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A second observation that follows consistently from the data is that large percentagesof queries are repeated each update cycle. We have found that 22% up to even 92%of queries are repeated in consecutive update cycles.Observation 2 A significant number of queries are repeated at subsequent update cycles,i.e., p > 20%.Secondly, in all agent/environment/platform combinations investigated, in a singledeliberation cycle an agent performs only a few (perhaps only one) actions that directlyor indirectly change the state of the agent. This is also supported by the fact that thenumber of deliberation cycles in most cases is larger than the number of update cycles.In other words, the execution of a deliberation cycle does not always result in an update.Comparing the average number of updates with the average number of unique queries,we consistently find that many more queries are performed than updates in each cycle.Observation 3 The number of updates U (add, deletes) performed in an update cycleis significantly smaller than the number of unique queries K performed in that cycle,i.e. K ≫ U.Note that all three observations are independent of the size or complexity of theenvironment, the complexity of the agent program or the agent platform used. Thisstrongly suggest that the query and update performance of agent programs in the platformsinvestigated can be significantly improved.4 Query CachingThe observations in the previous section suggest that efficiency can be significantlyincreased by memoization, i.e. by caching query results. The cache stores answers toqueries, so that if the same query is performed again in the same update cycle, theanswers can be returned without recourse to the underlying knowledge representation.In this section, we first show how to modify the interface to the underlying knowledgerepresentation to incorporate caching. We then extend the abstract performancemodel introduced in Section 2 in order to analyse the potential increase in performanceof caching, and derive a relationship between n = N/K and the costs of maintainingthe cache which characterises when caching is likely to be beneficial.4.1 Extending the Knowledge Representation InterfaceThe most straightforward approach to exploit the fact that some queries are performedmultiple times in a single update cycle, is to add the results of a query to the cachethe first time it is performed in an update cycle, and then retrieve the results the fromthe cache if the query is reevaluated at the same update cycle. Although very simplein requiring no information about the average number of times each unique query isrepeated in a cycle, as we show below, if the cost of cache insertion is sufficiently low,significant performance improvements can be achieved. Moreover, such an approachrequires only a very loose coupling between the cache and the underlying knowledge127
epresentation. The cache simply acts as a filter: if a query is a cache hit the results areimmediately returned by the cache; if a query is a cache miss, the query is delegated tothe knowledge representation and the results stored in the cache, before being returnedto the agent program.The use of a cache requires an extension of the KRT interface with a cache operationlookup to lookup entries, an operation put to put entries into the cache, and anoperation clear to clear the cache again. The basic approach can be implemented asshown in the algorithm below.Listing 1.1: Query Cache1 % Query Phase2 clear(cache)3 FOR EACH query Q i DO4 IF lookup(Q i , answer, cache)5 THEN return(answer)6 ELSE DO7 answer = query(Q i , beliefbase)8 put(Q i :answer, cache)9 return(answer)10 ENDDO11 ENDDOOf course, by only storing the query results, it is not possible to detect when cacheentries are invalidated, so the cache needs to be cleared at the start of each query phase inan update cycle and rebuilt from scratch. In addition, when compiling an agent program,care is required to ensure that differences in variable names are resolved so that similarqueries are retrieved from the cache instead of being recomputed. For example, thequeries q(X, Y ) and q(A, B) which represent the same query but use different variablesshould not result in a cache miss.The cache can be implemented by a hash table. Given Observation 2, the size of thehash table can be tuned to optimal size after one or two cycles. By implementing thecache as a hash table, the insertion costs c ins of an entry are constant and the evaluationcosts of performing a query a second time are equal to the lookup costs, i.e. a constantc hit that represents the cost for a cache hit. This results in the following performancemodel, adapted from the model in Section 2:It follows that wheneverK · (c qry + c ins ) + N · c hit + U · c upd (2)c qry >KN − K · c ins +NN − K · c hit (3)it is beneficial to implement a cache. That is, the cache increases performance wheneverthe average query cost is greater than the average lookup cost of a query plus the averageinsertion cost times the proportion of unique to non-unique queries (for N > K). Asexpected, the larger the average number of times n a query is performed in a single128
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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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learn or gain knowledge from experi
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A Programming Framework for Multi-A
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exchange and storage of tuples (key
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Although some success [13] [14] hav
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as well as important non-functional
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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
Page 91 and 92: the context of a communication-heav
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
Page 103 and 104: In the following section we will at
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
Page 113 and 114: optimized for this format feature s
Page 115 and 116: Java serialization and Jadex Binary
Page 117 and 118: 10. P. Hoffman and F. Yergeau, “U
Page 119 and 120: Caching the results of previous que
Page 121 and 122: querying an agent’s beliefs and g
Page 123 and 124: or relative performance of each pla
Page 125 and 126: were run for 1.5 minutes; 1.5 minut
Page 127: Size N K n p c qry U c upd Update c
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
Page 143 and 144: that sends a message to the receive
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Page 147 and 148: Given an organization model, it is
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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