1,403 research outputs found
Towards Persistent Storage and Retrieval of Domain Models using Graph Database Technology
We employ graph database technology to persistently store and retrieve robot
domain models.Comment: Presented at DSLRob 2015 (arXiv:1601.00877
Towards Declarative Safety Rules for Perception Specification Architectures
Agriculture has a high number of fatalities compared to other blue collar
fields, additionally population decreasing in rural areas is resulting in
decreased work force. These issues have resulted in increased focus on
improving efficiency of and introducing autonomy in agriculture. Field robots
are an increasingly promising branch of robotics targeted at full automation in
agriculture. The safety aspect however is rely addressed in connection with
safety standards, which limits the real-world applicability. In this paper we
present an analysis of a vision pipeline in connection with functional-safety
standards, in order to propose solutions for how to ascertain that the system
operates as required. Based on the analysis we demonstrate a simple mechanism
for verifying that a vision pipeline is functioning correctly, thus improving
the safety in the overall system.Comment: Presented at DSLRob 2015 (arXiv:1601.00877
Consciosusness in Cognitive Architectures. A Principled Analysis of RCS, Soar and ACT-R
This report analyses the aplicability of the principles of consciousness developed in the ASys project to three of the most relevant cognitive architectures. This is done in relation to their aplicability to build integrated control systems and studying their support for general mechanisms of real-time consciousness.\ud
To analyse these architectures the ASys Framework is employed. This is a conceptual framework based on an extension for cognitive autonomous systems of the General Systems Theory (GST).\ud
A general qualitative evaluation criteria for cognitive architectures is established based upon: a) requirements for a cognitive architecture, b) the theoretical framework based on the GST and c) core design principles for integrated cognitive conscious control systems
Motivations, Values and Emotions: 3 sides of the same coin
This position paper speaks to the interrelationships between the three concepts of motivations, values, and emotion. Motivations prime actions, values serve to choose between motivations, emotions provide a common currency for values, and emotions implement motivations. While conceptually distinct, the three are so pragmatically intertwined as to differ primarily from our taking different points of view. To make these points more transparent, we briefly describe the three in the context a cognitive architecture, the LIDA model, for software agents and robots that models human cognition, including a developmental period. We also compare the LIDA model with other models of cognition, some involving learning and emotions. Finally, we conclude that artificial emotions will prove most valuable as implementers of motivations in situations requiring learning and development
Programmable Agents
We build deep RL agents that execute declarative programs expressed in formal
language. The agents learn to ground the terms in this language in their
environment, and can generalize their behavior at test time to execute new
programs that refer to objects that were not referenced during training. The
agents develop disentangled interpretable representations that allow them to
generalize to a wide variety of zero-shot semantic tasks
A Survey of Knowledge-based Sequential Decision Making under Uncertainty
Reasoning with declarative knowledge (RDK) and sequential decision-making
(SDM) are two key research areas in artificial intelligence. RDK methods reason
with declarative domain knowledge, including commonsense knowledge, that is
either provided a priori or acquired over time, while SDM methods
(probabilistic planning and reinforcement learning) seek to compute action
policies that maximize the expected cumulative utility over a time horizon;
both classes of methods reason in the presence of uncertainty. Despite the rich
literature in these two areas, researchers have not fully explored their
complementary strengths. In this paper, we survey algorithms that leverage RDK
methods while making sequential decisions under uncertainty. We discuss
significant developments, open problems, and directions for future work
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