Baral, Chitta

Incremental and Iterative Learning of Answer Set Programs from Mutually Distinct Examples Artificial Intelligence

Over these years the Artificial Intelligence (AI) community has produced several datasets which have given the machine learning algorithms the opportunity to learn various skills across various domains. However, a subclass of these machine learning algorithms that aimed at learning logic programs, namely the Inductive Logic Programming algorithms, have often failed at the task due to the vastness of these datasets. This has impacted the usability of knowledge representation and reasoning techniques in the development of AI systems. In this research, we try to address this scalability issue for the algorithms that learn Answer Set Programs. We present a sound and complete algorithm which takes the input in a slightly different manner and perform an efficient and more user controlled search for a solution. We show via experiments that our algorithm can learn from two popular datasets from machine learning community, namely bAbl (a question answering dataset) and MNIST (a dataset for handwritten digit recognition), which to the best of our knowledge was not previously possible. The system is publicly available at

Addressing a Question Answering Challenge by Combining Statistical Methods with Inductive Rule Learning and Reasoning

AAAI Conferences

A group of researchers from Facebook has recently proposed a set of 20 question-answering tasks (Facebook's bAbl dataset) as a challenge for the natural language understanding ability of an intelligent agent. These tasks are designed to measure various skills of an agent, such as: fact based question-answering, simple induction, the ability to find paths, co-reference resolution and many more. Their goal is to aid in the development of systems that can learn to solve such tasks and to allow a proper evaluation of such systems. They show existing systems cannot fully solve many of those toy tasks. In this work, we present a system that excels at all the tasks except one. The proposed model of the agent uses the Answer Set Programming (ASP) language as the primary knowledge representation and reasoning language along with the standard statistical Natural Language Processing (NLP) models. Given a training dataset containing a set of narrations, questions and their answers, the agent jointly uses a translation system, an Inductive Logic Programming algorithm and Statistical NLP methods to learn the knowledge needed to answer similar questions. Our results demonstrate that the introduction of a reasoning module significantly improves the performance of an intelligent agent.

Automatic Extraction of Events-Based Conditional Commonsense Knowledge

AAAI Conferences

Reasoning with commonsense knowledge plays an important role in various NLU tasks. Often the commonsense knowledge is needed to be extracted separately. In this paper we present our work of automatically extracting a certain type of commonsense knowledge. The knowledge resembles the kind that humans have about the events and the entities that participate in those events. One example of such knowledge is that "IF A bullying B causes T rescued Z THEN (possibly) Z = B ''. We call this knowledge an event-based conditional commonsense. Our approach involves semantic parsing of natural language sentences by using the Knowledge Parser (K-Parser) and extracting the knowledge, if found. We extracted about 19000 instances of such knowledge from the Open American National Corpus.

Towards Addressing the Winograd Schema Challenge — Building and Using a Semantic Parser and a Knowledge Hunting Module

AAAI Conferences

Concerned about the Turing test's ability to correctly evaluate if a system exhibits human-like intelligence, the Winograd Schema Challenge (WSC) has been proposed as an alternative. A Winograd Schema consists of a sentence and a question. The answers to the questions are intuitive for humans but are designed to be difficult for machines, as they require various forms of commonsense knowledge about the sentence. In this paper we demonstrate our progress towards addressing the WSC. We present an approach that identifies the knowledge needed to answer a challenge question, hunts down that knowledge from text repositories, and then reasons with them to come up with the answer. In the process we develop a semantic parser ( We show that our approach works well with respect to a subset of Winograd schemas.

Multi-Agent Action Modeling Through Action Sequences And Perspective Fluents

AAAI Conferences

Actions in a multi-agent setting have complex characteristics. They may not only affect the real world, but also affect the knowledge and beliefs of agents in the world. In many cases, the effect on the beliefs or knowledge of an agent is not due to that agent actively doing some actions, but could be simply the result of that agent’s perspective in terms of where it is looking. In dynamic epistemic logic (DEL), such multi-agent actions are expressed as complex constructs or as Kripke model type structures. This paper uses the multi-agent action language mA+ to show how one can take advantage of some of the perspective fluents of the world to model com- plex actions, in the sense of DEL, as simple action sequences. The paper describes several plan modules using such actions. Such plan modules will be helpful in planning for belief and knowledge goals in a multi-agent setting, as planning from scratch would often be prohibitively time consuming.

An Approach to Solve Winograd Schema Challenge Using Automatically Extracted Commonsense Knowledge

AAAI Conferences

The Winograd Schema Challenge has recently been proposed as an alternative to the Turing test. A Winograd Schema consists of a sentence and question pair such that the answer to the question depends on the resolution of a definite pronoun in the sentence. The answer is fairly intuitive for humans but is difficult for machines because it requires commonsense knowledge about words or concepts in the sentence. In this paper we propose a novel technique which semantically parses the text, hunts for the needed commonsense knowledge and uses that knowledge to answer the given question.

Visual Commonsense for Scene Understanding Using Perception, Semantic Parsing and Reasoning

AAAI Conferences

In this paper we explore the use of visual common-sense knowledge and other kinds of knowledge (such as domain knowledge, background knowledge, linguistic knowledge) for scene understanding. In particular, we combine visual processing with techniques from natural language understanding (especially semantic parsing), common-sense reasoning and knowledge representation and reasoning to improve visual perception to reason about finer aspects of activities.

Exploring the KD45 Property of a Kripke Model After the Execution of an Action Sequence

AAAI Conferences

The paper proposes a condition for preserving the KD45 property of a Kripke model when a sequence of update models is applied to it. The paper defines the notions of a primitive update model and a semi-reflexive KD45 (or sr-KD45) Kripke model. It proves that updating a sr-KD45 Kripke model using a primitive update model results in a sr-KD45 Kripke model, i.e., a primitive update model preserves the properties of a sr-KD45 Kripke model. It shows that several update models for modeling well-known actions found in the literature are primitive. This result provides guarantees that can be useful in presence of multiple applications of actions in multi-agent system (e.g., multi-agent planning).

Pathway Specification and Comparative Queries: A High Level Language with Petri Net Semantics

AAAI Conferences

Understanding biological pathways is an important activity in the biological domain for drug development. Due to the parallelism and complexity inherent in pathways, computer models that can answer queries about pathways are needed. A researcher may ask `what-if' questions comparing alternate scenarios, that require deeper understanding of the underlying model. In this paper, we present overview of such a system we developed and an English-like high level language to express pathways and queries. Our language is inspired by high level action and query languages and it uses Petri Net execution semantics.