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Neural networks are a powerful function approximation tool which has the ability to model any function with arbitrary precision. For any function as a black box, it is able to reconstruct the function given the target and the input data. However, there are problems where the target is at least partially unknown. In such cases it is impossible for a traditional neural network to compute the gradient...
This paper introduces SmartCare, a project revolving around a smart environment especially built to enable aging in place. The paper describes the vision behind SmartCare as well as its translation into a deployed system. The physical incarnation of SmartCare is the SmartCare apartment, an actual apartment in a retirement community. We provide a description the technologies that are deployed in the...
Heuristic search is considered state-of-the-art for classical planning. However, the performance of search heuristics varies significantly from problem to problem and no single heuristic is superior to all others. As a result, it is highly desirable to identify and utilize the best available heuristic for a particular planning problem. This paper presents a novel approach for planning that monitors...
A major challenge in the area of multiagent reinforcement learning has been addressing the problem of scale, more specifically the fact that increasing the number of agents in a system dramatically increases both the cost of representing the problem and the cost of calculating a solution. In single agent systems, temporal abstractions in the form of options have been used to address part of the scaling...
With the availability of more data, classification is increasingly important. However, traditional classification algorithms do not scale well to large data sets and are often not suited when only limited samples of the dataset are available at any point in time. The latter arises, for example, in streaming data when the accumulation of data a priori is infeasible either due to limitations in memory...
The SmartCare project is to design, develop, and evaluate an intelligent sensor-driven living environment for the elderly. The core objectives are to provide emergency detection, improve quality of life, extend independence for the elderly, and detect patterns of behavior that could suggest early signs of a physical or cognitive issue, all in an unobtrusiveness manner. This paper specifically focuses...
Intelligent agents over their lifetime face multiple tasks that require simultaneous modeling and control of complex, initially unknown environments, observed via incomplete and uncertain observations. In such scenarios, policy learning is subject to the curse of dimensionality, leading to scaling problems for traditional Reinforcement Learning (RL). To address this, the agent has to efficiently acquire...
Sparse coding is a very powerful method to learn high-level features from raw data input. It is able to learn an over complete basis that has the potential to capture robust and discriminative patterns within the data. However, like many other feature learning algorithms, it is unable to detect very similar features or stimuli on different input channels. In this paper, we propose a novel method to...
Sparse coding is a very powerful method to learn high-level features from raw data input. It is able to learn an over complete basis that has the potential to capture robust and discriminative patterns within the data. However, like many other feature learning algorithms, it is unable to detect very similar features or stimuli on different input channels. In this paper, we propose a novel method to...
In this paper, we propose a method to reduce the complexity of solving POMDPs in continuous state spaces by decomposing them into separate, coupled perceptual and decision processes which leads to a reduction of the state space size of the decision learning problem. In our method, we reduce the state space of the POMDP by handling some aspects of the state space outside of the decision POMDP. To achieve...
Communication is an important element of multiagent systems (MAS). In fully decentralized systems it is needed to allow the agents to coordinate their actions to achieve certain goals. When the agents have no means to coordinate their actions, they generally choose actions that minimize their chance of losses. If the agents were allowed to coordinate, on the other hand, they can choose actions that...
Markov Decision Processes (MDPs) and Partially Observable Markov Decision Processes (POMDPs) are very powerful frameworks to model decision and decision learning tasks in a wide range of problem domains. Thus, they are used widely in complex and real-world situations such as robot control tasks. However, this modeling power and generality of the framework comes at a cost in that the complexity of...
One of the challenges for artificial agents is managing the complexity of their environment and task domain as they learn increasingly difficult tasks. This is especially true of agents that are grounded in the physical world, which contains a vast number of features and potentially very complex dynamics. A scalable solution to this problem in terms of forming, managing, and re-using compact, grounded...
The objective of inverse reinforcement learning (IRL) is to learn an agent's reward function based on either the agent's policies or the observations of the policy. In this paper we address the issue of using inverse reinforcement learning to learn the reward function in a multi agent setting, where the agents can either cooperate or be strictly non-cooperative. The case of cooperataing agents is...
Combining expert knowledge and user explanation with automated reasoning in domains with uncertain information poses significant challenges in terms of representation and reasoning mechanisms. In particular, reasoning structures understandable and usable by humans are often different from the ones used for automated reasoning and data mining systems. Rules with certainty factors represent one possible...
Complex control tasks involving varying or evolving system dynamics often pose a great challenge to mainstream reinforcement learning algorithms. Specifically, in most standard methods, actions are often assumed to be a concrete and fixed set that applies to the state space in a predefined manner. Consequently, without resorting to a substantial re-learning procedure, the derived policy lacks the...
A life-long learning agent must have the ability to learn new tasks, adapt the policies of already learned tasks, and extract and reuse knowledge from previous tasks for future use. To do the latter, it needs methods that can autonomously identify, categorize and generalize control and representational knowledge. This paper presents a novel approach to achieve this by combining the policy homomorphism...
The standard reinforcement learning framework often faces challenges in a varying or evolving environment due to an inherent limitation in its representation. In particular, useful actions for decision making are often assumed to be a prefixed set prior to the learning process. Consequently, the derived policy in general lacks the ability to adapt to possible variations in the action outcomes or the...
The human–computer interface remains a mostly visual environment with little or no haptic interaction. While haptics is finding inroads in specialized areas such as surgery, gaming, and robotics, there has been little work to bring haptics to the computer desktop, which is largely dominated today by the GUI/mouse relationship. The mouse as an input device, however, poses many challenges for users...
The behavior and self-organization of ant colonies has been widely studied to address distributed clustering. However, most models that directly mimic ants produce too many clusters and converge too slowly. A wide range of research has attempted to address this through various means, but a number of sources of inefficiency remain, including: i) ants must physically move from one cluster to another...
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