The Qualification Problem: A solution to the problem of anomalous models

AbstractIntelligent agents in open environments inevitably face the Qualification Problem: The executability of an action can never be predicted with absolute certainty; unexpected circumstances, albeit unlikely, may at any time prevent the successful performance of an action. Reasoning agents in real-world environments rely on a solution to the Qualification Problem in order to make useful predictions but also to explain and recover from unexpected action failures. Yet the main theoretical result known today in this context is a negative one: While a solution to the Qualification Problem requires to assume away by default abnormal qualifications of actions, straightforward minimization of abnormality falls prey to the production of anomalous models. We present an approach to the Qualification Problem which resolves this anomaly. Anomalous models are shown to arise from ignoring causality, and they are avoided by appealing to just this concept. Our theory builds on the established predicate logic formalism of the Fluent Calculus as a solution to the Frame Problem and to the Ramification Problem in reasoning about actions. The monotonic Fluent Calculus is enhanced by a default theory in order to obtain the nonmonotonic approach called for by the Qualification Problem. The approach has been implemented in an action programming language based on the Fluent Calculus and successfully applied to the high-level control of robots

About the original frame problem and some other related problems

El problema de marco es uno de los problemas más controvertidos y difíciles de resolver dentro del campo de la filosofía de la mente, y aun hoy no se ha logrado consensuar su definición ni su solución. Es por esta razón, que nuestro principal objetivo es esclarecer el problema de marco original, y algunos otros problemas relacionados. Sostendremos que incluso esta formulación del problema, de entre varias, posee muchos aspectos y que no puede entenderse como “un” solo problema sino más bien como un conjunto de problemas estrechamente relacionados con la misma cuestión.The frame problem is one of the most controversial and difficult problems to solve within the field of philosophy of the mind, and even today it has not been possible to reach a consensus on its definition or its solution. It is for this reason that our main objective is to clarify the original frame problem, and some other related problems. We will argue that even this formulation of the problem, among several, has many aspects and that it cannot be understood as “one” problem only but rather as a set of problems closely related to the same issue.Fil: Silenzi, María Inés. Universidad Nacional del Sur. Departamento de Humanidades; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones Económicas y Sociales del Sur. Universidad Nacional del Sur. Departamento de Economía. Instituto de Investigaciones Económicas y Sociales del Sur; Argentin

Normative thinking on wastewater treatment plants

This document is the report of the thesis "Normative thinking on wastewater treatment plants". This thesis was born from the interest of the author in Artificial Intelligence (A.I.). Having done all the subjects related with AJ. that the Barcelona School of Informatics (FIB) offers, I asked the teachers of my favorite ones for a thesis related with the A.I. . Ulises Cortés and Juan Carlos Nieves offered me this interesting thesis based on a doctoral thesis of environmental sciences done by Montse Aulinas [23]. The proposed work implied theoretical research, a working implementation and a real life domain to work with. I accepted without any doubt. Aulinas's thesis proposed a multi-agent based system to manage the problems caused by the industrial wastewater discharges in rivers. She discussed that, by the use of intelligent agents in the managing process of wastewaters, there could be an important increase in the quality of the river water and in the efficiency from the organizational point of view. To do that she proposed a group of agents, which would take the roles of the most important entities in the process of wastewater discharges, from industries to the agencies in charge of controlling them, in order to represent all the involved parts. It is obvious that, for the agents to be able to work rationally, they need to interact with the laws they are subject too That is the main issue this thesis deals with. Based on a real world doma in, this thesis proposes a way to make those laws to be comprehensible for agents. It will discuss a methodology for analyzing, specifying, implementing and testing those laws, in a generic way that can be applied to any normative environment. The goals of this thesis are, To obtain a generic and complete specification syntax for analyzing laws and norms, prove that specification with an implementation of reallaws applied to the given doma in and To develop a prototype where the norms implementation can be tested using a possible real scenario

Multi-robot systems in cognitive factories: representation, reasoning, execution and monitoring

We propose the use of causality-based formal representation and automated reasoning methods from artificial intelligence to endow multiple teams of robots in a factory, with high-level cognitive capabilities, such as optimal planning and diagnostic reasoning. We present a framework that features bilateral interaction between task and motion planning, and embeds geometric reasoning in causal reasoning. We embed this planning framework inside an execution and monitoring framework and show its applicability on multi-robot systems. In particular, we focus on two domains that are relevant to cognitive factories: i) a manipulation domain with multiple robots working concurrently / co-operatively to achieve a common goal and ii) a factory domain with multiple teams of robots utilizing shared resources. In the manipulation domain two pantograph robots perform a complex task that requires true concurrency. The monitoring framework checks plan execution for two sorts of failures: collisions with unknown obstacles and change of the world due to human interventions. Depending on the cause of the failures, recovery is done by calling the motion planner (to find a different trajectory) or the causal reasoner (to find a new task plan). Therefore, recovery relies on not only motion planning but also causal reasoning. We extend our planning and monitoring framework for the factory domain with multiple teams of robots by introducing algorithms for finding optimal decoupled plans and diagnosing the cause of a failure/discrepancy (e.g., robots may get broken or tasks may get reassigned to teams). We show the applicability of these algorithms on an intelligent factory scenario through dynamic simulations and physical experiments

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

Intention as Commitment toward Time

In this paper we address the interplay among intention, time, and belief in dynamic environments. The first contribution is a logic for reasoning about intention, time and belief, in which assumptions of intentions are represented by preconditions of intended actions. Intentions and beliefs are coherent as long as these assumptions are not violated, i.e. as long as intended actions can be performed such that their preconditions hold as well. The second contribution is the formalization of what-if scenarios: what happens with intentions and beliefs if a new (possibly conflicting) intention is adopted, or a new fact is learned? An agent is committed to its intended actions as long as its belief-intention database is coherent. We conceptualize intention as commitment toward time and we develop AGM-based postulates for the iterated revision of belief-intention databases, and we prove a Katsuno-Mendelzon-style representation theorem.Comment: 83 pages, 4 figures, Artificial Intelligence journal pre-prin

Normative thinking on wastewater treatment plants

This document is the report of the thesis "Normative thinking on wastewater treatment plants". This thesis was born from the interest of the author in Artificial Intelligence (A.I.). Having done all the subjects related with AJ. that the Barcelona School of Informatics (FIB) offers, I asked the teachers of my favorite ones for a thesis related with the A.I. . Ulises Cortés and Juan Carlos Nieves offered me this interesting thesis based on a doctoral thesis of environmental sciences done by Montse Aulinas [23]. The proposed work implied theoretical research, a working implementation and a real life domain to work with. I accepted without any doubt. Aulinas's thesis proposed a multi-agent based system to manage the problems caused by the industrial wastewater discharges in rivers. She discussed that, by the use of intelligent agents in the managing process of wastewaters, there could be an important increase in the quality of the river water and in the efficiency from the organizational point of view. To do that she proposed a group of agents, which would take the roles of the most important entities in the process of wastewater discharges, from industries to the agencies in charge of controlling them, in order to represent all the involved parts. It is obvious that, for the agents to be able to work rationally, they need to interact with the laws they are subject too That is the main issue this thesis deals with. Based on a real world doma in, this thesis proposes a way to make those laws to be comprehensible for agents. It will discuss a methodology for analyzing, specifying, implementing and testing those laws, in a generic way that can be applied to any normative environment. The goals of this thesis are, To obtain a generic and complete specification syntax for analyzing laws and norms, prove that specification with an implementation of reallaws applied to the given doma in and To develop a prototype where the norms implementation can be tested using a possible real scenario