Knowledge Base Completion: Baselines Strike Back

Many papers have been published on the knowledge base completion task in the past few years. Most of these introduce novel architectures for relation learning that are evaluated on standard datasets such as FB15k and WN18. This paper shows that the accuracy of almost all models published on the FB15k can be outperformed by an appropriately tuned baseline - our reimplementation of the DistMult model. Our findings cast doubt on the claim that the performance improvements of recent models are due to architectural changes as opposed to hyper-parameter tuning or different training objectives. This should prompt future research to re-consider how the performance of models is evaluated and reported

Text Understanding with the Attention Sum Reader Network

Several large cloze-style context-question-answer datasets have been introduced recently: the CNN and Daily Mail news data and the Children's Book Test. Thanks to the size of these datasets, the associated text comprehension task is well suited for deep-learning techniques that currently seem to outperform all alternative approaches. We present a new, simple model that uses attention to directly pick the answer from the context as opposed to computing the answer using a blended representation of words in the document as is usual in similar models. This makes the model particularly suitable for question-answering problems where the answer is a single word from the document. Ensemble of our models sets new state of the art on all evaluated datasets.Comment: Presented at ACL 201

DyBaNeM: Bayesovský model epizodické paměti

Title: DyBaNeM: Bayesian Model of Episodic Memory Author: Mgr. Rudolf Kadlec E-mail: [email protected] Department: Department of Software and Computer Science Education Supervisor: Mgr. Cyril Brom, Ph.D. Department of Software and Computer Science Education Abstract: Artificial agents endowed with episodic (or autobiographic) memory systems have the abilities to remember and recall what happened to them in the past. The existing Episodic Memory (EM) models work as mere data-logs with indexes: they enable record, retrieval and delete operations, but rarely organize events in a hierarchical fashion, let alone abstract automatically detailed streams of "what has just happened" to a "gist of the episode." Consequently, the most interest- ing features of human EM, reconstructive memory retrieval, emergence of false memory phenomena, gradual forgetting and predicting surprising situations are out of their reach. In this work we introduce a computational framework for episodic memory modeling called DyBaNeM. DyBaNeM connects episodic mem- ory abilities and activity recognition algorithms and unites these two computer science themes in one framework. This framework can be conceived as a general architecture of episodic memory systems, it capitalizes on Bayesian statistics and, from the psychological...Název práce: DyBaNeM: Bayesovský model epizodické paměti Autor: Mgr. Rudolf Kadlec E-mail: [email protected] Katedra: Kabinet software a výuky informatiky Vedoucí disertační práce: Mgr. Cyril Brom, Ph.D. Kabinet software a výuky informatiky Abstrakt: Umělí agenti vybavení epizodickou (nebo autobiografickou) pamětí mají schop- nost zapamatovat si a následně si i vybavit, co se jim stalo v minulosti. Stávající modely epizodické paměti (EP) fungují jako pouhé logy s indexy: umožňují záznam, vyhledávání a mazání vzpomínek, ale jen zřídka uchovávají agentovu aktivitu v hierarchické podobě, natož aby umožňovaly automaticky abstraho- vat pozorovanou aktivitu do obecnějších epizod. V důsledku toho nejzajímavější rysy lidské EP, jako jsou rekonstrukce vzpomínek, vznik falešných vzpomínek, postupné zapomínání a předpovídání překvapivých situací, zůstávají mimo jejich dosah. V této práci představíme výpočetní model epizodické paměti pojmenovaný DyBaNeM. DyBaNeM propojuje modelování EP s algoritmy pro rozpoznávání aktivit v jednom výpočetním modelu. DyBaNeM staví na principech Bayesovské statistiky a na takzvané Fuzzy-Trace teorii vycházející z oblasti výzkumu falešných vzpomínek. V práci bude představeno několik...Katedra softwaru a výuky informatikyDepartment of Software and Computer Science EducationFaculty of Mathematics and PhysicsMatematicko-fyzikální fakult

Variance Reduction in Monte Carlo Counterfactual Regret Minimization (VR-MCCFR) for Extensive Form Games using Baselines

Learning strategies for imperfect information games from samples of interaction is a challenging problem. A common method for this setting, Monte Carlo Counterfactual Regret Minimization (MCCFR), can have slow long-term convergence rates due to high variance. In this paper, we introduce a variance reduction technique (VR-MCCFR) that applies to any sampling variant of MCCFR. Using this technique, per-iteration estimated values and updates are reformulated as a function of sampled values and state-action baselines, similar to their use in policy gradient reinforcement learning. The new formulation allows estimates to be bootstrapped from other estimates within the same episode, propagating the benefits of baselines along the sampled trajectory; the estimates remain unbiased even when bootstrapping from other estimates. Finally, we show that given a perfect baseline, the variance of the value estimates can be reduced to zero. Experimental evaluation shows that VR-MCCFR brings an order of magnitude speedup, while the empirical variance decreases by three orders of magnitude. The decreased variance allows for the first time CFR+ to be used with sampling, increasing the speedup to two orders of magnitude

Towards fast prototyping of IVAs behavior: Pogamut 2.

Abstract. We present the platform for IVAs development in the human like environment of the first-person shooter game Unreal Tournament 2004. This environment is extendible and supported by vast community of users. Based on our previous experience the problem of fast verification of models of artificial intelligence or IVAs is in implementation issues. The developer spends most of his time solving technical environment dependent issues and malfunctions, which drives him away from his goals. Therefore our modular platform provides a tool, which helps solving those problems and the developer can spend saved time by solving another AI based issues and model verification. The platform is aimed for research and educational purposes

Adaptive agents and emotions

This thesis investigates possible assets of emotions for autonomous adaptive agents working in environments similar to the real world. In living organisms, emotions have developed as a mechanism of adaptation to the surrounding environment. Therefore it is worth asking whether mechanisms similar to emotions can be implemented in models of autonomous agents. In this thesis a model of ethology inspired agent using reinforcement learning was implemented. This model suggests that emotions influence the balance between exploring new strategies and exploiting the strategies already known (the so-called explore/exploit problem). Negative emotional evaluation leads to changes in action selection strategy. The emotional version proved to be better than the non-emotional one in some environments. In other types of environments, the expectations have not been fulfilled. The instability of the received results is probably caused by non-optimal parameterization of the whole model.Tato práce zkoumá možný přinos emocí pro autonomní adaptivní agenty pracující v prostředích podobných skutečnému světu. Emoce u živých organismů vznikly jako jeden z mechanismů adaptace na okolní prostředí a je proto na místě klást si otázku zda by se jim podobné mechanismy nedaly přenést i do modelů autonomních agentů. V rámci práce byl implementován model etologicky inspirovaného agenta používajícího algoritmus zpětnovazebního učení. Emoce v něm ovlivňují vyvážení mezi průzkumem nových strategií a využíváním strategií již známých (tzv. "explore/exploit problem"). Negativní emocionální hodnocení současného počínání vede ke změně strategie výběru akcí. V některých typech prostředí dosahovala emocionální varianta agenta lepších výsledků než neemocionální, v jiných se ale očekávání nepotvrdila. Nestabilita výsledků je pravděpodobně zapříčiněna neoptimální parametrizací celého modelu.Katedra softwaru a výuky informatikyDepartment of Software and Computer Science EducationMatematicko-fyzikální fakultaFaculty of Mathematics and Physic

DyBaNeM: Bayesian Model of Episodic Memory

Title: DyBaNeM: Bayesian Model of Episodic Memory Author: Mgr. Rudolf Kadlec E-mail: [email protected] Department: Department of Software and Computer Science Education Supervisor: Mgr. Cyril Brom, Ph.D. Department of Software and Computer Science Education Abstract: Artificial agents endowed with episodic (or autobiographic) memory systems have the abilities to remember and recall what happened to them in the past. The existing Episodic Memory (EM) models work as mere data-logs with indexes: they enable record, retrieval and delete operations, but rarely organize events in a hierarchical fashion, let alone abstract automatically detailed streams of "what has just happened" to a "gist of the episode." Consequently, the most interest- ing features of human EM, reconstructive memory retrieval, emergence of false memory phenomena, gradual forgetting and predicting surprising situations are out of their reach. In this work we introduce a computational framework for episodic memory modeling called DyBaNeM. DyBaNeM connects episodic mem- ory abilities and activity recognition algorithms and unites these two computer science themes in one framework. This framework can be conceived as a general architecture of episodic memory systems, it capitalizes on Bayesian statistics and, from the psychological..

Adaptive agents and emotions

This thesis investigates possible assets of emotions for autonomous adaptive agents working in environments similar to the real world. In living organisms, emotions have developed as a mechanism of adaptation to the surrounding environment. Therefore it is worth asking whether mechanisms similar to emotions can be implemented in models of autonomous agents. In this thesis a model of ethology inspired agent using reinforcement learning was implemented. This model suggests that emotions influence the balance between exploring new strategies and exploiting the strategies already known (the so-called explore/exploit problem). Negative emotional evaluation leads to changes in action selection strategy. The emotional version proved to be better than the non-emotional one in some environments. In other types of environments, the expectations have not been fulfilled. The instability of the received results is probably caused by non-optimal parameterization of the whole model

Evoluce chování inteligentních agentů v počítačových hrách

In the present work we study evolution of both high-level and low-level behaviour of agents in the environment of the commercial game Unreal Tournament 2004. For optimization of high-level behaviour in Deathmatch and Capture the flag game modes a new functional architecture for description of player's behaviour was designed and implemented. Then a genetic programming technique was used to optimise it. Experiments with both standard evolution schema and with coevolution are presented. In second series of experiments the NEAT algo- rithm was used to evolve low-level missile avoidance behaviour (so called "dodging")