The Complexity of Planning Problems With Simple Causal Graphs

We present three new complexity results for classes of planning problems with simple causal graphs. First, we describe a polynomial-time algorithm that uses macros to generate plans for the class 3S of planning problems with binary state variables and acyclic causal graphs. This implies that plan generation may be tractable even when a planning problem has an exponentially long minimal solution. We also prove that the problem of plan existence for planning problems with multi-valued variables and chain causal graphs is NP-hard. Finally, we show that plan existence for planning problems with binary state variables and polytree causal graphs is NP-complete

Deep Policies for Width-Based Planning in Pixel Domains

Width-based planning has demonstrated great success in recent years due to its ability to scale independently of the size of the state space. For example, Bandres et al. (2018) introduced a rollout version of the Iterated Width algorithm whose performance compares well with humans and learning methods in the pixel setting of the Atari games suite. In this setting, planning is done on-line using the "screen" states and selecting actions by looking ahead into the future. However, this algorithm is purely exploratory and does not leverage past reward information. Furthermore, it requires the state to be factored into features that need to be pre-defined for the particular task, e.g., the B-PROST pixel features. In this work, we extend width-based planning by incorporating an explicit policy in the action selection mechanism. Our method, called $\pi$-IW, interleaves width-based planning and policy learning using the state-actions visited by the planner. The policy estimate takes the form of a neural network and is in turn used to guide the planning step, thus reinforcing promising paths. Surprisingly, we observe that the representation learned by the neural network can be used as a feature space for the width-based planner without degrading its performance, thus removing the requirement of pre-defined features for the planner. We compare $\pi$-IW with previous width-based methods and with AlphaZero, a method that also interleaves planning and learning, in simple environments, and show that $\pi$-IW has superior performance. We also show that $\pi$-IW algorithm outperforms previous width-based methods in the pixel setting of Atari games suite.Comment: In Proceedings of the 29th International Conference on Automated Planning and Scheduling (ICAPS 2019). arXiv admin note: text overlap with arXiv:1806.0589

The Influence of k-Dependence on the Complexity of Planning

A planning problem is k-dependent if each action has at most k pre-conditions on variables unaffected by the action. This concept is well-founded since k is a constant for all but a few of the standard planning domains, and is known to have implications for tractability. In this paper, we present several new complexity results for P(k), the class of k-dependent planning problems with binary variables and polytree causal graphs. The problem of plan generation for P(k) is equivalent to determining how many times each variable can change. Using this fact, we present a polytime plan generation algorithm for P(2) and P(3). For constant k> 3, we introduce and use the notion of a cover to find conditions under which plan generation for P(k) is polynomial

Bland mappar och bloggar

I denna studie behandlar vi hur bildlärare genom dokumentation synliggör elevers läroprocesser. Det är av vikt för läraren att se processen i det han/hon ska handleda elever med utgångspunkt i respektive elevs utvecklingszon. För eleverna själva är det viktigt för att kunna utveckla en förståelse för sitt eget lärande. En nödvändighet är därmed att läraren måste inneha verktyg för att kunna få syn på dessa processer. Problemet med det hela är lärarens arbetssituation som är komplex. Vi har upplevt ett uttryckt problem med de verktyg som finns i den tidspressade verkligheten. Vårt syfte blev därför att söka finna hanterbara verktyg för synliggörande av processer. Vår undersökning består av två delar, intervjuer och gestaltning. Intervjudelen innefattar tre intervjuer i halvstrukturerad form av slumpmässigt utvalda bildlärare samt dokumentation av fyra bildmappar och en blogg, tillhörande de intervjuade lärarna. Gestaltningsdelen var uppdelad i två moment. De begränsades till 15 timmar per del, vilket är ungefär detsamma som en termins bildundervisning för grundskolans äldre åldrar. I den första delen genomförde vi själva 15 stycken 60 minuters lektioner innefattande sju bilduppgifter som är avsedda för årskurs 9. I den andra gestaltningsdelen arbetade vi endast utifrån ett tema och med återkommande responstillfällen, även detta uppdelat i 15 stycken 60 minuters pass. Hela gestaltningsdelen dokumenterade vi genom att filma fyra lektionstillfällen, fotografera våra arbeten och skriva egna reflektioner efter varje lektionstillfälle. Vi fann att det var svårt att i de dokumentationsverktyg vi undersökte spåra läroprocesser. Genom vår undersökning synliggjorde vi brister i ett lektionsupplägg, vanligt förekommande i bildämnet. Detta upplägg och problem med att förankra nyare synsätt grundade på den pedagogiska forskningen relaterar till att man försöker realisera teorierna med traditionella undervisningsmetoder. Om processer skall bli synliga i dokumentationen krävs det att undervisningen ger utrymme och präglas av det dialogiska undersökande som främjar lärandeprocesserna. De dokumentationsformer som redan är vanliga och används ute i verksamheten kan fungera som synliggörande verktyg om dess innehåll istället för slutprodukter fokuserar på elevernas undersökande material, förutsatt att undervisningen genererar samma fokus. Det viktigaste verktyget för synliggörandet av processer vi fann var det egna lektionsinnehållet och förhållningssättet till lärarnas professionella objekt - lärandet

Usage of Network Simulators in Machine-Learning-Assisted 5G/6G Networks

Without any doubt, Machine Learning (ML) will be an important driver of future communications due to its foreseen performance when applied to complex problems. However, the application of ML to networking systems raises concerns among network operators and other stakeholders, especially regarding trustworthiness and reliability. In this paper, we devise the role of network simulators for bridging the gap between ML and communications systems. In particular, we present an architectural integration of simulators in ML-aware networks for training, testing, and validating ML models before being applied to the operative network. Moreover, we provide insights on the main challenges resulting from this integration, and then give hints discussing how they can be overcome. Finally, we illustrate the integration of network simulators into ML-assisted communications through a proof-of-concept testbed implementation of a residential Wi-Fi network

Bayes prediction of binary outcomes based on correlated discrete predictors.

An approach based on Bayes theorem is proposed for predicting the binary outcomes X = 0, 1, given that a vector of predictors Z has taken the value z. It is assumed that Z can be decomposed into 9 independent vectors given X = 1 and h independent vectors given X = 0. First, point and interval estimators are derived for the target probability P (X = 1|z). In a second step these estimators are used to predict the outcomes for new subjects chosen from the same population. Sample sizes needed to achieve reliable estimates of the target probability in the first step are suggested, as well as sample sizes needed to get stable estimates of the predictive values in the second step_ It is also shown that the effects of ignoring correlations between the predictors can be serious. The results are illustrated on Swedish data of work resumption among long-term sick-listed individuals

Effects of self-assessment on self-regulated learning and self-efficacy: Four meta-analyses

This meta-analytic review explores the effects of self-assessment on students' selfregulated learning (SRL) and self-efficacy. A total of 19 studies were included in the four different meta-analyses conducted with a total sample of 2305 students. The effects sizes from the three meta-analyses addressing effects on different measures of SRL were 0.23, 0.65, and 0.43. The effect size from the meta-analysis on self-efficacy was 0.73. In addition, it was found that gender (with girls benefiting more) and certain self-assessment components (such as self-monitoring) were significant moderators of the effects on selfefficacy. These results point to the importance of self-assessment interventions to promote students’ use of learning strategies and its effects on motivational variables such as self-efficacyFirst author funded by the Ministerio de Economía y Competitividad via Spanish Ramon y Cajal programme (Referencia RYC-2013-13469