Narrative based Postdictive Reasoning for Cognitive Robotics

Making sense of incomplete and conflicting narrative knowledge in the presence of abnormalities, unobservable processes, and other real world considerations is a challenge and crucial requirement for cognitive robotics systems. An added challenge, even when suitably specialised action languages and reasoning systems exist, is practical integration and application within large-scale robot control frameworks. In the backdrop of an autonomous wheelchair robot control task, we report on application-driven work to realise postdiction triggered abnormality detection and re-planning for real-time robot control: (a) Narrative-based knowledge about the environment is obtained via a larger smart environment framework; and (b) abnormalities are postdicted from stable-models of an answer-set program corresponding to the robot's epistemic model. The overall reasoning is performed in the context of an approximate epistemic action theory based planner implemented via a translation to answer-set programming.Comment: Commonsense Reasoning Symposium, Ayia Napa, Cyprus, 201

Exploiting Deep Semantics and Compositionality of Natural Language for Human-Robot-Interaction

We develop a natural language interface for human robot interaction that implements reasoning about deep semantics in natural language. To realize the required deep analysis, we employ methods from cognitive linguistics, namely the modular and compositional framework of Embodied Construction Grammar (ECG) [Feldman, 2009]. Using ECG, robots are able to solve fine-grained reference resolution problems and other issues related to deep semantics and compositionality of natural language. This also includes verbal interaction with humans to clarify commands and queries that are too ambiguous to be executed safely. We implement our NLU framework as a ROS package and present proof-of-concept scenarios with different robots, as well as a survey on the state of the art

Grounding Dynamic Spatial Relations for Embodied (Robot) Interaction

This paper presents a computational model of the processing of dynamic spatial relations occurring in an embodied robotic interaction setup. A complete system is introduced that allows autonomous robots to produce and interpret dynamic spatial phrases (in English) given an environment of moving objects. The model unites two separate research strands: computational cognitive semantics and on commonsense spatial representation and reasoning. The model for the first time demonstrates an integration of these different strands.Comment: in: Pham, D.-N. and Park, S.-B., editors, PRICAI 2014: Trends in Artificial Intelligence, volume 8862 of Lecture Notes in Computer Science, pages 958-971. Springe

Postdictive Reasoning in Epistemic Action Theory

If an agent executes an action, this will not only change the world physically, but also the agent's knowledge about the world. Therefore the occurrence of an action can be modeled as an epistemic state transition which maps the knowledge state of an agent to a successor knowledge state. For example, consider that an agent in a state s_0 executes an action a. This causes a transition to a state s_1. Subsequently, the agent executes a sensing action a_s, which produces knowledge and causes a transition to a state s_2. With the information which is gained by the sensation, the agent can not only extend its knowledge about s_2, but also infer additional knowledge about the initial state s_0. That is, the agent uses knowledge about the present to retrospectively acquire additional information about the past. We refer to this temporal form of epistemic inference as postdiction. Existing action theories are not capable of efficiently performing postdictive reasoning because they require an exponential number of state variables to represent an agent's knowledge state. The contribution of this thesis is an approximate epistemic action theory which is capable of postdictive reasoning while it requires only a linear number of state variables to represent an agent's knowledge state. In addition, the theory is able to perform a more general temporal form of postdiction, which most existing approaches do not support. We call the theory the h-approximation (HPX) because it explicitly represents historical knowledge about past world states. In addition to the operational semantics of HPX, we present its formalization in terms of Answer Set Programming (ASP) and provide respective soundness results. The ASP implementation allows us to apply HPX in real robotic applications by using off-the-shelf ASP solvers. Specifically, we integrate of HPX in an online planning framework for Cognitive Robotics where planning, plan execution and abductive explanation tasks are interleaved. As a proof-of-concept, we provide a case-study which demonstrates the application of HPX for high-level robot control in a Smart Home. The case-study emphasizes the usefulness of postdiction for abnormality detection in robotics: actions which are performed by robots are often not successful due to unforeseen practical problems. A solution is to verify action success by observing the effects of the action. If the desired effects do not hold after action execution, then one can postdict the existence of an abnormality

Developing an empirically based agent-based model to support local transitions

Sustainable technologies (e.g., hydrogen) have great potential to contribute to achieving sustainability goals. Nevertheless, sustainable technologies are often not readily adopted. Many transitions are currently being explored with agent based models, allowing stakeholders to explore different scenarios to advance local transformations. However, in most cases, agent-based models, mainly built by engineers, still assume the rational actor. Actual decision-making behaviour is, however, hardly rational, lowering model reliability. A theory-based framework describing technology adoption behaviour is needed to represent local systems and actor behaviour in agent-based models accurately. Integrating psychological factors regarding adopting sustainable technologies in agent-based models helps address the complexity of the interrelated technical and social phenomena and the heterogeneous social actors. In the presented research, we, based on the results of a quantitative literature review and an initial study, build a theoretical framework that includes influential psychological factors of technology adoption and distinguishes between individuals, households and organisations. With this distinction, we explore whether similar or different factors are relevant for the various stakeholders. Through our research, we seek to advance the application of behavioural insights in energy system modelling and provide a better understanding of agent-based model potentials, which allow the exploration of outcomes of different scenarios and thereby contribute to successful decision making and intervention design. We discuss implications for transition research and reflect on hurdles and solutions regarding the integration of psychological insights into an agent-based model

Persistent organic pollutant burden, experimental POP exposure and tissue properties affect metabolic profiles of blubber from grey seal pups

Persistent organic pollutants (POPs) are toxic, ubiquitous, resist breakdown, bioaccumulate in living tissue and biomagnify in food webs. POPs can also alter energy balance in humans and wildlife. Marine mammals experience high POP concentrations, but consequences for their tissue metabolic characteristics are unknown. We used blubber explants from wild, grey seal (Halichoerus grypus) pups to examine impacts of intrinsic tissue POP burden and acute experimental POP exposure on adipose metabolic characteristics. Glucose use, lactate production and lipolytic rate differed between matched inner and outer blubber explants from the same individuals and between feeding and natural fasting. Glucose use decreased with blubber dioxin-like PCBs (DL-PCB) and increased with acute experimental POP exposure. Lactate production increased with DL-PCBs during feeding, but decreased with DL-PCBs during fasting. Lipolytic rate increased with blubber dichlorodiphenyltrichloroethane (DDT) and its metabolites (DDX) in fasting animals, but declined with DDX when animals were feeding. Our data show that POP burdens are high enough in seal pups to alter adipose function early in life, when fat deposition and mobilisation are vital. Such POP-induced alterations to adipose glucose use may significantly alter energy balance regulation in marine top predators with the potential for long term impacts on fitness and survival

h-approximation: History-Based Approximation of Possible World Semantics as ASP

We propose an approximation of the Possible Worlds Semantics (PWS) for action planning. A corresponding planning system is implemented by a transformation of the action specification to an Answer-Set Program. A novelty is support for postdiction wrt. (a) the plan existence problem in our framework can be solved in NP, as compared to $\Sigma_2^P$ for non-approximated PWS of Baral(2000); and (b) the planner generates optimal plans wrt. a minimal number of actions in $\Delta_2^P$. We demo the planning system with standard problems, and illustrate its integration in a larger software framework for robot control in a smart home.Comment: 12th International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR 2013