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Query Caching in Agent Programming LanguagesNatasha Alechina 1 , Tristan Behrens 2 , Koen Hindriks 3 , and Brian Logan 11 School of Computer ScienceUniversity of NottinghamNottingham NG8 1BB UK2 Department of InformaticsClausthal University of TechnologyGermany3 Delft University of TechnologyAbstract. Agent programs are increasingly widely used for large scale, timecritical applications. In developing such applications, the performance of theagent platform is a key concern. Many logic-based BDI-based agent programminglanguages rely on inferencing over some underlying knowledge representation.While this allows the development of flexible, declarative programs, repeatedinferencing triggered by queries to the agent’s knowledge representationcan result in poor performance. In this paper we present an approach to querycaching for agent programming languages. Our approach is motivated by the observationthat agents repeatedly perform queries against a database of beliefs andgoals to select possible courses of action. Caching the results of previous queries(memoization) is therefore likely to be beneficial. We develop an abstract modelof the performance of a logic-based BDI agent programming language. Using ourmodel together with traces from typical agent programs, we quantify the possibleperformance improvements that can be achieved by memoization. Our resultssuggest that memoization has the potential to significantly increase the performanceof logic-based agent platforms.1 IntroductionBelief-Desire-Intention (BDI) based agent programming languages facilitate the developmentof rational agents specified in terms of beliefs, goals and plans. In the BDIparadigm, agents select a course of action that will achieve their goals given their beliefs.To select plans based on their goals and beliefs, many logic-based BDI-basedagent programming languages rely on inferencing over some underlying knowledgerepresentation. While this allows the development of flexible, declarative programs, repeatedinferencing triggered by queries to the agent’s knowledge representation canresult in poor performance. When developing multiagent applications for large scale,time critical applications such performance issues are often a key concern, potentiallyadversely impacting the adoption of BDI-based agent programming languages and platformsas an implementation technology.In this paper we present an approach to query caching for agent programming languages.Our approach is motivated by the observation that agents repeatedly performqueries against a database of beliefs and goals to select possible courses of action.117
Caching the results of previous queries (memoization) is therefore likely to be beneficial.Indeed caching as used in algorithms such as Rete [1] and TREAT [2] has beenshown to be beneficial in a wide range of related AI applications, including cognitiveagent architectures, e.g., [3], expert systems, e.g., [4], and reasoners, e.g., [5]. Howeverthat work has focused on the propagation of simple ground facts through a dependencynetwork. In contrast, the key contribution of this paper is to investigate the potential ofcaching the results of arbitrary logical queries in improving the performance of agentprogramming languages. We develop an abstract model of the performance of a logicbasedBDI agent programming language, defined in terms of the basic query and updateoperations that form the interface to the agent’s knowledge representation. Using ourmodel together with traces from typical agent programs, we quantify the possible performanceimprovements that can be achieved by memoization. Our results suggest thatmemoization has the potential to significantly increase the performance of logic-basedagent platforms.The remainder of the paper is organised as follows. In Section 2 we introduce an abstractmodel of the interface to a logic-based BDI agent’s underlying knowledge representationand an associated performance model. Section 3 presents experimental resultsobtained from traces of typical agent programs and several key observations regardingquery and update patterns in these programs. Section 4 introduces two models to exploitthese observations and improve the efficiency of the use of Knowledge RepresentationTechnologies (KRTs) by agent programs. Section 5 discusses related work, and Section6 concludes the paper.2 Abstract Performance ModelIn this section, we present an abstract model of the performance of a logic-based agentprogramming language as a framework for our analysis. The model abstracts away detailsthat are specific to particular agent programming languages (such as Jason [6],2APL [7], and GOAL [8]), and focuses on key elements that are common to most, if notall, logic-based agent programming languages.The interpreter of a logic-based BDI agent programming language repeatedly executesa ‘sense-plan-act’ cycle (often called a deliberation cycle [9] or agent reasoningcycle [6]). The details of the deliberation cycle vary from language to language, but inall cases it includes processing of events (sense), deciding on what to do next (plan),and executing one or more selected actions (act). In a logic-based agent programminglanguage, the plan phase of the deliberation cycle is implemented by executing the setof rules comprising the agent’s program. The rule conditions consist of queries to beevaluated against the agent’s beliefs and goals (e.g., plan triggers in Jason, the headsof practical reasoning rules in 2APL) and the rule actions consist of actions or plans(sequences of actions) that may be performed by the agent in a situation where the rulecondition holds. In the act phase, we can distinguish between two different kinds ofactions. Query actions involve queries against the agent’s beliefs and goals and do notchange the agent’s state. Update actions, on the other hand, are either actions that directlychange the agent’s beliefs and goals (e.g., ‘mental notes’ in Jason, belief update118
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Proceedings of the Tenth Internatio
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OrganisationOrganising CommitteeMeh
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Table of ContentseJason: an impleme
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in Sect. 3 the translation of the J
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init_count(0).max_count(2000).(a)(b
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For instance, a failure in the ERES
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{plan, fun start_count_trigger/1,fu
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single parameter, an Erlang record
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1. Belief annotations. Even though
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decisions taken during the design a
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Conceptual Integration of Agents wi
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Fig. 2. Active component structurep
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the service provider component. As
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Fig. 4. Web Service Invocationretri
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01: public interface IBankingServic
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tate them in the same way as in the
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01: public interface IChartService
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implementations being available for
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deliberative behavior in BDI archit
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layer modules (i.e. nodes) can be d
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different methods to choose the cur
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also a single scheduler module, imp
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andom choice (OR), conditional choi
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- Dealing with conflicts based on p
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5. Brooks, R. A. (1991) Intelligenc
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An Agent-Based Cognitive Robot Arch
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It has been argued that building ro
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EnvironmentHardwareLocal SoftwareC+
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a cognitive layer can connect as a
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can reliably be differentiated and
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4 ExperimentTo evaluate the feasibi
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Page 70 and 71: A Programming Framework for Multi-A
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Page 74 and 75: Although some success [13] [14] hav
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Page 85 and 86: ACKNOWLEDGMENTThis work has been su
Page 87 and 88: The code was analysed both objectiv
Page 89 and 90: a conversation is following. Additi
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Page 93 and 94: Table 1. Core Agent ProtocolsAgent
Page 95 and 96: statistically significant using an
Page 97 and 98: to the conversation and has a perfo
Page 99 and 100: principal reasons. Firstly, it is a
Page 101 and 102: 2. Muldoon, C., O’Hare, G.M.P., C
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Page 105 and 106: DevelopmentProductionHuman Readabil
Page 107 and 108: will then evaluate this new format
Page 109 and 110: encoder, it is first checked if the
Page 111 and 112: nents themselves. However, since th
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Page 115 and 116: Java serialization and Jadex Binary
Page 117: 10. P. Hoffman and F. Yergeau, “U
Page 121 and 122: querying an agent’s beliefs and g
Page 123 and 124: or relative performance of each pla
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Page 127 and 128: Size N K n p c qry U c upd Update c
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Page 131 and 132: 6 ConclusionWe presented an abstrac
Page 133 and 134: Typing Multi-Agent Programs in simp
Page 135 and 136: 1 // agent ag02 iterations (" zero
Page 137 and 138: 3.1 simpAL OverviewThe main inspira
Page 139 and 140: 3.2 Typing Agents with Tasks and Ro
Page 141 and 142: Defining Agent Scripts in simpAL (F
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Page 149 and 150: Learning to Improve Agent Behaviour
Page 151 and 152: 2.1 Agent Programming LanguagesAgen
Page 153 and 154: choosing actions is to find a good
Page 155 and 156: 1 init module {2 knowledge{3 block(
Page 157 and 158: of a module. For example, to change
Page 159 and 160: if bel(on(X,Y), clear(X)), a-goal(c
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Page 163 and 164: the current percepts of the agent.
Page 165: Author IndexAbdel-Naby, S., 69Alelc
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