Micalizio, R

Sormano, Samuele

Torta, G

English

Institutional Research Information System University of Turin

Explaining Failures Propagations in the Executionof Multi-Agent Temporal PlansExtended AbstractGianluca TortaUniversità di Torino, Dip.Informatica,Torino, Italygianluca.torta@unito.itRoberto MicalizioUniversità di Torino, Dip.Informatica,Torino, Italyroberto.micalizio@unito.itSamuele SormanoUniversità di Torino, Dip.Informatica,Torino, Italysamuele.sormano@edu.unito.itABSTRACTThis paper addresses the problem of explaining execution failuresin TemporalMultiagent Plans (TMAPs). A diagnosis identifies faultyactions (primary failures), and those that were affected by faultpropagation (secondary failures). Temporal explanations group di-agnoses for increasing understanding.KEYWORDSTemporal Multi-agent Plans; Diagnosis; Explanation; SMTACM Reference Format:Gianluca Torta, Roberto Micalizio, and Samuele Sormano. 2019. ExplainingFailures Propagations in the Execution of Multi-Agent Temporal Plans. InProc. of the 18th International Conference on Autonomous Agents and Multia-gent Systems (AAMAS 2019), Montreal, Canada, May 13–17, 2019, IFAAMAS,3 pages.1 INTRODUCTIONMultiagent plans (MAPs) accomplish complex goals by decompos-ing them into subgoals, and then organizing the activities of a teamof agents. Plan execution, however, is not always straightforward.The actual execution of actions, in fact, can be affected by failures.When a failure occurs, detecting and diagnosing it is of primaryimportance in order to resume the nominal execution.Some recent works [1, 4, 5] have addressed the problem of di-agnosing the execution of a MAP. These works, however, do notmodel time explicitly, but only implicitly by assuming a sequenceof discrete time steps at which atomic actions are performed. Asa consequence, anomalous deviations on the duration of actionscannot be detected and diagnosed. Other approaches [6–8], on theother hand, have focused only on the temporal dimension disre-garding that a faulty action may not achieve all (or some) of itsexpected effects. Both families of approaches, thus, can’t handle ap-plications where durative actions may take longer than expected,as well as fail in producing the expected results.This paper addresses the diagnosis of the execution of a Temporal-MAP (TMAP), that is a plan where both missing effects and tem-poral deviations can occur. We adopt a consistency-based notionof diagnosis: a MAP diagnosis is a subset of actions whose non-nominal behavior is consistent with the observations received soProc. of the 18th International Conference on Autonomous Agents and Multiagent Sys-tems (AAMAS 2019), N. Agmon, M. E. Taylor, E. Elkind, M. Veloso (eds.), May 13–17, 2019,Montreal, Canada. © 2019 International Foundation for Autonomous Agents and Mul-tiagent Systems (www.ifaamas.org). All rights reserved.far. The Z3 Satisfiability Modulo Theories (SMT) solver [2], is ex-ploited for inferring all the minimal rank (i.e., most likely) diag-noses consisting in an attribution of a (possibly faulty) modalityto each plan action. Diagnoses are then complemented with a setof temporal explanations highlighting contingent causal dependen-cies that might have occurred, and help a user gain the awarenessof how a primary action failure has caused other secondary ones.2 PLAN EXECUTION FAILURE PROBLEMA rigorous formalization of a TMAP and of a Plan Execution Fail-ure (PEF) problem can be found in [9], due to lack of space we givehere just some intuitive notions. Figure 1 exemplifies a TMAP inthe logistic domain. The picture shows the causal (dashed edges)and precedence (solid edges) constraints between actions. It is ex-pected that a nominal execution of the plan will satisfy all theseconstraints. However, the actual execution could be affected by ac-tion failures whose consequencesmay lead an agent to violate bothtypes of constraints. Indeed, the temporal aspect is modeled by as-sociating each actionawith a set ofmodalitiesM(a):{N , F1, F2, . . .}.The nominal modality N denotes the duration (as an interval) ofthe action under normal condition, and has rank 0, meaning thatis preferred to other, faulty modes. The remaining faulty modesin M(a) are associated with ever greater durations and ranks andwith the specification of what effects will be missing when a isperformed in that mode. As usual in model-based diagnosis, a PEFproblem arises when, given a set of observationsObs , the nominalhypothesis, that is the assumption that each action behaves in itsag1:ag2:ag3:ag4:ac11load(o, loc1)ac12move(loc1,loc2)ac13put(o,loc2)ac21move(loc3, loc2)ac22load(o, loc2)ac23move(loc2,loc4)ac24put(o, loc4)ac31move(loc5,loc4)ac32load(o,loc4)ac33move(loc4,loc2)ac34put(o,loc2)ac41move(loc7, loc6)ac42move(loc6, loc5)ac43move(loc5, loc2)ac44load(o, loc2)Figure 1: An example TMAP.Extended Abstract AAMAS 2019, May 13-17, 2019, Montréal, Canada2232normal mode N , is no longer consistent. Note that in our frame-work observations have the form 〈e, t〉, where e is a literal (e.g.,at(aд1,p1) and ¬at(aд1,p1)), and t is the time at which the ob-servation is captured. When the nominal hypothesis is no longerconsistent with Obs , a diagnostic inference is needed to find a set∆ of alternative hypotheses that are consistent with Obs . The set∆ is a solution to a PEF problem if each hypothesis δ ∈ ∆ is anassignment of modes to every action in the TMAP, such that it isconsistent withObs and its rank (order-of-magnitude inverse prob-ability, see [3]) is minimal. Where the rank of δ is simply defined asthe sum of the ranks of the action modes in δ . A solution δ to a PEFproblem highlights with a special mode FP that some actions havebeen affected by previously occurred action failures. For example(Fig. 1), the move(loc3, loc2) may have a delay (mode F1), so thatthe object released by aд1 at loc2 is actually loaded by aд4. This sit-uation makes actions ac22, ac24, ac32, and ac34 fail with mode FP .The diagnosis δex (except for N modes) lists: ac21(F1), ac22(FP ),ac24(FP ), ac32(FP ), ac34(FP ).3 EXPLAINING FAILURE PROPAGATIONSA diagnosis as the one just discussed above is not sufficient, for theuser, to understand what has actually happened. In general, fail-ures can propagate via the shared literals, that is, via the servicesproduced by an action and consumed by another one. For example,an action may fail because one of the required inputs is not avail-able at the right time, and this may happen because the producerfailed in supplying it (including supplying it with too much delay),or because another action has erroneously consumed the servicein its place. Explaining δ , thus, means tracing back the temporalrelations among the actions that are related to some shared literalof interest, and whose occurrence justifies a secondary failure.To this end, we define a Temporal Explanation E(δ ,R) of δ w.r.t.a shared literal R as a set of Allen algebra relations (i.e., before,after, during, . . .) among the actions in P that produce/consume R.A (full) explanation of a diagnosis δ is simply a set E(δ ) of severalsub-explanations E(δ ,R), one for each shared literal 1.Let us refer to diagnosis δex from section 2. The producers ofliteral R = at(o, loc2) are ac13, and ac34; while the consumers of Rare: ac22, and ac44. Figure 2 shows E(δex ,R) graphically on a di-agram where time increases from left to right. Note that, besidesthe actions related with R and their Allen algebra relations speci-fied by E(δ ,R) (black), the schema also shows some other actionswith mode assignments specified by the diagnosis δ (gray); suchactions are depicted just to further increase the information con-veyed by the schema to the reader.The set of non-FP actions that have to do with R are just ac13and ac44, so that the timeline is partitioned in five regions (dot-ted vertical bars): before ac13; during ac13; between ac13 and ac44;during ac44; after ac44. The definition of explanation requires usto relate ac13 and ac44, and a possible scenario is ac13 before ac44,i.e., when ac13 ends, some time passes before ac44 becomes readyand consumes at(o, loc2). Note that R would be available for otherconsumers between the end of ac13 to the start of ac44. However,according to explanation E(δex ,R), ac22 becomes ready and then1Note that, given a diagnosis δ , it is in general possible to find several alternativeexplanations, corresponding to different orders of events compatible with δ .Figure 2: A Temporal Explanation of Diagnosis δex ={ac21(F1), ac22(FP ), ac24(FP ), ac32(FP ), ac34(FP )}.CBFStime #sol time/sol #explag 2ac 8 (R2) 0.48 2.0 0.24 2.0ag 4ac 10 (R2) 1.32 2.5 0.53 3.0ac 20 (R2) 6.83 4.0 1.71 6.1ac 20 (R4) 25.53 15.6 1.64 23.2Table 1: avg time (sec), sols, time/sol, and expls in experi-ments.fails with mode FP (segment starting with > and ending with ^)only after ac44 ends. By looking at the figure, it is easy to see thatsuch a delay is due to the failure with mode F1 of action ac21. Thefigure also shows actions ac24 and ac32, that are respectively a putand load related to another literal at(o, loc4) (indicated by the րafter the actions), which fail as a consequence of the failure of ac22(see Fig. 1). Also ac34 fails as a consequence of the failure of ac32.4 IMPLEMENTATION AND TESTSWe have encoded the check of consistency of a hypothesis δ as anSMT problem for the solver Z3, and used a conflict-based best firstsearch (CBFS) written in Java to find all the minimal rank diag-noses and explanations. For more details about CBFS see [9]. Thetests have been run on a machine running Ubuntu 18.04.1 LTS,equipped with an i7 7700HQ CPU at 2.80 GHz, and 8 GB RAM. Wehave considered a Logistic domain where agents can move, load,and put objects, giving rise to several kinds of inter-agent interac-tions. We have experimented our approach by running a numberof software simulated tests under different configurations, definedby varying the following dimensions: #ag (2 and 4 agents), #ac (8,10, 20) actions per agent, #rnk (injected failures of ranks 2, 4). Theobservability rate (i.e., ratio between the number of actions withobservable effects and the total number of actions) was 30%. In Ta-ble 1, we show results obtained with 4 different configurations ofincreasing complexity. The average total time for solving the PEFproblems goes from 0.48s (2 agents x 8 actions, rank 2), up to 25.53s(4 agents x 20 actions, rank 4). However, it should be noted that thetotal time includes the computation of all the preferred diagnoses,as well as their temporal explanations. If we look at the averagetime taken for computing each preferred diagnosis (including itsexplanations), the increase is more limited, since, as the test casesbecome more challenging, the average number of preferred diag-noses increases (from 2.0 to 15.6), as well as the average numberof associated explanations (from 2.0 to 23.2).Extended Abstract AAMAS 2019, May 13-17, 2019, Montréal, Canada2233REFERENCES[1] F. de Jonge, N. Roos, and C.Witteveen. 2009. Primary and secondary diagnosis ofmulti-agent plan execution. Journal of Autonomous Agent and MAS 18, 2 (2009),267–294.[2] Leonardo De Moura and Nikolaj Bjørner. 2008. Z3: An efficient SMT solver. InInternational conference on Tools and Algorithms for the Construction and Analysisof Systems. Springer, 337–340.[3] M. Goldszmidt and J. Pearl. 1992. Rank-based systems: a simple approach tobelief revision, belief update, and reasoning about evidence and actions. In Proc.KR92. 661–672.[4] M. Kalech and G. A. Kaminka. 2007. On the design of coordination diagnosisalgorithms for teams of situated agents. Artificial Intelligence 171, 8-9 (2007),491–513.[5] RobertoMicalizio and Pietro Torasso. 2014. CooperativeMonitoring to DiagnoseMultiagent Plans. Journal of Artificial Intelligence Research 51 (2014), 1–70.[6] Roberto Micalizio and Gianluca Torta. 2012. Diagnosing delays in multi-agentplans execution. In Proceedings of the 20th European Conference on Artificial In-telligence. IOS Press, 594–599.[7] Roberto Micalizio and Gianluca Torta. 2016. Explaining interdependent actiondelays in multiagent plans execution. Autonomous Agents and Multi-Agent Sys-tems 30, 4 (2016), 601–639.[8] N. Roos and C. Witteveen. 2008. Diagnosis of Simple Temporal Networks. InProc. of ECAI’08. 593–597.[9] Gianluca Torta and Roberto Micalizio. 2018. SMT-Based Diagnosis of Multi-Agent Temporal Plans. In Proceedings of the 17th International Conference onAutonomous Agents and MultiAgent Systems, AAMAS 2018, Stockholm, Sweden,July 10-15, 2018. 2097–2099.Extended Abstract AAMAS 2019, May 13-17, 2019, Montréal, Canada2234

Explaining Failures Propagations in the Execution of Multi-Agent Temporal Plans

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Explaining Failures Propagations in the Execution of Multi-Agent Temporal Plans

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