Combining logic and probability in tracking and scene interpretation

The paper gives a high-level overview of some ways in which logical representations and reasoning can be used in computer vision applications, such as tracking and scene interpretation. The combination of logical and statistical approaches is also considered

Situation awareness and ability in coalitions

This paper proposes a discussion on the formal links between the Situation Calculus and the semantics of interpreted systems as far as they relate to Higher-Level Information Fusion tasks. Among these tasks Situation Analysis require to be able to reason about the decision processes of coalitions. Indeed in higher levels of information fusion, one not only need to know that a certain proposition is true (or that it has a certain numerical measure attached), but rather needs to model the circumstances under which this validity holds as well as agents' properties and constraints. In a previous paper the authors have proposed to use the Interpreted System semantics as a potential candidate for the unification of all levels of information fusion. In the present work we show how the proposed framework allow to bind reasoning about courses of action and Situation Awareness. We propose in this paper a (1) model of coalition, (2) a model of ability in the situation calculus language and (3) a model of situation awareness in the interpreted systems semantics. Combining the advantages of both Situation Calculus and the Interpreted Systems semantics, we show how the Situation Calculus can be framed into the Interpreted Systems semantics. We illustrate on the example of RAP compilation in a coalition context, how ability and situation awareness interact and what benefit is gained. Finally, we conclude this study with a discussion on possible future works

Detecting and repairing anomalous evolutions in noisy environments: logic programming formalization and complexity results

In systems where agents are required to interact with a partially known and dynamic world, sensors can be used to obtain further knowledge about the environment. However, sensors may be unreliable, that is, they may deliver wrong information (due, e.g., to hardware or software malfunctioning) and, consequently, they may cause agents to take wrong decisions, which is a scenario that should be avoided. The paper considers the problem of reasoning in noisy environments in a setting where no (either certain or probabilistic) data is available in advance about the reliability of sensors. Therefore, assuming that each agent is equipped with a background theory (in our setting, an extended logic program) encoding its general knowledge about the world, we define a concept of detecting an anomaly perceived in sensor data and the related concept of agent recovering to a coherent status of information. In this context, the complexities of various anomaly detection and anomaly recovery problems are studied.IFIP International Conference on Artificial Intelligence in Theory and Practice - Agents 1Red de Universidades con Carreras en Informática (RedUNCI

LTL Verification of Online Executions with Sensing in Bounded Situation Calculus

Abstract. We look at agents reasoning about actions from a firstperson perspective. The agent has a representation of world as situation calculus action theory. It can perform sensing actions to acquire information. The agent acts “online”, i.e., it performs an action only if it is certain that the action can be executed, and collects sensing results from the actual world. When the agent reasons about its future actions, it indeed considers that it is acting online; however only possible sensing values are available. The kind of reasoning about actions we consider for the agent is verifying a first-order (FO) variant (without quantification across situations) of linear time temporal logic (LTL). We mainly focus on bounded action theories, where the number of facts that are true in any situation is bounded. The main results of this paper are: (i) possible sensing values can be based on consistency if the initial situation description is FO; (ii) for bounded action theories, progression over histories that include sensing results is always FO; (iii) for bounded theories, verifying our FO LTL against online executions with sensing is decidable.

Detecting and repairing anomalous evolutions in noisy environments: logic programming formalization and complexity results

In systems where agents are required to interact with a partially known and dynamic world, sensors can be used to obtain further knowledge about the environment. However, sensors may be unreliable, that is, they may deliver wrong information (due, e.g., to hardware or software malfunctioning) and, consequently, they may cause agents to take wrong decisions, which is a scenario that should be avoided. The paper considers the problem of reasoning in noisy environments in a setting where no (either certain or probabilistic) data is available in advance about the reliability of sensors. Therefore, assuming that each agent is equipped with a background theory (in our setting, an extended logic program) encoding its general knowledge about the world, we define a concept of detecting an anomaly perceived in sensor data and the related concept of agent recovering to a coherent status of information. In this context, the complexities of various anomaly detection and anomaly recovery problems are studied.IFIP International Conference on Artificial Intelligence in Theory and Practice - Agents 1Red de Universidades con Carreras en Informática (RedUNCI