule-Based Agents.Natasha Alechina, Mark Jago & Brian Logan - 2008 - Synthese 165 (2):159-177.
    Agents which perform inferences on the basis of unreliable information need an ability to revise their beliefs if they discover an inconsistency. Such a belief revision algorithm ideally should be rational, should respect any preference ordering over the agent’s beliefs (removing less preferred beliefs where possible) and should be fast. However, while standard approaches to rational belief revision for classical reasoners allow preferences to be taken into account, they typically have quite high complexity. In this paper, we consider belief revision (...)
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  4. Modelling Communicating Agents in Timed Reasoning Logics.Brian Logan, Mark Jago & Natasha Alechina - 2006 - In U. Endriss & M. Baldoni (eds.), Declarative Agent Languages and Technologies 4. Springer.
    Practical reasoners are resource-bounded—in particular they require time to derive consequences of their knowledge. Building on the Timed Reasoning Logics (TRL) framework introduced in [1], we show how to represent the time required by an agent to reach a given conclusion. TRL allows us to model the kinds of rule application and conflict resolution strategies commonly found in rule-based agents, and we show how the choice of strategy can influence the information an agent can take into account when making decisions (...)
     
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  5.  28
    A Logic of Situated Resource-Bounded Agents.Natasha Alechina & Brian Logan - 2009 - Journal of Logic, Language and Information 18 (1):79-95.
    We propose a framework for modelling situated resource-bounded agents. The framework is based on an objective ascription of intentional modalities and can be easily tailored to the system we want to model and the properties we wish to specify. As an elaboration of the framework, we introduce a logic, OBA, for describing the observations, beliefs, goals and actions of simple agents, and show that OBA is complete, decidable and has an efficient model checking procedure, allowing properties of agents specified in (...)
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  6.  40
    Verifying Space and Time Requirements for Resource-Bounded Agents.Natasha Alechina, Piergiorgio Bertoli, Chiara Ghidini, Mark Jago, Brian Logan & Luciano Serafini - 2007 - In A. Lomuscio & S. Edelkamp (eds.), Model Checking and Artificial Intelligence. Springer.
    The effective reasoning capability of an agent can be defined as its capability to infer, within a given space and time bound, facts that are logical consequences of its knowledge base. In this paper we show how to determine the effective reasoning capability of an agent with limited memory by encoding the agent as a transition system and automatically verifying whether a state where the agent believes a certain conclusion is reachable from the start state. We present experimental results using (...)
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  7.  14
    Dynamic Interest Management in the Distributed Simulation of Agent-Based Systems.Brian Logan & Georgios Theodoropoulos - forthcoming - Proceedings of the Tenth Conference on Ai, Simulation and Planning, Ais-2000’, Society for Computer Simulation International.
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