Publications
123 results found
Ma J, Russo A, Broda K, et al., 2010, Distributed abductive reasoning with constraints., Publisher: IFAAMAS, Pages: 1381-1382
Hosobe H, Satoh K, Ma J, et al., 2010, Speculative Constraint Processing for Hierarchical Agents, AI Communications: the European journal on artificial intelligence
Broda K, Hogger CJ, 2010, Designing Effective Policies for Minimal Agents, The Computer Journal
Komendantskaya E, Broda K, Garcez A, 2010, Neuro-Symbolic Representation of Logic Programs Defining Infinite Sets, ICANN 2010
Ma J, Broda K, Goebel R, et al., 2010, Speculative Abductive Reasoning for Hierarchical Agent Systems, 11th Workshop on Computational Logic in Multi-Agent Systems
Ma J, Broda K, Russo A, et al., 2010, Distributed Abductive Reasoning with Constraints, Declarative Agent Languages and Technologies DALT-2010 Post proceedings
Guillame-Bert M, Broda K, Garcez A, 2010, First-order Logic Learning in Artificial Neural Networks, World Congress on Computational Intelligence (WCCI 2010), Pages: 1-8, ISSN: 1098-7576
Ma J, Russo A, Broda K, et al., 2010, On the Implementation of Speculative Constraint Processing, Workshop on Computational Logic in Multi-Agent Systems
Ma J, Russo A, Broda K, et al., 2010, Distributed Abductive Reasoning with Constraints, 9th Conference on Autonomous Agents and Multi-Agent Systems (AAMAS10)
Komendantskaya E, Broda K, Garcez A, 2010, Using Inductive Types for Ensuring Correctness of Neuro-Symbolic Computations, Computability in Europe(CiE)2010
Hosebe H, Satoh K, Ma J, et al., 2010, Speculative Constraint Processing for Hierarchical Agents, 7th European Workshop on Multi-Agent Systems, EUMAS-09
Dickens L, Broda K, Russo A, 2010, The Dynamics of Multi-Agent Reinforcement Learning, ECAI 2010 - 19TH EUROPEAN CONFERENCE ON ARTIFICIAL INTELLIGENCE, Pages: 367-372, ISSN: 0922-6389
Ma J, Russo A, Broda K, et al., 2009, Multi-agent planning with confidentiality., Publisher: IFAAMAS, Pages: 1275-1276
Broda K, Clark K, Miller R, et al., 2009, SAGE: A Logical Agent-Based Environment Monitoring and Control System, 3rd European Conf. on Ambient Intelligence
Ma J, Russo A, Broda K, et al., 2009, Multi-agent Planning with Confidentiality, 8th Int. Conf. on Autonomous Agents and Multi-Agent Systems
Ma J, Russo A, Broda K, et al., 2009, On the Implementation of Speculative Constraint Processing, 10th Int. Workshop on Computational Logic for Multi-Agent Systems
Kimber T, Broda K, Russo A, 2009, Induction on Failure: Learning Connected Horn Theories, LPNMR09, Logic Programming and Non-Monotonic Reasoning, Pages: 169-181, ISSN: 0302-9743
Broda K, 2008, Book Review, Journal of Logic, Language and Information, Vol: 17, Pages: 229-231, ISSN: 0925-8531
Dickens L, Broda K, Russo A, 2008, Transparent modelling of finite stochastic processes for multiple agents, Departmental Technical Report: 08/2, Publisher: Department of Computing, Imperial College London, 08/2
Stochastic Processes are ubiquitous, from automated engineering, through financialmarkets, to space exploration. These systems are typically highly dynamic, unpredictableand resistant to analytic methods; coupled with a need to orchestrate long controlsequences which are both highly complex and uncertain. This report examines some existingsingle- and multi-agent modelling frameworks, details their strengths and weaknesses,and uses the experience to identify some fundamental tenets of good practice in modellingstochastic processes. It goes on to develop a new family of frameworks based on these tenets,which can model single- and multi-agent domains with equal clarity and flexibility, whileremaining close enough to the existing frameworks that existing analytic and learning toolscan be applied with little or no adaption. Some simple and larger examples illustrate thesimilarities and differences of this approach, and a discussion of the challenges inherent indeveloping more flexible tools to exploit these new frameworks concludes matters.
Dickens L, Broda K, Russo A, 2008, Modelling MAS as Finite Analytic Stochastic Processes, AISB 2008
Ma K, Broda K, Clark KL, et al., 2008, DARE: A System for Distributed Abductive REasoning, Journal of Autonomous Agents and Multi-Agent Systems
Ma J, Broda K, Clark K, et al., 2008, A Dynamic System for Distributed Reasoning, International Symposium on Architectures for Intelligent Theory Based Agents
Broda K, Ma J, Sinnadurai G, et al., 2007, Pandora: A reasoning toolbox using natural deduction style, LOGIC JOURNAL OF THE IGPL, Vol: 15, Pages: 293-304, ISSN: 1367-0751
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Broda K, Hogger C, 2007, Abstraction as a Tool for Multi-Agent Policy Evaluation, AISB 2007 Symposium on Artificial Societies for Ambient Agents
Broda K, Ma J, Sinnadurai G, et al., 2007, Pandora: A Reasoning Toolbox using Natural Deduction Style, Logic Journal of the IGPL
Kamoda H, Yamaoka M, Matsuda S, et al., 2006, Access Control Policy Analysis Using Free Variable Tableaux, Information Processing Society of Japan (IPSJ)Digital Courier, Vol: 2, Pages: 207-221
Kamoda H, Yamaoka M, Matsuda S, et al., 2006, Access Control Policy Analysis Using Free Variable Tableaux, Information Processing Society of Japan (IPSJ)Digital Courier, Vol: 2, Pages: 207-221
Broda K, Hogger C, 2006, Optimizing minimal agents through abstraction, Departmental Technical Report: 06/4, Publisher: Department of Computing, Imperial College London, 06/4
Abstraction is a valuable tool for dealing with scalability in large statespace contexts. This paper addresses the design, using abstraction, of good policiesfor minimal autonomous agents applied within a situation-graph-framework.In this framework an agent’s policy is some function that maps perceptual inputsto actions deterministically. A good policy disposes the agent towards achievingone or more designated goal situations, and the design process aims to identifysuch policies. The agents to which the framework applies are assumed to haveonly partial observability, and in particular may not be able to perceive fully agoal situation. A further assumption is that the environment may influence anagent’s situation by unpredictable exogenous events, so that a policy cannot takeadvantage, of a reliable history of previous actions. The Bellman discount measureprovides a means of evaluating situations and hence the overall value of apolicy. When abstraction is used, the accuracy of the method can be significantlyimproved by modifying the standard Bellman equations. This paper describesthe modification and demonstrates its power through comparison with simulationresults.
Broda K, Hogger CJ, 2006, Designing effective policies for minimal agents, Departmental Technical Report: 06/3, Publisher: Department of Computing, Imperial College London, 06/3
A policy for a minimal reactive agent is a set of condition-action rules used todetermine its response to perceived environmental stimuli. When the policy pre-disposes theagent to achieving a stipulated goal we call it a teleo-reactive policy. This paper presents aframework for constructing and evaluating teleo-reactive policies for one or more minimalagents, based upon discounted-reward evaluation of policy-restricted subgraphs of completesituation-graphs. The main feature of the method is that it exploits explicit and definiteassociations of the agent’s perceptions with states. The combinatorial burden that wouldpotentially ensue from such associations can be ameliorated by suitable use of abstractions.The framework allows one to plan for a number of agents by focusing upon the behaviourof a single representative of them. It allows for varied behaviour to be modelled, includingcommunication between agents. Simulation results presented here indicate that the methodaffords a good degree of scalability and predictive power.
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