Qualitative techniques for spatial reasoning are important in artificial intelligence. We present an extended cardinal direction calculus (XCDC) for spatio-temporal event representation and reasoning. The methods presented in this paper can be used in systems based on natural language processing which are also discussed in this paper.

Highly capable multiple robot architectures often resort to micromanagement to provide enhanced cooperative abilities, sacrificing individual autonomy. Conversely, multi-robot architectures that maintain individual autonomy are often limited in their cooperative abilities.  This article presents a modified three layer architecture that solves both of these issues.  The addition of a Coordinator layer to a three-layered approach provides a platform-independent interface for coordination on tasks and takes advantage of individual autonomy to improve coordination capabilities.  This reduces communication overhead versus many multi-robot architecture designs and allows for more straightforward resizing of the robot collective and increased individual autonomy.

We propose a new approach for Temporal Diagnosis Problems. This approach is an extension of  Bouzid and Ligeza's  method for temporal diagnosis problems. In this latter work, the authors define a Temporal Causal Graph (TCG) where time delays are expressed as temporal instants. We extend the TCG by including two quantitative relations in order to handle temporal intervals. We call ExTCG this new model. Solving a temporal diagnosis problem represented by the ExTCG consists of finding all possible explanations. It is performed using a backtrack search algorithm. In many diagnosis applications, the generation of all possible explanations is not necessary. For this reason, we augment the ExTCG in order to consider the degree of causality between symptoms. We call weighted ExTCG this extended model. Solving it consists of finding the explanation  having the highest probability to occur. Through a real world diagnosis application in medicine, we illustrate the weighted ExTCG and its corresponding solving algorithm.

The performance of all relational learning techniques has an implicit dependence on the underlying connectivity structure of the relations that are used as input. In this paper, we show how clustering can be used to develop an efficient optimization strategy can be used to effectively measure the structure of a graph in the absence of labeled instances.

While team action recognition has a relatively extended literature, less attention has been given to the detailed realtime analysis of the internal structure of the team actions.  This includes recognizing the current state of the action, predicting the next state, recognizing deviations from the standard action model, and handling ambiguous cases. The underlying probabilistic reasoning model has a major impact on the type of data it can extract, its accuracy, and the computational cost of the reasoning process. In this paper we are using Cascading Hidden Markov Models (CHMM) to analyze Bounding Overwatch, an important team action in military tactics. The team action is represented in the CHMM as a plan tree. Starting from real-world recorded data, we identify the subteams through clustering and extract team oriented discrete features. In an experimental study, we investigate whether the better scalability and the more structured information provided by the CHMM comes with an unacceptable cost in accuracy. We find the a properly parametrized CHMM estimating the current goal chain of the Bounding Overwatch plan tree comes very close to a flat HMM estimating only the overall Bounding Overwatch state (a subset of the goal chain) at a respective overall state accuracy of 95% vs 98%, making the CHMM a good candidate for deployed systems.

When research articles introduce new findings or concepts they typically relate them only to knowledge and domain concepts of immediate relevance. However, many domain concepts relevant for the article and its findings are omitted in the text. This may prevent us from retrieving articles of interest when executing a search query. Approaches such as probabilistic latent semantic indexing (PLSI) overcome this limitation by projecting terms in articles to a lower dimensional latent space and best possible matches in this space are identified. Nevertheless, this approach may not perform well enough if the number of explicit knowledge concepts in the articles is too small compared to the amount of knowledge in the domain. The objective of this paper is to address the problem by exploiting a domain knowledge layer: a rich network of associations among knowledge concepts in the domain of interest. We present a new document retrieval framework that i) extracts associations among knowledge concepts from many documents in the literature corpus; ii) and exploits them to improve the retrieval of relevant documents. We test our approach on the problem of retrieval of biomedical documents and show that it outperforms standard Lucene and BM25 information-retrieval methods.

This paper presents an approach to creating flexible general-logic representations from language for use in high-level reasoning tasks in cognitive modeling.  These representations are grounded in a large-scale ontology and emphasize the need for semantic breadth at the cost of syntactic breadth.  The task-independent interpretation process allows task-specific pragmatics to guide the interpretation process. In the context of a particular cognitive model, we discuss our use of limited abduction for interpretation and show results of its performance.

We propose a memory-based approach to the problem of goal-schema recognition. We use a generic episodic memory module to perform incremental goal schema recognition and to build the plan library. Unlike other case-based plan recognizers it does not require complete knowledge of the planning domain or the ability to record intermediate planning states. Similarity of plans is computed incrementally using a semantic matcher that considers the type and parameters of the observed actions.  We evaluate this approach on two datasets and show that it is able to achieve similar or better performance compared to a statistical approach, but offers important advantages: plan library is acquired incrementally and the memory structure it builds is multi-functional and can be used for other tasks such as plan generation or classification.

The Multiple ANSwer EXtraction system is a framework for interpreting a conjecture with outermost existentially quantified variables as a question, and extracting multiple answers to the question by repetitive calls to a base system that can report the bindings for the variables in one proof of the conjecture. This paper describes the framework and demonstrates its use on an illustrative example.

This paper extends the concept of trust modeling within a multi-agent environment.  Trust modeling often focuses on identifying the appropriate trust level for the other agents in the environment and then using these levels to determine how to interact with each agent.  However, this type of modeling does not account for sneaky agents who are willing to cooperate when the stakes are low and take selfish, greedy actions when the rewards rise.  Adding trust to an interactive partially observable Markov decision process (I-POMDP) allows trust levels to be continuously monitored and corrected enabling agents to make better decisions.  The addition of trust modeling increases the decision process calculations, but solves more complex trust problems that are representative of the human world.  The modified I-POMDP reward function and belief models can be used to accurately track the trust levels of agents with hidden agendas.  Testing demonstrates that agents quickly identify the hidden trust levels to mitigate the impact of a deceitful agent.