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entirely new rules from sample solutions encountered, as well as updating theutilities of existing rules from ongoing experience. While not a programminglanguage per se, ACT-R learning is nevertheless quite related in that Q-Learningis also used to learn rule preferences. SOAR [30] also uses production rules tocapture procedural knowledge about the domain. It uses a process called chunkingto create new production rules based on the results of subgoals, in a kindof explanation-based learning. SOAR-RL [31] integrates reinforcement learningto improve operator selection based on experience, similar to learnt utilities inACT-R. ICARUS [32] is a cognitive architecture that incorporates ideas from workon production systems, hierarchical task networks, and logic programming. Ituses a form of explanation-based learning to find the task hierarchy in hierarchicaltask networks. Overall, ACT-R, SOAR and our work share similarities in theway reinforcement learning is used to learn rule preferences, however the motivationsare quite different. While ACT-R is heavily used in cognitive psychologyresearch to model human behaviour, and SOAR is a general cognitive architecturefor building intelligent systems, Goal is an agent programming languagein the BDI tradition. For us, the key motivation for integrating learning is tomake adaptive technologies more accessible to agent programmers.4 The GOAL Agent Programming LanguageGoal is a logic-based agent programming language similar to 2APL [8] and Jason[4]. Goal agents maintain a dynamic mental state consisting of beliefs and goalsthat are represented in Prolog. Goal agents also have a static knowledge basethat is part of their mental state and consists of domain knowledge. They mayperform built-in actions that update their mental state or send a message as wellas actions that are available in the environment that the agent is connected to.Environment actions are specified using a STRIPS-like pre- and post-conditionspecification. A Goal agent derives its choice of action from its beliefs andgoals (in combination with its knowledge) by means of rules. Rules consist of acondition that is evaluated on the mental state of the agent and one or moreactions that may be executed if the condition holds. In addition, Goal supportsmultiple types of rules, rule evaluation strategies, and modules that facilitatestructured programming.Figure 1 provides a listing of a simple example Goal agent program for theBlocks World [33]. We have used this program also in our experiments to evaluatethe learning mechanism we have added to the language. The Blocks World is awell studied toy domain that has been used extensively in artificial intelligenceresearch. The setup consists of a fixed number of blocks that are sitting on a tablebig enough to hold them all. Each block exists on top of exactly one other objectthat can either be another block or the table itself. Each block is considered tobe clear when no other block exists on top of it. There is only one type of actionthat is possible in this domain: move a single clear block, either from anotherblock onto the table, or from an object onto another clear block. A problemspecification in this domain consists of an initial configuration of blocks, as well153
1 init module {2 knowledge{3 block(X) :- on(X,_).4 clear(X) :- block(X), not( on(_,X)).5 clear(table).6 }7 actionspec{8 move(X,Y) {9 pre{ clear(X), clear(Y), on(X,Z), not(on(X,Y)) }10 post{ not(on(X,Z)), on(X,Y) }11 }12 }13 goals{ on(b1,table), on(b2,b1), on(b3,b2), on(b4,b3). }14 beliefs{}15 }16 event module { program {17 forall bel(percept(on(X,Y)), not(on(X,Y))) do insert(on(X,Y)).18 forall bel(on(X,Y), not(percept(on(X,Y)))) do delete(on(X,Y)).19 }}20 main module { program[order=random] {21 if bel(true) then move(X,Y).22 }}Fig. 1: A simple Goal agent for solving the Blocks World problemas the desired configuration. The task for the agent is to move blocks aroundone at a time until the final configuration is realised.The program processes percepts and randomly selects an action that is enabledin the Blocks World environment. The init module consists of code toinitialize the mental state of the agent and a single action specification for themove(X,Y) action that the agent can perform in the Blocks World. The Prologrules in the knowledge section of this module define the concepts of a blockand a block being clear. An initial goal is specified in the goals section. Initiallythe agent has no beliefs; the agent must first perceive the environment toobtain information about the blocks’ configuration. The event module is executedat the start of each decision or reasoning cycle of an agent. Its purposeis to process received percepts (and messages). The two forall rules part ofthis module process the percepts received at the start of the cycle of the formpercept(on(X,Y)). The first rule checks whether the agent sees that a block Xis on top of a block Y and inserts this fact if the agent does not currently believeit; the second rule removes facts that are believed but not perceived (assumingfull observability this is a sound rule).The main module consists of the decision logic for acting in the environmentand selects actions after the mental state has been updated with the most recentperceptual information. The option order=random associated with the program154
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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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component plans have been instantia
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A in the example) can evaluate all
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1. robot-1 issues a Localization(ro
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ACKNOWLEDGMENTThis work has been su
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The code was analysed both objectiv
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a conversation is following. Additi
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the context of a communication-heav
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Table 1. Core Agent ProtocolsAgent
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statistically significant using an
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to the conversation and has a perfo
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principal reasons. Firstly, it is a
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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 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 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
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: choosing actions is to find a good
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
Page 161 and 162: mance. Figure 2d shows the same A f
Page 163 and 164: the current percepts of the agent.
Page 165: Author IndexAbdel-Naby, S., 69Alelc
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