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OriginalAdaptive10410 210 1(a) Program A520 1,000 2,00000 1,000 2,00000 1,000 2,000(b) Program B(c) Program C30810 3 (d) Program A20610 210 10 2,000 4,0001000 2,000 4,0004200 2,000 4,000(e) Program B(f) Program CFig. 2: Comparison of the number of moves (y-axis) over successive episodes (xaxis)to solve randomly generated worlds of four (top row) and six (bottom row)blocks, with original (light shade) and adaptive (dark shade) rule ordering.ResultsFigure 2 shows the results of running the programs A, B, and C, with andwithout adaptive behaviours enabled (in dark and light shading respectively).Figure 2a, Figure 2b, and Figure 2c are results for problems with four blocks,while Figure 2d, Figure 2e, and Figure 2f are for problems with six blocks.Program A: In Figure 2a, the light shading shows that the average numberof steps taken by the original A to solve problems with four blocks is around350 moves. This is not very surprising since A is really only trying to solve theproblem using random moves. The dark shading shows the results for the sameset of problems and the same A, but using adaptive rule ordering. While initiallythe program performs similarly as it tries to find the first few solutions,it improves to around seven moves per problem by 100 episodes. Beyond thatit improves progressively and by the end of the experiment at 2000 episodesthe program takes around five moves per problem. Compared to our baselineprogram that averages 2(n − √ n) = 4, i.e., four moves for a problem with fourblocks, we can already see that the learnt ordering gives competitive perfor-159
mance. Figure 2d shows the same A for problems with six blocks using adaptiveordering. We have not included results for the original program since it takesover 30000 moves on average per problem. For adaptive mode, this number improvesto about 60 moves by the 100th episode, and progressively to around 12moves by 4000 episodes. This gets us close to the baseline of 2(n − √ n) = 7.1but not quite there. It would be possible to improve further if the program wasallowed to run for more episodes, but the improvement will occur very slowly.We also did not run this program for problems with more than six blocks assolving larger problems becomes impractical with this strategy.Program B: Figure 2b shows the performance of the original B for problemswith four blocks at around 11 moves. The performance is already reasonable tostart with as it is a more informed programmed strategy than A. With adaptiveordering, the performance improves to around five moves per problem by 100episodes. This is on par with the performance of A at 2000 episodes. At the endof the experiment, the program performs slightly above 4.5 moves and is closeto optimal. For six blocks, the original program averages around 28 moves perproblem as shown in Figure 2e. In adaptive mode, this improves to around 58moves by 100 episodes, and at the end of the experiment to around 10 moves.This is higher than the baseline of 7.1 moves but slightly better than adaptiveperformance with A that averages around 12 moves in that timeframe. Overall,B performs far better than A due to its informed strategy, and this performancealso translates to faster and better learning.Program C : In contrast to the other programs, C is already known to performclose to optimal, and achieves around 4.5 moves on average per problem of fourblocks as shown in Figure 2c. With adaptive ordering, this does not seem toimprove in the 2000 episodes that we ran the experiment for. This is expectedsince the program is already performing close to optimal. However, interestinglywe know from previous studies that C does not perform optimally for certain“deadlock” cases. We would have hoped to overcome this using learning but fromthe averaged results this is not evident as there is no significant difference in theperformance with and without learning. Importantly, for six blocks, for the firsttime in the experiments we see that the adaptive ordering actually performsworse than the original program in Figure 2f, albeit by only 0.25 moves perproblem on average at its worst. On closer analysis this seems to be becausewe simply have not run the experiment long enough. Certainly the differencebetween the two modes of execution is diminishing as the experiment progressesand is evident in Figure 2f. We should note that regardless, the performanceof C with or without learning is significantly better than the other programs ataround 8 moves and only slightly higher than the baseline case of 2(n− √ n) = 7.1.Overall, we can conclude that C is already very informed about the domain, solearning is not very useful in this case.Interestingly, in all experiments, adaptive mode does not do any worse thanthe default behaviour. This is a useful insight for agent programmers who mayotherwise feel reluctant to try a “black box” technology that directly impactsthe performance of the agent but that they do not really understand. Anotherimportant point is that the performance improvement with adaptive mode is160
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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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In the following section we will at
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DevelopmentProductionHuman Readabil
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will then evaluate this new format
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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
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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 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
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Page 157 and 158: of a module. For example, to change
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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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